Quantum advantages for syndrome-aware noisy logical observable estimation Authors Kento Tsubouchi, Hyukgun Kwon, Liang Jiang, Nobuyuki Yoshioka Published: 03.05.2026 Updated: 03.05.2026 Summary Recent progress in fault-tolerant quantum computing suggests that leveraging error-syndrome information at the logical layer can substantially improve performance, including the estimation of logical observables from noisy states. In this work, based on quantum estimation theory, we develop an information-theoretic framework to quantify the utility of error syndromes for noisy logical observable estimation. We distinguish two operational regimes of such syndrome-aware protocols: classical protocols, in which the logical measurement basis is fixed and syndrome information is used only in classical post-processing, and quantum protocols, in which the logical quantum control can be tailored to depend on the observed error syndrome. For classical syndrome-aware protocols, we prove a universal limitation: on average, syndrome information can improve the effective logical error rate by at most a factor of two, implying at most a quadratic reduction in sampling overhead. In contrast, once syndrome-conditioned quantum control is permitted, we exhibit settings in which the effective logical error rate decays exponentially with the number of logical qubits. These findings provide fundamental guidance for designing future fault-tolerant architectures that actively exploit syndrome records rather than discarding them after decoding. Source arXiv: 2603.05145v1
Variational Quantum Transduction Authors Pengcheng Liao, Haowei Shi, Quntao Zhuang Published: 03.04.2026 Updated: 03.04.2026 Summary Quantum transducers are critical for quantum interconnect, enabling coherent signal transfer across disparate frequency domains. Beyond material and device advances, protocol design has become a powerful means to improve transduction. We introduce a variational quantum transduction (VQT) framework that employs variational tools from near-term quantum computing to systematically optimize protocol performance. As a variational quantum circuit framework, VQT is not plagued by known training issues such as barren plateau, because a small-scale problem is sufficient for substantial advantage and training only needs to be done once to configure a VQT system. Maximizing the quantum information rate within this framework yields protocols that surpass all known schemes in their respective classes. For non-adaptive protocols, VQT exceeds the performance envelopes of Gottesman-Kitaev-Preskill (GKP)-based and entanglement-assisted approaches. In the adaptive setting, VQT provides only a marginal improvement over Gaussian feedforward strategies, indicating that Gaussian adaptive transduction is already close to optimal. With increasingly universal quantum control, VQT provides a systematic path toward optimal quantum transduction. Source arXiv: 2603.03642v1
Rate-Fidelity Tradeoffs in All-Photonic and Memory-Equipped Quantum Switches Authors Panagiotis Promponas, Leonardo Bacciottini, Paul Polakos, Gayane Vardoyan, Don Towsley, Leandros Tassiulas Published: 03.03.2026 Updated: 03.03.2026 Summary Quantum entanglement switches are a key building block for early quantum networks, and a central design question is whether near-term devices should use only flying photons or also incorporate quantum memories. We compare two architectures: an all-photonic entanglement generation switch (EGS) that repeatedly attempts Bell-state measurements (BSM) without storing qubits, and a quantum memory-equipped switch that buffers entanglement and triggers measurements only when heralded connectivity is available (herald-then-swap control). These two designs trade off simple, memoryless operation that avoids decoherence and memory-induced latency against heralding-based control that buffers entanglement to use BSMs more efficiently. We formalize both models under a common hardware abstraction and characterize their achievable rate-fidelity regions, yielding a benchmarking methodology that translates hardware and protocol parameters into network-level performance. Numerical evaluation quantifies the rate-fidelity tradeoffs of both models, identifies operating regions in which each architecture dominates, and shows how hardware and protocol knobs can be tuned to meet application-specific targets. Source arXiv: 2603.02610v1
Ultra-low loss piezo-optomechanical low-confinement silicon nitride platform for visible wavelength quantum photonic circuits Authors Mayank Mishra, Gwangho Choi, Wenhua He, Gina M. Talcott, Katherine Kearney, Michael Gehl, Andrew Leenheer, Daniel Dominguez, Nils T. Otterstrom, Matt Eichenfield Published: 03.03.2026 Updated: 03.04.2026 Summary The stringent demands of photonic quantum computing protocols motivate photonic integrated circuit (PIC) platforms with passive optical properties such as extremely low losses and correspondingly large circuit depths, as well as active optical properties such as high reconfiguration rates, low power dissipation, and minimal crosstalk. At the same time, many quantum photonic resource state generators, such as single-photon sources and quantum memories, require operation in the visible wavelength range. These requirements make the passive optical properties of CMOS-fabricated, ultralow-loss, low-confinement silicon nitride waveguides especially attractive. However, the conventional active properties of these systems based on thermo-optic modulation are plagued by high levels of crosstalk, slow modulation rates, and high power dissipation. Although there have been recent demonstrations of CMOS-fabricated, visible wavelength, piezo-optomechanical PICs that solve the above challenges associated with implementing active functionality, these have made use of high-confinement waveguides with currently demonstrated losses of order $0.3$-$1~mathrm{dB/cm}$, precluding circuit depths required for scalable quantum algorithms. Here, we demonstrate that combining piezo-optomechanical actuation with a low-confinement, ultra-low loss silicon nitride platform addresses the scalability challenge while enabling high-performance active functionality at visible wavelengths. This platform achieves a propagation loss $0.026~mathrm{dB/cm}$ at $780~mathrm{nm}$, modulation bandwidths in the MHz range, and a phase shifter voltage-length product ($V_πL$) of approximately $2.8~mathrm{mathrm{V}cdotmathrm{m}}$ and negligible hysteresis. We further demonstrate reconfigurable Mach-Zehnder interferometers based on spiral phase shifters with 0.63 dB loss per phase shifter. Source arXiv: 2603.02584v2
Optimizing Orbital Parameters of Satellites for a Global Quantum Network Authors Athul Ashok, Owen DePoint, Jackson MacDonald, Albert Williams, Don Towsley Published: 03.03.2026 Updated: 03.03.2026 Summary Due to fundamental limitations on terrestrial quantum links, satellites have received considerable attention for their potential as entanglement generation sources in a global quantum internet. In this work, we focus on the problem of designing a constellation of satellites for such a quantum network. We find satellite inclination angles and satellite cluster allocations to achieve maximal entanglement generation rates to fixed sets of globally distributed ground stations. Exploring two black-box optimization frameworks: a Bayesian Optimization (BO) approach and a Genetic Algorithm (GA) approach, we find comparable results, indicating their effectiveness for this optimization task. While GA and BO often perform remarkably similar, BO often converges more efficiently, while later growth noted in GAs is indicative of less susceptibility towards local maxima. In either case, they offer substantial improvements over naive approaches that maximize coverage with respect to ground station placement. Source arXiv: 2603.02480v1
Quantum squeezing in an all-resonant periodically poled lithium niobate microresonator Authors Xinyi Ren, Reshma Kopparapu, Tushar Sanjay Karnik, Chun-Ho Lee, Kiwon Kwon, Clayton Cheung, Yue Yu, Shi-Yuan Ma, Bo-Han Wu, Ran Yin, Lian Zhou, Quntao Zhuang, Dirk Englund, Zaijun Chen, Mengjie Yu Published: 02.26.2026 Updated: 02.26.2026 Summary Quantum noise limits the sensitivity of optical measurements, but squeezed states of light enable quantum-enhanced metrology, sensing, and information processing. Most on-chip squeezed-light sources rely on Kerr ($χ^{(3)}$) nonlinearities, remain limited by pump power and excess loss constraints. Quadratic ($χ^{(2)}$) platforms instead provide stronger parametric interactions, lower pump power requirements, and greater spectral engineering flexibility. Here, we demonstrate strong, broadband squeezed-light generation on a thin-film lithium niobate (TFLN) photonic chip using a dual-resonant optical parametric amplifier implemented in a single periodically poled LN (PPLN) microresonator. Near-full-depth domain inversion is achieved simultaneously with highly over-coupled resonances, exhibiting escape efficiencies exceeding 90% and intrinsic quality factors above 2.5 million in a 0.6 mm$^2$ X-cut TF-PPLN resonator, enabling efficient squeezing at 1587 nm when pumped at 793.5 nm. Operating in the continuous-wave regime, we directly measure -0.81 dB of squeezing below the shot-noise limit with a pump power of 27 mW, together with +4.29 dB of anti-squeezing. From these measurements, we infer an on-chip squeezing level of -7.52 dB $pm$ 0.22 dB (95% confidence interval: [-7.96,-7.10] dB), and an on-chip anti-squeezing level of +9.62 dB $pm$ 0.25 dB. We demonstrate single-mode squeezing at degeneracy with a squeezed-light spectrum exceeding 10.3 THz. This work reports the highest squeezing ratio among integrated $χ^{(2)}$ cavity platforms and the first quasi-phase matched, fully resonant $χ^{(2)}$ cavity squeezer on chip, establishing a scalable route to fully integrated power-efficient squeezed-light sources for quantum-enhanced sensing and metrology. Source arXiv: 2602.22693v1
Passive Environment-Assisted Quantum Communication Authors Evelyn Voss, Bikun Li, Zhaoyou Wang, Liang Jiang Published: 02.25.2026 Updated: 02.25.2026 Summary As quantum information systems mature, efficient and coherent transfer of quantum information through noisy channels becomes increasingly important. We examine how passive environment-assisted quantum communication enhances direct quantum information transfer efficiency. A bosonic pure-loss channel, modeled as transmission through a beam splitter with a vacuum input state at the dark port, has zero quantum capacity when transmissivity is below 50%. Quantum communication through the channel can be enhanced by passive environment assistance, achieved via the selection of an appropriate input state for the ancilla port. Although ideal Gottesman-Kitaev-Preskill (GKP) states enable perfect quantum information transmission at arbitrarily small transmissivity, they are challenging to realize experimentally. We therefore explore more experimentally accessible non-Gaussian ancilla states, such as Fock, cat, and squeezed cat states, and numerically determine the optimal encoding and decoding strategies. We also construct analytical schemes that yield high-fidelity transmission and good information rates. Source arXiv: 2602.21549v1
Passive Environment-Assisted Quantum Communication Authors Evelyn Voss, Bikun Li, Zhaoyou Wang, Liang Jiang Published: 02.25.2026 Updated: 02.27.2026 Summary As quantum information systems mature, efficient and coherent transfer of quantum information through noisy channels becomes increasingly important. We examine how passive environment-assisted quantum communication enhances direct quantum information transfer efficiency. A bosonic pure-loss channel, modeled as transmission through a beam splitter with a vacuum input state at the dark port, has zero quantum capacity when transmissivity is below 50%. Quantum communication through the channel can be enhanced by passive environment assistance, achieved via the selection of an appropriate input state for the ancilla port. Although ideal Gottesman-Kitaev-Preskill (GKP) states enable perfect quantum information transmission at arbitrarily small transmissivity, they are challenging to realize experimentally. We therefore explore more experimentally accessible non-Gaussian ancilla states, such as Fock, cat, and squeezed cat states, and numerically determine the optimal encoding and decoding strategies. We also construct analytical schemes that yield high-fidelity transmission and good information rates. Source arXiv: 2602.21549v2
Universal Sample Complexity Bounds in Quantum Learning Theory via Fisher Information matrix Authors Hyukgun Kwon, Seok Hyung Lie, Liang Jiang Published: 02.25.2026 Updated: 02.25.2026 Summary In this work, we show that the sample complexity (equivalently, the number of measurements) required in quantum learning theory within a general parametric framework, is fundamentally governed by the inverse Fisher information matrix. More specifically, we derive upper and lower bounds on the number of samples required to estimate the parameters of a quantum system within a prescribed small additive error and with high success probability under maximum likelihood estimation. The upper bound is governed by the supremum of the largest diagonal entry of the inverse Fisher information matrix, while the lower bound is characterized by any diagonal element evaluated at arbitrary parameter values. We then apply the general bounds to Pauli channel learning and to the estimation of Pauli expectation values in the asymptotic small-error regime, and recover the previously established sample complexity through considerably streamlined derivations. Furthermore, we identify the structural origin of exponential sample complexity in Pauli channel learning without entanglement and in Pauli expectation value estimation without quantum memory. We then extend the analysis to an error criterion based on the Euclidean distance between the true parameter values and their estimators. We derive the corresponding upper and lower bounds on the sample complexity, which are likewise characterized by the inverse Fisher information matrix. As an application, we consider Pauli expectation estimation with entangled probes. Finally, we highlight two fundamental contributions to quantum learning theory. First, we establish a systematic framework that determines the task-independent sample complexity under maximum-likelihood estimation. Second, we show that, in the small-error regime, learning sample complexity is governed by the inverse Fisher information matrix. Source arXiv: 2602.21510v1
Agentic AI for Scalable and Robust Optical Systems Control Authors Zehao Wang, Mingzhe Han, Wei Cheng, Yue-Kai Huang, Philip Ji, Denton Wu, Mahdi Safari, Flemming Holtorf, Kenaish AlQubaisi, Norbert M. Linke, Danyang Zhuo, Yiran Chen, Ting Wang, Dirk Englund, Tingjun Chen Published: 02.23.2026 Updated: 02.23.2026 Summary We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural language tasks and executes protocol-compliant actions on heterogeneous optical devices through a structured tool abstraction layer. We implement 64 standardized MCP tools across 8 representative optical devices and construct a 410-task benchmark to evaluate request understanding, role-aware responses, multi-step coordination, robustness to linguistic variation, and error handling. We assess two deployment configurations–commercial online LLMs and locally hosted open-source LLMs–and compare them with LLM-based code generation baselines. AgentOptics achieves 87.7%–99.0% average task success rates, significantly outperforming code-generation approaches, which reach up to 50% success. We further demonstrate broader applicability through five case studies extending beyond device-level control to system orchestration, monitoring, and closed-loop optimization. These include DWDM link provisioning and coordinated monitoring of coherent 400 GbE and analog radio-over-fiber (ARoF) channels; autonomous characterization and bias optimization of a wideband ARoF link carrying 5G fronthaul traffic; multi-span channel provisioning with launch power optimization; closed-loop fiber polarization stabilization; and distributed acoustic sensing (DAS)-based fiber monitoring with LLM-assisted event detection. These results establish AgentOptics as a scalable, robust paradigm for autonomous control and orchestration of heterogeneous optical systems. Source arXiv: 2602.20144v1
Exact Solutions to Acoustoelectric Interactions in Arbitrary Geometries Authors William W. Roberts, Matt Eichenfield Published: 02.23.2026 Updated: 02.23.2026 Summary Acoustoelectric interactions occur when free carriers in a semiconductor interact with the fields of an acoustic wave in a piezoelectric medium. These interactions can amplify acoustic waves, as well as give rise to extremely large phononic nonlinearities and strong non-reciprocal effects. Despite the tremendous progress in the last ten years, the field is entirely dependent on analytical and perturbative solutions for the two simplest arrangements of piezoelectric-semiconductor materials. While these models have allowed the field to advance substantially, new geometries are arising that do not satisfy assumptions integral to these canonical models. These models rely on simplifying assumptions that remove the tensorial nature of the materials, restricting analysis to plane wave and perturbative solutions. Such restrictions fails to capture the non-perturbative nature of the acoustoelectric effect, illustrating the need for more advanced computational methods to analyze acoustoelectric systems. We develop, for the first time, a finite element method (FEM) model to solve for acoustoelectric interactions in arbitrary geometries. We use the model to verify existing results for amplification, dispersion, and non-reciprocity obtained from the canonical models. We then examine the acoustoelectric effect in two geometries not covered by the canonical models: a thin piezoelectric film placed above a semiconductor substrate and a fully 2D waveguide under a thin semiconductor layer. This work lays the foundation for accurate modeling of arbitrary acoustoelectric geometries such as those currently being developed for all-acoustic radio frequency (RF) signal processing, acoustoelectrically enhanced photonic devices, and quantum acoustoelectric devices. Source arXiv: 2602.19482v1
Exact Solutions to Acoustoelectric Interactions in Arbitrary Geometries Authors William W. Roberts, Matt Eichenfield Published: 02.23.2026 Updated: 02.25.2026 Summary Acoustoelectric interactions occur when free carriers in a semiconductor interact with the fields of an acoustic wave in a piezoelectric medium. These interactions can amplify acoustic waves, as well as give rise to extremely large phononic nonlinearities and strong non-reciprocal effects. The field of acoustoelectric devices is currently dependent on analytical and perturbative solutions for the two simplest arrangements of piezoelectric-semiconductor materials. While these canonical models have allowed the field to advance substantially, new geometries are arising that do not satisfy assumptions integral to these models. These assumptions include the treatment of the interactions between the acoustic fields and free carriers as weak, the neglect of the tensorial nature of the material properties, the omission of the spatial variations in the phonons’ electric field profiles, and the disregard of elastic coupling across material boundaries, among others. We develop, for the first time, a finite element method (FEM) model to solve for acoustoelectric interactions in arbitrary geometries that avoids making the assumptions of the canonical models. We verify the FEM model using results for amplification, dispersion, and non-reciprocity obtained from the canonical models in their regime of validity. We then examine the acoustoelectric effect in two geometries not covered by the canonical models: a thin piezoelectric film placed on a semiconductor substrate and a fully 2D waveguide under a thin semiconductor layer. This work lays the foundation for accurate modeling of arbitrary acoustoelectric geometries such as those currently being developed for all-acoustic radio frequency (RF) signal processing, acoustoelectrically enhanced photonic devices, and quantum acoustoelectric devices. Source arXiv: 2602.19482v2
Superconducting phase diagram of multi-layer square-planar nickelates Authors Grace A. Pan, Dan Ferenc Segedin, Sophia F. R. TenHuisen, Lopa Bhatt, Harrison LaBollita, Abigail Y. Jiang, Qi Song, Ari B. Turkiewicz, Denitsa R. Baykusheva, Abhishek Nag, Stefano Agrestini, Ke-Jin Zhou, Jonathan Pelliciari, Valentina Bisogni, Hua Zhou, Mark P. M. Dean, Hanjong Paik, David A. Muller, Lena F. Kourkoutis, Charles M. Brooks, Matteo Mitrano, Antia S. Botana, Berit H. Goodge, Julia A. Mundy Published: 02.22.2026 Updated: 02.22.2026 Summary The discovery of superconductivity in square-planar nickelates has offered a rich materials platform to explore the origins of cuprate-like superconductivity. Experimental investigations however have largely been limited to the infinite-layer $R$NiO$_2$ ($R$=rare-earth) nickelates. Here, we construct a phase diagram of multi-layer square-planar Nd$_{n+1}$Ni$_n$O$_{2n+2}$ compounds and discover signatures of superconductivity for $n$ = 4 – 8. Upon decreasing the dimensionality $n$, the superconducting anisotropy evolves due to 4$f$ electron effects, and electronic structure characteristics approach cuprate-like behavior. Magnetic fluctuations persist from within the superconducting regime and into the over-doped, non-superconducting regime. Remarkably, the superconducting regime overlaps with that of chemically-doped infinite-layer nickelates, demonstrating underlying commonalities and distinct differences across varying structural realizations of square-planar nickelates. Our work establishes this layered template for creating new nickel-based superconductors. Source arXiv: 2602.19093v1
Engineering quantum criticality and dynamics on an analog-digital simulator Authors Alexandra A. Geim, Nazli Ugur Koyluoglu, Simon J. Evered, Rahul Sahay, Sophie H. Li, Muqing Xu, Dolev Bluvstein, Nik O. Gjonbalaj, Nishad Maskara, Marcin Kalinowski, Tom Manovitz, Ruben Verresen, Susanne F. Yelin, Johannes Feldmeier, Markus Greiner, Vladan Vuletic, Mikhail D. Lukin Published: 02.20.2026 Updated: 02.20.2026 Summary Understanding emergent phenomena in out-of-equilibrium interacting many-body systems is an exciting frontier in physical science. While quantum simulators represent a promising approach to this long-standing problem, in practice it can be challenging to directly realize the required interactions, measure arbitrary observables, and mitigate errors. Here we use coherent mapping between the Rydberg and hyperfine qubits in a neutral atom array simulator to engineer and probe complex quantum dynamics. We combine efficient analog dynamics with fully programmable state preparation and measurement, leverage non-destructive readout for loss information and atomic qubit reuse, and use an atom reservoir for replacing lost atoms. With this analog-digital approach, we first demonstrate dynamical engineering of ring-exchange and particle hopping dynamics via Floquet driving and measure the spectral function of single excitations by evolving initial superposition states. Extending these techniques to a 271-site kagome lattice, we employ closed-loop optimization to target an out-of-equilibrium critical quantum spin liquid of the Rokhsar-Kivelson type. We observe the key features of such a state, including the absence of local order, many-body coherences between nearly equal-amplitude dimer configurations over up to 18 sites, and universal correlations consistent with predictions from field theory. Together, these results pave the way for using dynamical control in analog-digital quantum simulators to study complex quantum many-body systems. Source arXiv: 2602.18555v1
Quantum superresolution and noise spectroscopy with quantum computing Authors James W. Gardner, Federico Belliardo, Gideon Lee, Tuvia Gefen, Liang Jiang Published: 02.19.2026 Updated: 02.19.2026 Summary Quantum metrology of an incoherent signal is a canonical sensing problem related to superresolution and noise spectroscopy. We show that quantum computing can accelerate searches for a weak incoherent signal when the signal and noise are not precisely known. In particular, we consider weak Schur sampling, density matrix exponentiation, and quantum signal processing for testing the rank, purity, and spectral gap of the unknown quantum state to detect the incoherent signal. We show that these algorithms are faster than full-state tomography, which scales with the dimension of the Hilbert space. We apply our results to detecting exoplanets, stochastic gravitational waves, ultralight dark matter, geontropic quantum gravity, and Pauli noise. Source arXiv: 2602.17862v1
Approaching the Limit in Multiparameter AC Magnetometry with Quantum Control Authors Takuya Isogawa, Zhiyao Hu, Ayumi Kanamoto, Nutdech Phadetsuwannukun, Shilin Wang, Shunsuke Nishimura, Boning Li, Liang Jiang, Zain H. Saleem, Guoqing Wang, Haidong Yuan, Paola Cappellaro Published: 02.19.2026 Updated: 02.19.2026 Summary Simultaneously estimating multiple parameters at the ultimate limit is a central challenge in quantum metrology, often hindered by inherent incompatibilities in optimal estimation strategies. At its most extreme, this incompatibility culminates in a fundamental impossibility when the quantum Fisher information matrix (QFIM) becomes singular, rendering joint estimation unattainable. This is the case for a canonical problem: estimating the amplitude and frequency of an AC magnetic field, where the generators are parallel to each other. Here, we introduce a quantum control protocol that resolves this singularity. Our control protocol strategically engineers the sensor’s time evolution so the generators for the two parameters become orthogonal. It not only removes the singularity but also restores the optimal scaling of precision with interrogation time for both parameters simultaneously. We experimentally validate this protocol using a nitrogen-vacancy center in diamond at room temperature, demonstrating the concurrent achievement of the optimal scaling for both parameters under realistic conditions. Source arXiv: 2602.17648v1
Recirculating Quantum Photonic Networks for Fast Deterministic Quantum Information Processing Authors Emil Grovn, Matias Bundgaard-Nielsen, Jesper Mørk, Dirk Englund, Mikkel Heuck Published: 02.11.2026 Updated: 02.11.2026 Summary A fundamental challenge in photonics-based deterministic quantum information processing is to realize key transformations on time scales shorter than those of detrimental decoherence and loss mechanisms. This challenge has been addressed through device-focused approaches that aim to increase nonlinear interactions relative to decoherence rates. In this work, we adopt a complementary architecture-focused approach by proposing a recirculating quantum photonic network (RQPN) that minimizes the duration of quantum information processing tasks, thereby reducing the requirements on nonlinear interaction rates. The RQPN consists of a network of all-to-all connected nonlinear cavities with dynamically controlled waveguide couplings, and it processes information by capturing a photonic input state, recirculating photons between the cavities, and releasing a photonic output state. We demonstrate the RQPN’s architectural advantage through two examples: first, we show that processing all qubits simultaneously yields faster operations than single- and two-qubit decompositions of the three-qubit Toffoli gate. Second, we demonstrate implementations of a measurement-free correction for single-photon loss, achieving up to seven-fold speedups and significantly improved hardware efficiency relative to state-of-the-art architecture proposals. Our work shows that a single hardware-efficient recirculating architecture substantially reduces the temporal overhead of multi-qubit gates and quantum error correction, thereby lowering the barrier to experimental realizations of deterministic photonic quantum information processing. Source arXiv: 2602.11033v1
LitBench: A Graph-Centric Large Language Model Benchmarking Tool For Literature Tasks Authors Andreas Varvarigos, Ali Maatouk, Jiasheng Zhang, Ngoc Bui, Jialin Chen, Leandros Tassiulas, Rex Ying Published: 02.10.2026 Updated: 02.10.2026 Summary While large language models (LLMs) have become the de facto framework for literature-related tasks, they still struggle to function as domain-specific literature agents due to their inability to connect pieces of knowledge and reason across domain-specific contexts, terminologies, and nomenclatures. This challenge underscores the need for a tool that facilitates such domain-specific adaptation and enables rigorous benchmarking across literature tasks. To that end, we introduce LitBench, a benchmarking tool designed to enable the development and evaluation of domain-specific LLMs tailored to literature-related tasks. At its core, LitBench uses a data curation process that generates domain-specific literature sub-graphs and constructs training and evaluation datasets based on the textual attributes of the resulting nodes and edges. The tool is designed for flexibility, supporting the curation of literature graphs across any domain chosen by the user, whether high-level fields or specialized interdisciplinary areas. In addition to dataset curation, LitBench defines a comprehensive suite of literature tasks, ranging from node and edge level analyses to advanced applications such as related work generation. These tasks enable LLMs to internalize domain-specific knowledge and relationships embedded in the curated graph during training, while also supporting rigorous evaluation of model performance. Our results show that small domain-specific LLMs trained and evaluated on LitBench datasets achieve competitive performance compared to state-of-the-art models like GPT-4o and DeepSeek-R1. To enhance accessibility and ease of use, we open-source the tool along with an AI agent tool that streamlines data curation, model training, and evaluation. Source arXiv: 2603.00051v1
Quantization-aware Photonic Homodyne computing for Accelerated Artificial Intelligence and Scientific Simulation Authors Lian Zhou, Kaiwen Xue, Amirhossein Fallah, Lijin Liu, Chun-Ho Lee, Kiwon Kwon, Clayton Cheung, Yuan Li, Yue Yu, Yun-Jhu Lee, Songlin Zhao, Ryan Hamerly, Edo Waks, Dirk Englund, Constantine Sideris, Mengjie Yu, Zaijun Chen Published: 02.09.2026 Updated: 02.09.2026 Summary Modern problems in high-performance computing, ranging from training and inferencing deep learning models in computer vision and language models to simulating complex physical systems with nonlinearly-coupled equations, require exponential growth of computational resources. Photonic analog systems are emerging with solutions of intrinsic parallelism, high bandwidth, and low propagation loss. However, their application has been hindered by the low analog accuracy due to the electro-optic distortion, material nonlinearities, and signal-to-noise ratios. Here we overcome this barrier with a quantization-aware digital-photonic mixed-precision framework across chiplets for accelerated AI processing and physical simulation. Using Lithium Niobate photonics with channel equalization techniques, we demonstrate linear multiplication (9-bit amplitude-phase decoupling) in homodyne optical logics with 6-bit precision at the clock rate of 128 giga-symbol-per-second (128 GS/s), enabling AI processing with 6 ns latency. Codesign hardware-algorithms, including iterative solvers, sparse-dense quantization, and bit-sliced matrix multiplication, explore photonic amplitude and phase coherence for complex-valued, physics-inspired computation. In electromagnetic problems, our approach yields 12-bit solutions for partial differential equations (PDEs) in scattering problems that would conventionally require up to 32-bit and often even 64-bit precision. These results preserve digital-level fidelity while leveraging the high-speed low-energy photonic hardware, establishing a pathway toward general-purpose optical acceleration for generative artificial intelligence, real-time robotics, and accurate simulation for climate challenges and biological discoveries. Source arXiv: 2602.08269v1
HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation Authors Hiren Madhu, Ngoc Bui, Ali Maatouk, Leandros Tassiulas, Smita Krishnaswamy, Menglin Yang, Sukanta Ganguly, Kiran Srinivasan, Rex Ying Published: 02.08.2026 Updated: 02.08.2026 Summary Embedding geometry plays a fundamental role in retrieval quality, yet dense retrievers for retrieval-augmented generation (RAG) remain largely confined to Euclidean space. However, natural language exhibits hierarchical structure from broad topics to specific entities that Euclidean embeddings fail to preserve, causing semantically distant documents to appear spuriously similar and increasing hallucination risk. To address these limitations, we introduce hyperbolic dense retrieval, developing two model variants in the Lorentz model of hyperbolic space: HyTE-FH, a fully hyperbolic transformer, and HyTE-H, a hybrid architecture projecting pre-trained Euclidean embeddings into hyperbolic space. To prevent representational collapse during sequence aggregation, we introduce the Outward Einstein Midpoint, a geometry-aware pooling operator that provably preserves hierarchical structure. On MTEB, HyTE-FH outperforms equivalent Euclidean baselines, while on RAGBench, HyTE-H achieves up to 29% gains over Euclidean baselines in context relevance and answer relevance using substantially smaller models than current state-of-the-art retrievers. Our analysis also reveals that hyperbolic representations encode document specificity through norm-based separation, with over 20% radial increase from general to specific concepts, a property absent in Euclidean embeddings, underscoring the critical role of geometric inductive bias in faithful RAG systems. Source arXiv: 2602.07739v1
Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings Authors Yidong Jiang, Junrong Chen, Eftychia Makri, Jialin Chen, Peiwen Li, Ali Maatouk, Leandros Tassiulas, Eliot Brenner, Bing Xiang, Rex Ying Published: 02.07.2026 Updated: 02.07.2026 Summary With increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures. However, existing benchmarks often focus on isolated details, failing to reflect the complexity of professional analysis that requires synthesizing information across multiple documents, reporting periods, and corporate entities. They do not distinguish whether errors stem from retrieval failures, generation flaws, finance-specific reasoning mistakes, or misunderstanding of the query or context. This makes it difficult to pinpoint performance bottlenecks. To bridge these gaps, we introduce Fin-RATE, a benchmark built on U.S. Securities and Exchange Commission (SEC) filings and mirror financial analyst workflows through three pathways: detail-oriented reasoning within individual disclosures, cross-entity comparison under shared topics, and longitudinal tracking of the same firm across reporting periods. We benchmark 17 leading LLMs, spanning open-source, closed-source, and finance-specialized models, under both ground-truth context and retrieval-augmented settings. Results show substantial performance degradation, with accuracy dropping by 18.60% and 14.35% as tasks shift from single-document reasoning to longitudinal and cross-entity analysis. This is driven by rising comparison hallucinations, time and entity mismatches, and mirrored by declines in reasoning and factuality–limitations that prior benchmarks have yet to formally categorize or quantify. Source arXiv: 2602.07294v1
Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings Authors Yidong Jiang, Junrong Chen, Eftychia Makri, Jialin Chen, Peiwen Li, Ali Maatouk, Leandros Tassiulas, Eliot Brenner, Bing Xiang, Rex Ying Published: 02.07.2026 Updated: 02.12.2026 Summary With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures. However, existing benchmarks often focus on isolated details, failing to reflect the complexity of professional analysis that requires synthesizing information across multiple documents, reporting periods, and corporate entities. Furthermore, these benchmarks do not disentangle whether errors arise from retrieval failures, generation inaccuracies, domain-specific reasoning mistakes, or misinterpretation of the query or context, making it difficult to precisely diagnose performance bottlenecks. To bridge these gaps, we introduce Fin-RATE, a benchmark built on U.S. Securities and Exchange Commission (SEC) filings and mirroring financial analyst workflows through three pathways: detail-oriented reasoning within individual disclosures, cross-entity comparison under shared topics, and longitudinal tracking of the same firm across reporting periods. We benchmark 17 leading LLMs, spanning open-source, closed-source, and finance-specialized models, under both ground-truth context and retrieval-augmented settings. Results show substantial performance degradation, with accuracy dropping by 18.60% and 14.35% as tasks shift from single-document reasoning to longitudinal and cross-entity analysis. This degradation is driven by increased comparison hallucinations, temporal and entity mismatches, and is further reflected in declines in reasoning quality and factual consistency–limitations that existing benchmarks have yet to formally categorize or quantify. Source arXiv: 2602.07294v2
Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings Authors Yidong Jiang, Junrong Chen, Eftychia Makri, Jialin Chen, Peiwen Li, Ali Maatouk, Leandros Tassiulas, Eliot Brenner, Bing Xiang, Rex Ying Published: 02.07.2026 Updated: 02.14.2026 Summary With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures. However, existing benchmarks often focus on isolated details, failing to reflect the complexity of professional analysis that requires synthesizing information across multiple documents, reporting periods, and corporate entities. Furthermore, these benchmarks do not disentangle whether errors arise from retrieval failures, generation inaccuracies, domain-specific reasoning mistakes, or misinterpretation of the query or context, making it difficult to precisely diagnose performance bottlenecks. To bridge these gaps, we introduce Fin-RATE, a benchmark built on U.S. Securities and Exchange Commission (SEC) filings and mirroring financial analyst workflows through three pathways: detail-oriented reasoning within individual disclosures, cross-entity comparison under shared topics, and longitudinal tracking of the same firm across reporting periods. We benchmark 17 leading LLMs, spanning open-source, closed-source, and finance-specialized models, under both ground-truth context and retrieval-augmented settings. Results show substantial performance degradation, with accuracy dropping by 18.60% and 14.35% as tasks shift from single-document reasoning to longitudinal and cross-entity analysis. This degradation is driven by increased comparison hallucinations, temporal and entity mismatches, and is further reflected in declines in reasoning quality and factual consistency–limitations that existing benchmarks have yet to formally categorize or quantify. Source arXiv: 2602.07294v3
InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning Authors Yuchen Yan, Liang Jiang, Jin Jiang, Shuaicheng Li, Zujie Wen, Zhiqiang Zhang, Jun Zhou, Jian Shao, Yueting Zhuang, Yongliang Shen Published: 02.06.2026 Updated: 02.09.2026 Summary Large reasoning models achieve strong performance by scaling inference-time chain-of-thought, but this paradigm suffers from quadratic cost, context length limits, and degraded reasoning due to lost-in-the-middle effects. Iterative reasoning mitigates these issues by periodically summarizing intermediate thoughts, yet existing methods rely on supervised learning or fixed heuristics and fail to optimize when to summarize, what to preserve, and how to resume reasoning. We propose InftyThink+, an end-to-end reinforcement learning framework that optimizes the entire iterative reasoning trajectory, building on model-controlled iteration boundaries and explicit summarization. InftyThink+ adopts a two-stage training scheme with supervised cold-start followed by trajectory-level reinforcement learning, enabling the model to learn strategic summarization and continuation decisions. Experiments on DeepSeek-R1-Distill-Qwen-1.5B show that InftyThink+ improves accuracy by 21% on AIME24 and outperforms conventional long chain-of-thought reinforcement learning by a clear margin, while also generalizing better to out-of-distribution benchmarks. Moreover, InftyThink+ significantly reduces inference latency and accelerates reinforcement learning training, demonstrating improved reasoning efficiency alongside stronger performance. Source arXiv: 2602.06960v2
InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning Authors Yuchen Yan, Liang Jiang, Jin Jiang, Shuaicheng Li, Zujie Wen, Zhiqiang Zhang, Jun Zhou, Jian Shao, Yueting Zhuang, Yongliang Shen Published: 02.06.2026 Updated: 02.06.2026 Summary Large reasoning models achieve strong performance by scaling inference-time chain-of-thought, but this paradigm suffers from quadratic cost, context length limits, and degraded reasoning due to lost-in-the-middle effects. Iterative reasoning mitigates these issues by periodically summarizing intermediate thoughts, yet existing methods rely on supervised learning or fixed heuristics and fail to optimize when to summarize, what to preserve, and how to resume reasoning. We propose InftyThink+, an end-to-end reinforcement learning framework that optimizes the entire iterative reasoning trajectory, building on model-controlled iteration boundaries and explicit summarization. InftyThink+ adopts a two-stage training scheme with supervised cold-start followed by trajectory-level reinforcement learning, enabling the model to learn strategic summarization and continuation decisions. Experiments on DeepSeek-R1-Distill-Qwen-1.5B show that InftyThink+ improves accuracy by 21% on AIME24 and outperforms conventional long chain-of-thought reinforcement learning by a clear margin, while also generalizing better to out-of-distribution benchmarks. Moreover, InftyThink+ significantly reduces inference latency and accelerates reinforcement learning training, demonstrating improved reasoning efficiency alongside stronger performance. Source arXiv: 2602.06960v1
Efficient learning of logical noise from syndrome data Authors Han Zheng, Chia-Tung Chu, Senrui Chen, Argyris Giannisis Manes, Su-un Lee, Sisi Zhou, Liang Jiang Published: 01.29.2026 Updated: 02.10.2026 Summary Characterizing errors in quantum circuits is essential for device calibration, yet detecting rare error events requires a large number of samples. This challenge is particularly severe in calibrating fault-tolerant, error-corrected circuits, where logical error probabilities are suppressed to higher order relative to physical noise and are therefore difficult to calibrate through direct logical measurements. Recently, Wagner et al. [PRL 130, 200601 (2023)] showed that, for phenomenological Pauli noise models, the logical channel can instead be inferred from syndrome measurement data generated during error correction. Here, we extend this framework to realistic circuit-level noise models. From a unified code-theoretic perspective and spacetime code formalism, we derive necessary and sufficient conditions for learning the logical channel from syndrome data alone and explicitly characterize the learnable degrees of freedom of circuit-level Pauli faults. Using Fourier analysis and compressed sensing, we develop efficient estimators with provable guarantees on sample complexity and computational cost. We further present an end-to-end protocol and demonstrate its performance on several syndrome-extraction circuits, achieving orders-of-magnitude sample-complexity savings over direct logical benchmarking. Our results establish syndrome-based learning as a practical approach to characterizing the logical channel in fault-tolerant quantum devices. Source arXiv: 2601.22286v2
Efficient learning of logical noise from syndrome data Authors Han Zheng, Chia-Tung Chu, Senrui Chen, Argyris Giannisis Manes, Su-un Lee, Sisi Zhou, Liang Jiang Published: 01.29.2026 Updated: 01.29.2026 Summary Characterizing errors in quantum circuits is essential for device calibration, yet detecting rare error events requires a large number of samples. This challenge is particularly severe in calibrating fault-tolerant, error-corrected circuits, where logical error probabilities are suppressed to higher order relative to physical noise and are therefore difficult to calibrate through direct logical measurements. Recently, Wagner et al. [PRL 130, 200601 (2023)] showed that, for phenomenological Pauli noise models, the logical channel can instead be inferred from syndrome measurement data generated during error correction. Here, we extend this framework to realistic circuit-level noise models. From a unified code-theoretic perspective and spacetime code formalism, we derive necessary and sufficient conditions for learning the logical channel from syndrome data alone and explicitly characterize the learnable degrees of freedom of circuit-level Pauli faults. Using Fourier analysis and compressed sensing, we develop efficient estimators with provable guarantees on sample complexity and computational cost. We further present an end-to-end protocol and demonstrate its performance on several syndrome-extraction circuits, achieving orders-of-magnitude sample-complexity savings over direct logical benchmarking. Our results establish syndrome-based learning as a practical approach to characterizing the logical channel in fault-tolerant quantum devices. Source arXiv: 2601.22286v1
Hierarchy of discriminative power and complexity in learning quantum ensembles Authors Jian Yao, Pengtao Li, Xiaohui Chen, Quntao Zhuang Published: 01.29.2026 Updated: 01.29.2026 Summary Distance metrics are central to machine learning, yet distances between ensembles of quantum states remain poorly understood due to fundamental quantum measurement constraints. We introduce a hierarchy of integral probability metrics, termed MMD-$k$, which generalizes the maximum mean discrepancy to quantum ensembles and exhibit a strict trade-off between discriminative power and statistical efficiency as the moment order $k$ increases. For pure-state ensembles of size $N$, estimating MMD-$k$ using experimentally feasible SWAP-test-based estimators requires $Θ(N^{2-2/k})$ samples for constant $k$, and $Θ(N^3)$ samples to achieve full discriminative power at $k = N$. In contrast, the quantum Wasserstein distance attains full discriminative power with $Θ(N^2 log N)$ samples. These results provide principled guidance for the design of loss functions in quantum machine learning, which we illustrate in the training quantum denoising diffusion probabilistic models. Source arXiv: 2601.22005v1
Multi-Modal Time Series Prediction via Mixture of Modulated Experts Authors Lige Zhang, Ali Maatouk, Jialin Chen, Leandros Tassiulas, Rex Ying Published: 01.29.2026 Updated: 01.29.2026 Summary Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecasting methods leverage textual information such as news reports to improve prediction, but most rely on token-level fusion that mixes temporal patches with language tokens in a shared embedding space. However, such fusion can be ill-suited when high-quality time-text pairs are scarce and when time series exhibit substantial variation in scale and characteristics, thus complicating cross-modal alignment. In parallel, Mixture-of-Experts (MoE) architectures have proven effective for both time series modeling and multi-modal learning, yet many existing MoE-based modality integration methods still depend on token-level fusion. To address this, we propose Expert Modulation, a new paradigm for multi-modal time series prediction that conditions both routing and expert computation on textual signals, enabling direct and efficient cross-modal control over expert behavior. Through comprehensive theoretical analysis and experiments, our proposed method demonstrates substantial improvements in multi-modal time series prediction. The current code is available at https://github.com/BruceZhangReve/MoME Source arXiv: 2601.21547v1
Transversal gates for quantum CSS codes Authors Eduardo Camps-Moreno, Hiram H. López, Gretchen L. Matthews, Narayanan Rengaswamy, Rodrigo San-José Published: 01.29.2026 Updated: 01.29.2026 Summary In this paper, we focus on the problem of computing the set of diagonal transversal gates fixing a CSS code. We determine the logical actions of the gates as well as the groups of transversal gates that induce non-trivial logical gates and logical identities. We explicitly declare the set of equations defining the groups, a key advantage and differentiator of our approach. We compute the complete set of transversal stabilizers and transversal gates for any CSS code arising from monomial codes, a family that includes decreasing monomial codes and polar codes. As a consequence, we recover and extend some results in the literature on CSS-T codes, triorthogonal codes, and divisible codes. Source arXiv: 2601.21514v1
In-situ benchmarking of fault-tolerant quantum circuits. I. Clifford circuits Authors Xiao Xiao, Dominik Hangleiter, Dolev Bluvstein, Mikhail D. Lukin, Michael J. Gullans Published: 01.29.2026 Updated: 02.04.2026 Summary Benchmarking physical devices and verifying logical algorithms are important tasks for scalable fault-tolerant quantum computing. Numerous protocols exist for benchmarking devices before running actual algorithms. In this work, we show that both physical and logical errors of fault-tolerant circuits can even be characterized in-situ using syndrome data. To achieve this, we map general fault-tolerant Clifford circuits to subsystem codes using the spacetime code formalism and develop a scheme for estimating Pauli noise in Clifford circuits using syndrome data. We give necessary and sufficient conditions for the learnability of physical and logical noise from given syndrome data, and show that we can accurately predict logical fidelities from the same data. Importantly, our approach requires only a polynomial sample size, even when the logical error rate is exponentially suppressed by the code distance, and thus gives an exponential advantage against methods that use only logical data such as direct fidelity estimation. We demonstrate the practical applicability of our methods in various scenarios using synthetic data as well as the experimental data from a recent demonstration of fault-tolerant circuits by Bluvstein et al. [Nature 626, 7997 (2024)]. Our methods provide an efficient, in-situ way of characterizing a fault-tolerant quantum computer to help gate calibration, improve decoding accuracy, and verify logical circuits. Source arXiv: 2601.21472v2
In-situ benchmarking of fault-tolerant quantum circuits. I. Clifford circuits Authors Xiao Xiao, Dominik Hangleiter, Dolev Bluvstein, Mikhail D. Lukin, Michael J. Gullans Published: 01.29.2026 Updated: 01.29.2026 Summary Benchmarking physical devices and verifying logical algorithms are important tasks for scalable fault-tolerant quantum computing. Numerous protocols exist for benchmarking devices before running actual algorithms. In this work, we show that both physical and logical errors of fault-tolerant circuits can even be characterized in-situ using syndrome data. To achieve this, we map general fault-tolerant Clifford circuits to subsystem codes using the spacetime code formalism and develop a scheme for estimating Pauli noise in Clifford circuits using syndrome data. We give necessary and sufficient conditions for the learnability of physical and logical noise from given syndrome data, and show that we can accurately predict logical fidelities from the same data. Importantly, our approach requires only a polynomial sample size, even when the logical error rate is exponentially suppressed by the code distance, and thus gives an exponential advantage against methods that use only logical data such as direct fidelity estimation. We demonstrate the practical applicability of our methods in various scenarios using synthetic data as well as the experimental data from a recent demonstration of fault-tolerant circuits by Bluvstein et al. [Nature 626, 7997 (2024)]. Our methods provide an efficient, in-situ way of characterizing a fault-tolerant quantum computer to help gate calibration, improve decoding accuracy, and verify logical circuits. Source arXiv: 2601.21472v1
Entangling logical qubits without physical operations Authors Jin Ming Koh, Anqi Gong, Andrei C. Diaconu, Daniel Bochen Tan, Alexandra A. Geim, Michael J. Gullans, Norman Y. Yao, Mikhail D. Lukin, Shayan Majidy Published: 01.28.2026 Updated: 01.28.2026 Summary Fault-tolerant logical entangling gates are essential for scalable quantum computing, but are limited by the error rates and overheads of physical two-qubit gates and measurements. To address this limitation, we introduce phantom codes-quantum error-correcting codes that realize entangling gates between all logical qubits in a code block purely through relabelling of physical qubits during compilation, yielding perfect fidelity with no spatial or temporal overhead. We present a systematic study of such codes. First, we identify phantom codes using complementary numerical and analytical approaches. We exhaustively enumerate all $2.71 times 10^{10}$ inequivalent CSS codes up to $n=14$ and identify additional instances up to $n=21$ via SAT-based methods. We then construct higher-distance phantom-code families using quantum Reed-Muller codes and the binarization of qudit codes. Across all identified codes, we characterize other supported fault-tolerant logical Clifford and non-Clifford operations. Second, through end-to-end noisy simulations with state preparation, full QEC cycles, and realistic physical error rates, we demonstrate scalable advantages of phantom codes over the surface code across multiple tasks. We observe a one-to-two order-of-magnitude reduction in logical infidelity at comparable qubit overhead for GHZ-state preparation and Trotterized many-body simulation tasks, given a modest preselection acceptance rate. Our work establishes phantom codes as a viable architectural route to fault-tolerant quantum computation with scalable benefits for workloads with dense local entangling structure, and introduces general tools for systematically exploring the broader landscape of quantum error-correcting codes. Source arXiv: 2601.20927v1
Foundry-Enabled Patterning of Diamond Quantum Microchiplets for Scalable Quantum Photonics Authors Jawaher Almutlaq, Alessandro Buzzi, Anders Khaykin, Linsen Li, William Yzaguirre, Maxim Sirotin, Gerald Gilbert, Genevieve Clark, Dirk Englund Published: 01.27.2026 Updated: 01.27.2026 Summary Quantum technologies promise secure communication networks and powerful new forms of information processing, but building these systems at scale remains a major challenge. Diamond is an especially attractive material for quantum devices because it can host atomic-scale defects that emit single photons and store quantum information with exceptional stability. However, fabricating the optical structures needed to control light in diamond typically relies on slow, bespoke processes that are difficult to scale. In this work, we introduce a manufacturing approach that brings diamond quantum photonics closer to industrial production. Instead of sequentially defining each device by lithography written directly on diamond, we fabricate high-precision silicon masks using commercial semiconductor foundries and transfer them onto diamond via microtransfer printing. These masks define large arrays of nanoscale optical structures, shifting the most demanding pattern-definition steps away from the diamond substrate, improving uniformity, yield, and throughput. Using this method, we demonstrate hundreds of diamond “quantum microchiplets” with improved optical performance and controlled interaction with quantum emitters. The chiplet format allows defective devices to be replaced and enables integration with existing photonic and electronic circuits. Our results show that high-quality diamond quantum devices can be produced using scalable, foundry-compatible techniques. This approach provides a practical pathway toward large-scale quantum photonic systems and hybrid quantum-classical technologies built on established semiconductor manufacturing infrastructure. Source arXiv: 2601.20025v1
Quick Change Detection in Discrete-Time in Presence of a Covert Adversary Authors Amir Reza Ramtin, Philippe Nain, Don Towsley Published: 01.27.2026 Updated: 01.27.2026 Summary We study the problem of covert quickest change detection in a discrete-time setting, where a sequence of observations undergoes a distributional change at an unknown time. Unlike classical formulations, we consider a covert adversary who has knowledge of the detector’s false alarm constraint parameter $γ$ and selects a stationary post-change distribution that depends on it, seeking to remain undetected for as long as possible. Building on the theoretical foundations of the CuSum procedure, we rigorously characterize the asymptotic behavior of the average detection delay (ADD) and the average time to false alarm (AT2FA) when the post-change distribution converges to the pre-change distribution as $γto infty$. Our analysis establishes exact asymptotic expressions for these quantities, extending and refining classical results that no longer hold in this regime. We identify the critical scaling laws governing covert behavior and derive explicit conditions under which an adversary can maintain covertness, defined by ADD = $Θ(γ)$, whereas in the classical setting, ADD grows only as $mathcal{O}(log γ)$. In particular, for Gaussian and Exponential models under adversarial perturbations of their respective parameters, we asymptotically characterize ADD as a function of the Kullback–Leibler divergence between the pre- and post-change distributions and $γ$. Source arXiv: 2601.20022v1
Quickest Change Detection in Discrete-Time in Presence of a Covert Adversary Authors Amir Reza Ramtin, Philippe Nain, Don Towsley Published: 01.27.2026 Updated: 02.16.2026 Summary We study the problem of covert quickest change detection in a discrete-time setting, where a sequence of observations undergoes a distributional change at an unknown time. Unlike classical formulations, we consider a covert adversary who has knowledge of the detector’s false alarm constraint parameter $γ$ and selects a stationary post-change distribution that depends on it, seeking to remain undetected for as long as possible. Building on the theoretical foundations of the CuSum procedure, we rigorously characterize the asymptotic behavior of the average detection delay (ADD) and the average time to false alarm (AT2FA) when the post-change distribution converges to the pre-change distribution as $γto infty$. Our analysis establishes exact asymptotic expressions for these quantities, extending and refining classical results that no longer hold in this regime. We identify the critical scaling laws governing covert behavior and derive explicit conditions under which an adversary can maintain covertness, defined by ADD = $Θ(γ)$, whereas in the classical setting, ADD grows only as $mathcal{O}(log γ)$. In particular, for Gaussian and Exponential models under adversarial perturbations of their respective parameters, we asymptotically characterize ADD as a function of the Kullback–Leibler divergence between the pre- and post-change distributions and $γ$. Source arXiv: 2601.20022v2
Multivariate Multicycle Codes for Complete Single-Shot Decoding Authors Feroz Ahmed Mian, Owen Gwilliam, Stefan Krastanov Published: 01.26.2026 Updated: 01.26.2026 Summary We introduce multivariate multicycle (MM) codes, a new family of quantum error correcting codes that unifies and generalizes bivariate bicycle codes, multivariate bicycle codes, abelian two-block group algebra codes, generalized bicycle codes, trivariate tricycle codes, and n-dimensional toric codes. MM codes are Calderbank-Shor-Steane (CSS) codes defined from length-t chain complexes with $t ge 4$. The chief advantage of these codes is that they possess metachecks and high confinement that permit complete single-shot decoding, while also having additional algebraic structure that might enable logical non-Clifford gates. We offer a framework that facilitates the construction of long-length chain complexes through the use of Koszul complex. In particular, obtaining explicit boundary maps (parity check and metacheck matrices) is particularly straightforward in our approach. This simple but very general parameterization of codes permitted us to efficiently perform a numerical search, where we identify several MM code candidates that demonstrate these capabilities at high rates and high code distances. Examples of new codes with parameters $[[n,k,d]]$ include $[[96, 12, 8]]$, $[[96, 44, 4]]$ $[[144, 40, 4]]$, $[[216, 12, 12]]$, $[[360, 30, 6]]$, $[[384, 80, 4]]$, $[[486, 24, 12]]$, $[[486, 66, 9]]$ and $[[648, 60, 9]]$. Notably, our codes achieve confinement profiles that surpass all known single-shot decodable quantum CSS codes of practical blocksize. Source arXiv: 2601.18879v1
Upper bounds on the purity of Wigner positive quantum states that verify the Wigner entropy conjecture Authors Qipeng Qian, Christos Gagatsos Published: 01.23.2026 Updated: 01.23.2026 Summary We present analytical results toward the Wigner entropy conjecture, which posits that among all physical Wigner non-negative states the Wigner entropy is minimized by pure Gaussian states for which it attains the value $1+lnπ$.Working under a minimal set of constraints on the Wigner function, namely, non-negativity, normalization, and the pointwise bound $πWle 1$, we construct an explicit hierarchy of lower bounds $B_n$ on $S[W]$ by combining a truncated series lower bound for $-ln x$ with moment identities of the Wigner function.This yields closed-form purity-based sufficient conditions ensuring $S[W]ge 1+lnπ$.In particular, we first prove that all Wigner non-negative states with $μle 4-2sqrt3$ satisfy the Wigner entropy conjecture. We further obtain a systematic purity-only relaxation of the hierarchy, yielding the simple sufficient condition $μle 2/e$. On top of aforesaid results, our analysis clarifies why additional physicality constraints are necessary for purity-based approaches that aim to approach the extremal case $μleq1$. Source arXiv: 2601.16898v1
Integrated Photonic Quantum Computing: From Silicon to Lithium Niobate Authors Hui Zhang, Yiming Ma, Di Zhu, Yuancheng Zhan, Yuzhi Shi, Zhanshan Wang, Leong Chuan Kwek, Anthony Laing, Ai Qun Liu, Marko Loncar, Xinbin Cheng Published: 01.23.2026 Updated: 01.23.2026 Summary Quantum technologies have surpassed classical systems by leveraging the unique properties of superposition and entanglement in photons and matter. Recent advancements in integrated quantum photonics, especially in silicon-based and lithium niobate platforms, are pushing the technology toward greater scalability and functionality. Silicon circuits have progressed from centimeter-scale, dual-photon systems to millimeter-scale, high-density devices that integrate thousands of components, enabling sophisticated programmable manipulation of multi-photon states. Meanwhile, lithium niobate, thanks to its wide optical transmission window, outstanding nonlinear and electro-optic coefficients, and chemical stability, has emerged as an optimal substrate for fully integrated photonic quantum chips. Devices made from this material exhibit high efficiency in in generating, manipulating, converting, storing, and detecting photon states, thereby establishing a basis for deterministic multi-photon generation and single-photon quantum interactions, as well as comprehensive frequency-state control. This review explores the development of integrated photonic quantum technologies based on both silicon and lithium niobate, highlighting invaluable insights gained from silicon-based systems that can assist the scaling of lithium niobate technologies. It examines the functional integration mechanisms of lithium niobate in electro-optic tuning and nonlinear energy conversion, showcasing its transformative impact throughout the photonic quantum computing process. Looking ahead, we speculate on the developmental pathways for lithium niobate platforms and their potential to revolutionize areas such as quantum communication, complex system simulation, quantum sampling, and optical quantum computing paradigms. Source arXiv: 2601.16484v1
Heterogeneous Transfer of Thin Film BaTiO$_3$ onto Silicon for Device Fabrication Authors Temazulu S. Zulu, Larissa B. Little, Aaron M. Day, Chaoshen Zhang, Keith Powell, Kyeong-Yoon Baek, Benazir Fazlioglu-Yalcin, Neil Sinclair, Charles M. Brooks, David R. Barton, Marko Loncar, Julia A. Mundy Published: 01.21.2026 Updated: 01.21.2026 Summary Thin film BaTiO$_3$ has one of the highest known Pockels coefficients (>1200 pm/V), making it an attractive material for use in electro-optic devices. It is advantageous to integrate BaTiO$_3$ on silicon to enable complementary metal-oxide-semiconductor (CMOS) compatible processing. However, synthesis of high-quality BaTiO$_3$ directly on silicon remains a challenge. Here, we synthesize BaTiO$_3$ using hybrid metal-organic molecular beam epitaxy (hMBE) and demonstrate its transfer onto silicon using thermocompression bonding and chemical lift-off. Hybrid metal-organic MBE enables self-regulated synthesis of highly stoichiometric thin films at high growth rates (>100nm/hr). Our transfer method results in millimeter-scale areas of atomically flat, crack-free BaTiO$_3$ making it a potentially scalable method. Finally, we demonstrate the applicability of our process to device fabrication through characterization of lithographically-patterned and etch-transferred sub-micron features. Source arXiv: 2601.14551v1
Inverse Quantum Simulation for Quantum Material Design Authors Christian Kokail, Pavel E. Dolgirev, Rick van Bijnen, Daniel Gonzalez-Cuadra, Mikhail D. Lukin, Peter Zoller Published: 01.18.2026 Updated: 01.18.2026 Summary Quantum simulation provides a powerful route for exploring many-body phenomena beyond the capabilities of classical computation. Existing approaches typically proceed in the forward direction: a model Hamiltonian is specified, implemented on a programmable quantum platform, and its phase diagram and properties are explored. Here we present a quantum algorithmic framework for inverse quantum simulation, enabling quantum material design with desired properties. Target material characteristics are encoded as a cost function, which is minimized on quantum hardware to prepare a many-body state with the desired properties in quantum memory. Hamiltonian learning is then used to reconstruct a low-energy Hamiltonian for which this state is an approximate ground state, yielding a physically interpretable model that can guide experimental synthesis. As illustrative applications, we outline how the method can be used to search for high-temperature superconductors within the fermionic Hubbard model, enhancing $d$-wave correlations over a broad range of dopings and temperatures, design quantum phases by stabilizing a topological order through continuous Hamiltonian modifications, and optimize dynamical properties relevant for photochemistry and frequency- and momentum-resolved condensed-matter data. These results extend the scope of quantum simulators from exploring quantum many-body systems to designing and discovering new quantum materials. Source arXiv: 2601.12239v1
Controlling Rydberg atom-polariton interactions: from exceptional points to fast readout Authors Tamara Šumarac, Emily H. Qiu, Shai Tsesses, Peiran Niu, Adrian J. Menssen, Wenchao Xu, Valentin Walther, Uroš Delić, Soonwon Choi, Mikhail D. Lukin, Vladan Vuletić Published: 01.09.2026 Updated: 01.09.2026 Summary Rydberg atoms represent a platform underpinning many recent developments in quantum computation, simulation, sensing, and metrology. They further facilitate optical nonlinearity at the single-photon level when coupled to photons propagating in atomic clouds, which form collective atomic excitations called Rydberg polaritons, strongly interacting with each other. Here, we experimentally explore interactions between a Rydberg polariton in an atomic ensemble and a single, adjacent, Rydberg atom. We discover three different regimes of quantum dynamics corresponding to polariton blockade, coherent exchange, and probabilistic hopping, which are defined by their distinct transmission characteristics, with a transition through an exceptional point occurring between blockade and coherent exchange. We investigate the applications of such interactions for fast, non-destructive detection of Rydberg atoms and present proof-of-principle demonstrations for their potential application in nonlinear photonic networks. Source arXiv: 2601.06345v1
Benchmarking Quantum Data Center Architectures: A Performance and Scalability Perspective Authors Shahrooz Pouryousef, Eneet Kaur, Hassan Shapourian, Don Towsley, Ramana Kompella, Reza Nejabati Published: 01.04.2026 Updated: 01.04.2026 Summary Scalable distributed quantum computing (DQC) has motivated the design of multiple quantum data-center (QDC) architectures that overcome the limitations of single quantum processors through modular interconnection. While these architectures adopt fundamentally different design philosophies, their relative performance under realistic quantum hardware constraints remains poorly understood. In this paper, we present a systematic benchmarking study of four representative QDC architectures-QFly, BCube, Clos, and Fat-Tree-quantifying their impact on distributed quantum circuit execution latency, resource contention, and scalability. Focusing on quantum-specific effects absent from classical data-center evaluations, we analyze how optical-loss-induced Einstein-Podolsky-Rosen (EPR) pair generation delays, coherence-limited entanglement retry windows, and contention from teleportation-based non-local gates shape end-to-end execution performance. Across diverse circuit workloads, we evaluate how architectural properties such as path diversity and path length, and shared BSM (Bell State Measurement) resources interact with optical-switch insertion loss and reconfiguration delay. Our results show that distributed quantum performance is jointly shaped by topology, scheduling policies, and physical-layer parameters, and that these factors interact in nontrivial ways. Together, these insights provide quantitative guidance for the design of scalable and high-performance quantum data-center architectures for DQC. Source arXiv: 2601.01353v1
Benchmarking Quantum Data Center Architectures: A Performance and Scalability Perspective Authors Shahrooz Pouryousef, Eneet Kaur, Hassan Shapourian, Don Towsley, Ramana Kompella, Reza Nejabati Published: 01.04.2026 Updated: 01.10.2026 Summary Scalable distributed quantum computing (DQC) has motivated the design of multiple quantum data-center (QDC) architectures that overcome the limitations of single quantum processors through modular interconnection. While these architectures adopt fundamentally different design philosophies, their relative performance under realistic quantum hardware constraints remains poorly understood. In this paper, we present a systematic benchmarking study of four representative QDC architectures-QFly, BCube, Clos, and Fat-Tree-quantifying their impact on distributed quantum circuit execution latency, resource contention, and scalability. Focusing on quantum-specific effects absent from classical data-center evaluations, we analyze how optical-loss-induced Einstein-Podolsky-Rosen (EPR) pair generation delays, coherence-limited entanglement retry windows, and contention from teleportation-based non-local gates shape end-to-end execution performance. Across diverse circuit workloads, we evaluate how architectural properties such as path diversity and path length, and shared BSM (Bell State Measurement) resources interact with optical-switch insertion loss and reconfiguration delay. Our results show that distributed quantum performance is jointly shaped by topology, scheduling policies, and physical-layer parameters, and that these factors interact in nontrivial ways. Together, these insights provide quantitative guidance for the design of scalable and high-performance quantum data-center architectures for DQC. Source arXiv: 2601.01353v2
Generalized model of anisotropic thermo-optic response on thin-film lithium niobate platform Authors Joonsup Shim, Seonghun Kim, Shengyuan Lu, Jiayu Yang, Seongjin Jeon, Sanghyeon Kim, Marko Lončar, Young-Ik Sohn Published: 01.01.2026 Updated: 01.01.2026 Summary Thermo-optic (TO) control is crucial for thin-film lithium niobate (TFLN) photonic integrated circuits (PICs), offering a simple and practical method for low-frequency and DC tuning while remaining compatible with high-frequency electro-optic (EO) modulation. In x-cut TFLN, the TO response is inherently anisotropic, depending on both waveguide propagation angle and polarization due to the mode-specific overlap of the electric field with the ordinary and extraordinary refractive index axes of the crystal. Despite its significance, a systematic and quantitative analysis of this anisotropy has remained elusive. Here, we present the first generalized analytical model that describes the anisotropic TO response as a function of polarization and arbitrary waveguide orientation, and rigorously validate it through numerical simulations and experiments. This study provides foundational insight into anisotropic thermal tuning and enables new opportunities for engineering energy-efficient and scalable photonic design in next-generation TFLN PICs. Source arXiv: 2601.00174v1
Fast-Recovery Epitaxial NbN Superconducting Nanowire Single-Photon Detectors with Saturated Efficiency at 1550 nm in Liquid Helium Authors Francesca Incalza, Matteo Castellani, Dip Joti Paul, Alejandro Simon, Emma Batson, Davide Mondin, Owen Medeiros, Karl K. Berggren Published: 12.19.2025 Updated: 12.19.2025 Summary Achieving both high internal efficiency and fast reset times at elevated temperatures remains challenging due to limited understanding of how film properties govern SNSPD performance. We demonstrate that epitaxial NbN films on sapphire enable simultaneous high efficiency and rapid response. We fabricate and characterize SNSPDs based on these films deposited via DC magnetron sputtering on c-cut sapphire. High-quality epitaxial growth preserves a low electron diffusion coefficient and promotes strong electron-phonon coupling, yielding a high critical temperature and efficient hotspot formation in the dirty limit. X-ray diffraction and transmission electron microscopy confirm epitaxial alignment and lattice order. Nanowires of 20 nm width exhibit saturated internal efficiency at 1550 nm wavelength and short reset times at 4.2 K, enabled by lattice matching and high thermal conductance of the sapphire interface. Ab initio modeling reproduces photon count rates, linking device performance quantitatively to film properties such as diffusivity and electron-phonon coupling. Source arXiv: 2512.18063v1
Zero-added-loss entanglement multiplexing using time-bin spectral shearing Authors Joseph C. Chapman, Muneer Alshowkan, Jack Postlewaite, Saikat Guha, Nageswara Rao Published: 12.19.2025 Updated: 12.19.2025 Summary High-quality quantum communications that enable important capabilities, such as distributed quantum computing and sensing, will require quantum repeaters for providing high-quality entanglement. To realize high-rate heralded entanglement for quantum repeaters, Chen et al. [Phys. Rev. Appl. 19, 054209 (2023)] proposed a scheme for heralded-multiplexed generation of quasi-deterministic entangled photon pairs, called zero-added-loss multiplexing (ZALM). Here, we propose a design of ZALM source using time-bin entanglement and spectral shearing. Additionally, we provide an analysis of experimentally relevant spectral-shearing parameters to optimize the spectral multiplexing. Moreover, we experimentally verify the compatibility of time-bin pulses and spectral shearing, as supported by observation of no phase shift when the same shearing is applied to both time bins. These results expand the benefits of applying a ZALM source to time-bin entanglement use cases. Moreover, more fully demonstrating time-bin and spectral shearing compatibility clears a path towards a broader use of spectral shearing that provides a deterministic frequency shift of high utility. Source arXiv: 2512.17148v1
Comparing Homodyne and Heterodyne Tomography of Quantum States of Light Authors Rhea P. Fernandes, Andrew J. Pizzimenti, Christos N. Gagatsos, Joseph M. Lukens Published: 12.18.2025 Updated: 12.18.2025 Summary Non-Gaussian quantum states are critical resources in photonic quantum information processing, rendering their generation and characterization of increasing importance in quantum optics. In this work, we theoretically and numerically analyze the relative efficiency of homodyne versus heterodyne measurements for reconstructing non-Gaussian states, a major outstanding question in continuous-variable tomography. Combining a Fisher information-based formalism with simulated experiments, we find homodyne tomography to outperform heterodyne measurements for all non-Gaussian states tested, although the separation between the two modalities proves significantly narrower than suggested by the asymptotic Cramer-Rao lower bound. Our results should find use for optimizing measurement strategies in practical continuous-variable quantum systems. Source arXiv: 2512.17031v1
QuantumSavory: Write Symbolically, Run on Any Backend — A Unified Simulation Toolkit for Quantum Computing and Networking Authors Hana KimLee, Leonardo Bacciottini, Abhishek Bhatt, Andrew Kille, Stefan Krastanov Published: 12.18.2025 Updated: 12.18.2025 Summary Progress in quantum computing and networking depends on codesign across abstraction layers: device-level noise and heterogeneous hardware, algorithmic structure, and distributed classical control. We present QuantumSavory, an open-source toolkit built to make such end-to-end studies practical by cleanly separating a symbolic computer-algebra frontend from interchangeable numerical simulation backends. States, operations, measurements, and protocol logic are expressed in a backend-agnostic symbolic language; the same model can be executed across multiple backends (e.g., stabilizer, wavefunction, phase-space), enabling rapid exploration of accuracy-performance tradeoffs without rewriting the model. Furthermore, new custom backends can be added via a small, well-defined interface that immediately reuses existing models and protocols. QuantumSavory also addresses the classical-quantum interaction inherent to LOCC protocols via discrete-event execution and a tag/query system for coordination. Tags attach structured classical metadata to quantum registers and message buffers, and queries retrieve, filter, or wait on matching metadata by wildcards or arbitrary predicates. This yields a data-driven control plane where protocol components coordinate by publishing and consuming semantic facts (e.g., resource availability, pairing relationships, protocol outcomes) rather than by maintaining rigid object graphs or bespoke message plumbing, improving composability and reuse as models grow. Our toolkit is also not limited to qubits and Bell pairs; rather, any networking dynamics of any quantum system under any type of multipartite entanglement can be tackled. Lastly, QuantumSavory ships reusable libraries of standard states, circuits, and protocol building blocks with consistent interfaces, enabling full-stack examples to be assembled, modified, and compared with minimal glue code. Source arXiv: 2512.16752v1
Noncooperative Quantum Networks Authors Yanxuan Shao, Jannik L. Wyss, Don Towsley, Adilson E. Motter Published: 12.17.2025 Updated: 12.17.2025 Summary Existing protocols for quantum communication networks usually assume an initial allocation of quantum entanglement resources, which are then manipulated through local operations and classical communication (LOCC) to establish high-fidelity entanglement between distant parties. It is generally held that the resulting fidelity would increase monotonically with the entanglement budget. Here, we show that for noncooperative LOCC protocols, the resulting fidelity may decrease as more entanglement is added to a network with non-pure states. This effect results from a quantum analog of selfish routing and constitutes a potential obstacle to the optimal use of resources in large quantum networks. Source arXiv: 2512.15884v1
Opportunistic Scheduling for Single-downlink Satellite-based Quantum Key Distribution Authors Md Zakir Hossain, Nitish K. Panigrahy, Walter O. Krawec, Don Towsley, Bing Wang Published: 12.14.2025 Updated: 12.14.2025 Summary Satellite-based quantum key distribution (QKD), leveraging low photon loss in free-space quantum communication, is widely regarded as one of the most promising directions to achieve global-scale QKD. With a constellation of satellites and a set of ground stations in a satellite-based QKD system, how to schedule satellites to achieve efficient QKD is an important problem. This problem has been studied in the dual-downlink architecture, where each satellite distributes pairs of entanglements to two ground stations simultaneously. However, it has not been studied in the single downlink architecture, where satellites create keys with each individual ground station, and then serve as trusted nodes to create keys between pairs of ground stations. While the single downlink architecture provides weaker security in that the satellites need to be trusted, it has many advantages, including the potential of achieving significantly higher key rates, and generating keys between pairs of ground stations that are far away from each other and cannot be served using the dual-downlink architecture. In this paper, we propose a novel opportunistic approach for satellite scheduling that accounts for fairness among the ground station pairs, while taking advantage of the dynamic satellite channels to maximize the system performance. We evaluate this approach in a wide range of settings and demonstrate that it provides the best tradeoffs in terms of total and minimum key rates across the ground station pairs. Our evaluation also highlights the importance of considering seasonal effects and cloud coverage in evaluating satellite-based QKD systems. In addition, we show different tradeoffs in global and regional QKD systems. Source arXiv: 2512.12514v1
Hybrid electrostatic-piezo MEMS photonic integrated modulators Authors Thuy-Linh Le, Hardit Singh, Julia M. Boyle, Matthew Zimmermann, Andrew J. Leenheer, Daniel Dominguez, Matt Eichenfield, Mark Dong Published: 12.13.2025 Updated: 12.13.2025 Summary Programmable photonic integrated circuits (PICs) have recently emerged as an important technology for quantum information science and artificial neural networks. In particular, PICs with MEMS-based modulators have the advantages of voltage-based control, ultra-low-energy consumption, cryogenic compatibility, and CMOS-foundry support. Here we report a cantilever optical modulator that utilizes hybrid piezoelectric and electrostatic tuning forces together on a monolithic silicon nitride (SiN) PIC platform. The device achieves actuation of visible-wavelength light with quasi-static tuning up to 10 kHz at 1.5 Vπ-cm as well as high-speed (>20 MHz) AC modulation with dynamically adjustable (25 – 40 MHz) mechanical resonances. We report the physics of how geometric nonlinearities such as capacitive pull-in give rise to suspended and contacted cantilever modes. These reversible operating regimes generate different strain profiles and boundary conditions which are responsible for the active tuning of the mechanical resonances. Our proof-of-concept electrostatic-piezo modulator shows promising potential in large-scale programmable PICs applied to high-speed optical switching and optomechanical sensing. Source arXiv: 2512.12383v1
Programmable Quantum Photonic Interfaces for Quantum Networking Authors Siavash Mirzaei-Ghormish, Mahmoud Jalali Mehrabad, Helaman Flores, Dirk Englund, Ryan M. Camacho Published: 12.10.2025 Updated: 12.10.2025 Summary Quantum networks require interfaces translating memory photons to telecom wavelengths while controlling spatial modes; tasks performed by separate components today. We present a programmable alternative: a structured pump writes a virtual Bragg grating enabling simultaneous spatio-spectral conversion and real-time controlling of emission. Using a LiNbO$_3$ whispering-gallery resonator, we demonstrate 93% spatial coupling and bidirectional conversion between 736,nm and 1347,nm. This reconfigurable interface eliminates cascaded losses and hardware modifications. Source arXiv: 2512.10140v1
Broadband Spatio-Spectral Mode Conversion via Four-Wave Mixing Authors Helaman Flores, Mahmoud Jalali Mehrabad, Siavash Mirzaei-Ghormish, Ryan M. Camacho, Dirk Englund Published: 12.10.2025 Updated: 12.10.2025 Summary We introduce a framework for scalable and broadband free-space phase-matched four-wave mixing in ring resonators. This method for four-wave mixing reduces the complexity of coupling an emitter to a quantum network by combining the spatial and spectral interfaces between them into one nonlinear optical process. The device is compliant with current heterogeneous integration capabilities and has a bandwidth of 165 nm for efficient spatio-spectral conversion. We outline a fabrication-ready diamond-on-insulator pathway towards modular unit cells that natively bridge visible color centers to the infrared spectrum for scalable quantum networks. We also present and analyze an end-to-end framework for considering single-photon coupling efficiency from a color center to a quantum network. This framework represents a step forwards in analyzing and reducing system-scale losses in a spin-photon interface. Source arXiv: 2512.10045v1
Dressed-state Hamiltonian engineering in a strongly interacting solid-state spin ensemble Authors Haoyang Gao, Nathaniel T. Leitao, Siddharth Dandavate, Lillian B. Hughes Wyatt, Piotr Put, Mathew Mammen, Leigh S. Martin, Hongkun Park, Ania C. Bleszynski Jayich, Mikhail D. Lukin Published: 12.09.2025 Updated: 12.09.2025 Summary In quantum science applications, ranging from many-body physics to quantum metrology, dipolar interactions in spin ensembles are controlled via Floquet engineering. However, this technique typically reduces the interaction strength between spins, and effectively weakens the coupling to a target sensing field, limiting the metrological sensitivity. In this work, we develop and demonstrate a method for direct tuning of the native interaction in an ensemble of nitrogen-vacancy (NV) centers in diamond. Our approach utilizes dressed-state qubit encoding under a magnetic field perpendicular to the crystal lattice orientation. This method leads to a $3.2times$ enhancement of the dimensionless coherence parameter $JT_2$ compared to state-of-the-art Floquet engineering, and a $2.6times$ ($8.3~$dB) enhanced sensitivity in AC magnetometry. Utilizing the extended coherence we experimentally probe spin transport at intermediate to late times. Our results provide a powerful Hamiltonian engineering tool for future studies with NV ensembles and other interacting higher-spin ($S>frac{1}{2}$) systems. Source arXiv: 2512.09043v1
Scheduling in Quantum Satellite Networks: Fairness and Performance Optimization Authors Ashutosh Jayant Dikshit, Naga Lakshmi Anipeddi, Prajit Dhara, Saikat Guha, Deirdre Kilbane, Leandros Tassiulas, Don Towsley, Nitish K. Panigrahy Published: 12.08.2025 Updated: 12.15.2025 Summary Quantum satellite networks offer a promising solution for achieving long-distance quantum communication by enabling entanglement distribution across global scales. This work formulates and solves the quantum satellite network scheduling problem by optimizing satellite-to-ground station pair assignments under realistic system and environmental constraints. Our framework accounts for limited satellite and ground station resources, fairness, entanglement fidelity thresholds, and real world non-idealities including atmospheric losses, weather and background noise. In addition, we incorporate the complexities of multi-satellite relays enabled via inter-satellite links. We propose an integer linear programming (ILP) based optimization framework that supports multiple scheduling objectives, allowing us to analyze tradeoffs between maximizing total entanglement distribution rate and ensuring fairness across ground station pairs. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies. Source arXiv: 2512.07108v2
Scheduling in Quantum Satellite Networks: Fairness and Performance Optimization Authors Ashutosh Jayant Dikshit, Naga Lakshmi Anipeddi, Prajit Dhara, Saikat Guha, Deirdre Kilbane, Leandros Tassiulas, Don Towsley, Nitish K. Panigrahy Published: 12.08.2025 Updated: 12.08.2025 Summary Quantum satellite networks offer a promising solution for achieving long-distance quantum communication by enabling entanglement distribution across global scales. This work formulates and solves the quantum satellite network scheduling problem by optimizing satellite-to-ground station pair assignments under realistic system and environmental constraints. Our framework accounts for limited satellite and ground station resources, fairness, entanglement fidelity thresholds, and real world non-idealities including atmospheric losses, weather and background noise. In addition, we incorporate the complexities of multi-satellite relays enabled via inter-satellite links. We propose an integer linear programming (ILP) based optimization framework that supports multiple scheduling objectives, allowing us to analyze tradeoffs between maximizing total entanglement distribution rate and ensuring fairness across ground station pairs. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies. Source arXiv: 2512.07108v1
HiMoE-VLA: Hierarchical Mixture-of-Experts for Generalist Vision-Language-Action Policies Authors Zhiying Du, Bei Liu, Yaobo Liang, Yichao Shen, Haidong Cao, Xiangyu Zheng, Zhiyuan Feng, Zuxuan Wu, Jiaolong Yang, Yu-Gang Jiang Published: 12.05.2025 Updated: 12.05.2025 Summary The development of foundation models for embodied intelligence critically depends on access to large-scale, high-quality robot demonstration data. Recent approaches have sought to address this challenge by training on large collections of heterogeneous robotic datasets. However, unlike vision or language data, robotic demonstrations exhibit substantial heterogeneity across embodiments and action spaces as well as other prominent variations such as senor configurations and action control frequencies. The lack of explicit designs for handling such heterogeneity causes existing methods to struggle with integrating diverse factors, thereby limiting their generalization and leading to degraded performance when transferred to new settings. In this paper, we present HiMoE-VLA, a novel vision-language-action (VLA) framework tailored to effectively handle diverse robotic data with heterogeneity. Specifically, we introduce a Hierarchical Mixture-of-Experts (HiMoE) architecture for the action module which adaptively handles multiple sources of heterogeneity across layers and gradually abstracts them into shared knowledge representations. Through extensive experimentation with simulation benchmarks and real-world robotic platforms, HiMoE-VLA demonstrates a consistent performance boost over existing VLA baselines, achieving higher accuracy and robust generalization across diverse robots and action spaces. The code and models are publicly available at https://github.com/ZhiyingDu/HiMoE-VLA. Source arXiv: 2512.05693v1
ProPhy: Progressive Physical Alignment for Dynamic World Simulation Authors Zijun Wang, Panwen Hu, Jing Wang, Terry Jingchen Zhang, Yuhao Cheng, Long Chen, Yiqiang Yan, Zutao Jiang, Hanhui Li, Xiaodan Liang Published: 12.05.2025 Updated: 12.05.2025 Summary Recent advances in video generation have shown remarkable potential for constructing world simulators. However, current models still struggle to produce physically consistent results, particularly when handling large-scale or complex dynamics. This limitation arises primarily because existing approaches respond isotropically to physical prompts and neglect the fine-grained alignment between generated content and localized physical cues. To address these challenges, we propose ProPhy, a Progressive Physical Alignment Framework that enables explicit physics-aware conditioning and anisotropic generation. ProPhy employs a two-stage Mixture-of-Physics-Experts (MoPE) mechanism for discriminative physical prior extraction, where Semantic Experts infer semantic-level physical principles from textual descriptions, and Refinement Experts capture token-level physical dynamics. This mechanism allows the model to learn fine-grained, physics-aware video representations that better reflect underlying physical laws. Furthermore, we introduce a physical alignment strategy that transfers the physical reasoning capabilities of vision-language models (VLMs) into the Refinement Experts, facilitating a more accurate representation of dynamic physical phenomena. Extensive experiments on physics-aware video generation benchmarks demonstrate that ProPhy produces more realistic, dynamic, and physically coherent results than existing state-of-the-art methods. Source arXiv: 2512.05564v1
Little red dot variability over a century reveals black hole envelope via a giant Einstein cross Authors Zijian Zhang, Mingyu Li, Masamune Oguri, Xiaojing Lin, Kohei Inayoshi, Catherine Cerny, Dan Coe, Jose M. Diego, Seiji Fujimoto, Linhua Jiang, Guillaume Mahler, Jorryt Matthee, Rohan P. Naidu, Keren Sharon, Yue Shen, Adi Zitrin, Abdurro'uf, Hollis Akins, Joseph F. V. Allingham, Ricardo Amorín, Yoshihisa Asada, Hakim Atek, Franz E. Bauer, Maruša Bradač, Larry D. Bradley, Zheng Cai, Sebastiano Cantalupo, Christopher Conselice, Liang Dai, Pratika Dayal, Eiichi Egami, Daniel J. Eisenstein, Andreas L. Faisst, Xiaohui Fan, Qinyue Fei, Brenda L. Frye, Yoshinobu Fudamoto, Lukas J. Furtak, Miriam Golubchik, Mauro González-Otero, Yuichi Harikane, Tiger Yu-Yang Hsiao, Yolanda Jiménez-Teja, Jeyhan S. Kartaltepe, Tomokazu Kiyota, Anton M. Koekemoer, Kotaro Kohno, Vasily Kokorev, Nimisha Kumari, Ivo Labbe, Claudia D. P. Lagos, Conor Larison, Yongming Liang, Ray A. Lucas, Jianwei Lyu, Nicholas S. Martis, Georgios E. Magdis, Matteo Messa, Minami Nakane, Gaël Noirot, Rafael Ortiz, Masami Ouchi, Justin D. R. Pierel, Marc Postman, Naveen Reddy, Massimo Ricotti, Daniel Schaerer, Raffaella Schneider, Charles C. Steidel, Wei Leong Tee, Roberta Tripodi, James A. A. Trussler, Hiroya Umeda, Francesco Valentino, Eros Vanzella, Feige Wang, Rogier Windhorst, Yunjing Wu, Zihao Wu, Hiroto Yanagisawa, Jinyi Yang, Fengwu Sun Published: 12.04.2025 Updated: 12.04.2025 Summary “Little red dots” (LRDs) represent a new population of astronomical objects uncovered by JWST whose nature remains debated. Although many LRDs are suspected as active galactic nuclei (AGN), they show little variability on days-years timescales. We report the discovery of two gravitationally lensed LRDs at redshift $sim$4.3 behind the cluster RXCJ2211-0350, one of which (RX1) is quadruply imaged with time delays spanning $sim$130 years. RX1 exhibits intrinsic color and brightness variations of up to 0.7 magnitude among its images. These changes are consistent with blackbody-temperature variations of a photosphere, indicating long-term variability analogous to Cepheid-like pulsations but in a far more extended ($R sim 2000$ AU) and massive ($M gtrsim 10^6 , M_{odot}$) systems. These results suggest LRDs as a distinct class of AGN with stellar-like envelopes. Source arXiv: 2512.05180v1
Measurement of the branching fractions and longitudinal polarisations of $B^0_{(s)} to K^{*0} kern 0.18em overline{kern -0.18em K}{}^{*0}$ decays Authors LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, A. Artamonov, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J. A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P. B. Battista, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. A. Behling, S. Belin, A. Bellavista, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, J. Bhom, M. S. Bieker, N. V. Biesuz, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. Bolognani, R. Bolzonella, R. B. Bonacci, N. Bondar, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, E. Bottalico, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, J. D. Brandenburg, A. Brea Rodriguez, N. Breer, J. Brodzicka, J. Brown, D. Brundu, E. Buchanan, M. Burgos Marcos, A. T. Burke, C. Burr, C. Buti, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, S. Calderon Ramirez, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, A. F. Campoverde Quezada, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A. J. Chadwick, I. Chahrour, H. Chang, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, A. Chernov, S. Chernyshenko, X. Chiotopoulos, V. Chobanova, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, X. Dai, E. Dall'Occo, J. Dalseno, C. D'Ambrosio, J. Daniel, G. Darze, A. Davidson, J. E. Davies, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, H. -P. Dembinski, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, Y. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, A. Doheny, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, J. Eschle, S. Esen, T. Evans, F. Fabiano, S. Faghih, L. N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, L. A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Führing, T. Fulghesu, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, Y. Gao, Y. Gao, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M. A. Giza, F. C. Glaser, V. V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, D. Golubkov, A. Golutvin, S. Gomez Fernandez, W. Gomulka, I. Gonçales Vaz, F. Goncalves Abrantes, M. Goncerz, G. Gong, J. A. Gooding, I. V. Gorelov, C. Gotti, E. Govorkova, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, N. A. Grieser, L. Grillo, S. Gromov, C. Gu, M. Guarise, L. Guerry, A. -K. Guseinov, E. Gushchin, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P. M. Hamilton, J. Hammerich, Q. Han, X. Han, S. Hansmann-Menzemer, L. Hao, N. Harnew, T. H. Harris, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, L. Henry, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D. C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R. J. Hunter, M. Hushchyn, D. Hutchcroft, M. Idzik, D. Ilin, P. Ilten, A. Iniukhin, A. Iohner, A. Ishteev, K. Ivshin, H. Jage, S. J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, B. K. Jashal, A. Jawahery, C. Jayaweera, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, A. Kharisova, S. Kholodenko, G. Khreich, T. Kirn, V. S. Kirsebom, S. Klaver, N. Kleijne, A. Kleimenova, D. K. Klekots, K. Klimaszewski, M. R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, M. Korolev, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, A. Kozachuk, P. Kravchenko, L. Kravchuk, O. Kravcov, M. Kreps, P. Krokovny, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, V. Kudryavtsev, E. Kulikova, A. Kupsc, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, G. Lanfranchi, C. Langenbruch, J. Langer, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, A. Leflat, S. Legotin, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, M. Li, P. Li, P. -R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, C. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. Liu, Y. L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F. C. L. Lopes, J. H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, S. López Soliño, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, E. Luppi, K. Lynch, X. -R. Lyu, G. M. Ma, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, D. Magdalinski, D. Maisuzenko, J. J. Malczewski, S. Malde, L. Malentacca, A. Malinin, T. Maltsev, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, F. M. Manganella, D. Manuzzi, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, V. Matiunin, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, M. McCann, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, G. Meier, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R. D. Moise, E. F. Molina Cardenas, T. Mombächer, M. Monk, T. Monnard, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. Moro, J. Moron, W. Morren, A. B. Morris, A. G. Morris, R. Mountain, Z. M. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, G. Napoletano, I. Nasteva, M. Needham, E. Nekrasova, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, L. Nisi, Q. Niu, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, A. Okhotnikov, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R. H. O'Neil, J. S. Ordonez Soto, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, G. Panshin, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, D. Pereima, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M. J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, G. Pietrzyk, R. N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V. M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, N. Polukhina, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, S. Poslavskii, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J. R. Pybus, Q. Q. Qian, W. Qian, N. Qin, R. Quagliani, R. I. Rabadan Trejo, R. Racz, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogachev, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, S. R. Roy, M. S. Rudolph, M. Ruiz Diaz, R. A. Ruiz Fernandez, J. Ruiz Vidal, J. J. Saavedra-Arias, J. J. Saborido Silva, S. E. R. Sacha Emile R., N. Sagidova, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, D. Saranin, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, D. Savrina, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M. H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, A. Semennikov, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, A. Seuthe, B. Sevilla Sanjuan, Y. Shang, D. M. Shangase, M. Shapkin, R. S. Sharma, I. Shchemerov, L. Shchutska, T. Shears, L. Shekhtman, Z. Shen, S. Sheng, V. Shevchenko, B. Shi, Q. Shi, W. S. Shi, Y. Shimizu, E. Shmanin, R. Shorkin, J. D. Shupperd, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, N. Skidmore, T. Skwarnicki, M. W. Slater, E. Smith, K. Smith, M. Smith, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, A. Solovev, K. Solovieva, N. S. Sommerfeld, R. Song, Y. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, K. M. Sowa, E. Spadaro Norella, E. Spedicato, J. G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, E. N. Stein, O. Steinkamp, D. Strekalina, Y. Su, F. Suljik, J. Sun, J. Sun, L. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y. T. Tang, M. D. Tat, J. A. Teijeiro Jimenez, A. Terentev, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, A. R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T. T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, D. Torres Machado, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, D. J. Unverzagt, A. Upadhyay, B. Urbach, A. Usachov, A. Ustyuzhanin, U. Uwer, V. Vagnoni, A. Vaitkevicius, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F. C. Volle, D. vom Bruch, N. Voropaev, K. Vos, C. Vrahas, J. Wagner, J. Walsh, E. J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. Wang, X. W. Wang, Y. Wang, Y. Wang, Y. H. Wang, Z. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, M. A. Wiegertjes, C. Wild, G. Wilkinson, M. K. Wilkinson, M. Williams, M. J. Williams, M. R. J. Williams, R. Williams, S. Williams, Z. Williams, F. F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S. A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T. X. Xing, A. Xu, L. Xu, M. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, X. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, J. A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, R. Zhang, S. Zhang, S. L. Zhang, Y. Zhang, Y. Z. Zhang, Z. Zhang, Y. Zhao, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, Q. Zou, D. Zuliani, G. Zunica Published: 12.04.2025 Updated: 12.04.2025 Summary A time- and flavour-integrated amplitude analysis of $B^0$ and $B^0_s$ decays to the $(K^+π^-)(K^-π^+)$ final state in the $K^*(892)^0 kern 0.18em overline{kern -0.18em K}{}^{*}(892)^0$ region is presented, using $pp$ collision data recorded with the LHCb detector in 2011–2018, corresponding to an integrated luminosity of $9,text{fb}^{-1}$. The branching fractions of the $B^0$ and $B^0_s$ decays are measured relative to the $B^0 to D^-π^+$ and $B^0_s to D^-_s π^+$ modes, respectively. The corresponding longitudinal polarisation fractions are found to be $f_L^{d} = 0.600 pm 0.022 pm 0.017$ and $f_L^{s} = 0.159 pm 0.010 pm 0.007$, where the uncertainties are statistical and systematic, respectively. The theory-motivated $L_{K^{*0} kern 0.18em overline{kern -0.18em K}{}^{*0}}$ observable is found to be $L_{K^{*0} kern 0.18em overline{kern -0.18em K}{}^{*0}} = 4.92 pm 0.55 pm 0.47 pm 0.02 pm 0.10$, where the uncertainties are statistical, systematic, due to uncertainty of external mass and lifetime measurements, and due to knowledge of the fragmentation fraction ratio, respectively. This confirms the previously reported tension between experimental determinations and theoretical predictions of longitudinal polarisation in $B to VV$ decays. Source arXiv: 2512.05102v1
Pump Free Microwave-Optical Quantum Transduction Authors Fangxin Li, Jaesung Heo, Zhaoyou Wang, Andrew P. Higginbotham, Alexander A. High, Liang Jiang Published: 12.04.2025 Updated: 12.04.2025 Summary Distributed quantum computing involves superconducting computation nodes operating at microwave frequencies, which are connected by long-distance transmission lines that transmit photons at optical frequencies. Quantum transduction, which coherently converts between microwave and optical (M-O) photons, is a critical component of such an architecture. Current approaches are hindered by the unavoidable problem of device heating due to the optical pump. In this work, we propose a pump-free scheme based on color centers that generates time-bin encoded M-O Bell pairs. Our scheme first creates spin-photon entanglement and then converts the spin state into a time-bin-encoded microwave photon using a strongly coupled Purcell-enhanced resonator. In our protocol, the microwave retrieval is heralded by detecting the microwave signal with a three-level transmon. We have analyzed the resulting Bell state fidelity and generation probability of this protocol. Our simulation shows that by combining a state-of-the-art spin-optical interface with our proposed strongly-coupled spin-microwave design, the pump-free scheme can generate M-O Bell pairs at a heralding rate exceeding one kilohertz with near-unity fidelity, which establishes the scheme as a promising source for M-O Bell pairs. Source arXiv: 2512.05096v1
Evidence for the semileptonic decays $Λ_c^{+} to Σ^{pm} π^{mp} e^+ ν_e$ Authors BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, Z. K. Chen, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Y. N. Gao, Y. Y. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. J. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, C. K. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, K. L. Li, L. J. Li, Lei Li, M. H. Li, M. R. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. G. Li, Y. P. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, L. Q. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, W. T. Liu, X. Liu, X. Liu, X. Y. Liu, Y. Liu, Y. Liu, Yuan Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, F. M. Melendi, Y. H. Meng, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. R. Qi, M. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, J. L. Shi, J. Y. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, T. Z. Song, W. M. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, L. F. Tang, M. Tang, Y. A. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, B. Wang, B. Wang, Bo Wang, C. Wang, Cong Wang, D. Y. Wang, H. J. Wang, J. J. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, M. Wang, N. Y. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, Wei Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. L. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, D. H. Wei, H. R. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. Wu, X. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, H. Y. Yan, L. Yan, W. B. Yan, W. C. Yan, W. P. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, R. J. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. H. Yang, Y. Q. Yang, Y. X. Yang, Y. Z. Yang, M. Ye, M. H. Ye, Z. J. Ye, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, H. Yuan, J. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, S. H. Zeng, X. Zeng, Y. Zeng, Yujie Zeng, Y. J. Zeng, X. Y. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, G. Y. Zhang, H. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, Jin Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Z. Zhang, Zh. Zh. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Q. Zhou, J. Y. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, J. H. Zou, J. Zu Published: 12.04.2025 Updated: 12.04.2025 Summary Using $4.5, fb^{-1}$ of $e^+e^-$ collision data collected by the BESIII detector at center-of-mass energies between $4.600$ and $4.699,GeV$, we search for the semileptonic decays $Λ_c^{+} to Σ^{+} π^{-} e^+ ν_e$ and $Λ_c^{+} to Σ^{-} π^{+} e^+ ν_e$ for the first time. Assuming their branching fractions are equal under isospin symmetry, evidence for $Λ_c^{+} to Σ^{pm} π^{mp} e^+ ν_e$ is reported with a significance of $3.6σ$. The corresponding branching fraction is measured to be $mathcal{B}(Λ_c^{+} to Σ^pmπ^mp e^+ν_e) = (7.7^{+2.5}_{-2.3_{rm stat.}}pm1.3_{rm syst.})times 10^{-4}$, which is consistent with quark model predictions within two standard deviations. Source arXiv: 2512.05178v1
Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction Authors Nex-AGI Team, :, Yuxuan Cai, Lu Chen, Qiaoling Chen, Yuyang Ding, Liwen Fan, Wenjie Fu, Yufei Gao, Honglin Guo, Pinxue Guo, Zhenhua Han, Zhengfu He, Hanglei Hu, Kai Hu, Shengjia Hua, Tianyu Huai, Baodai Huang, Li Ji, Zhen Jiang, Zhikai Lei, Bufan Li, Jiahang Lin, Lizhi Lin, Jinxiu Liu, Shichun Liu, Ziming Liu, Yuchen Ni, Pengfang Qian, Yujiong Shen, Qingyun Shi, Wentao Shu, Peng Sun, Yiran Suo, Tian Tang, Boyu Tian, Guoteng Wang, Junzhe Wang, Peixin Wang, Zhiheng Xi, Hang Yan, Jie Yang, Zhixiong Yang, Tianchu Yao, Guangze Ye, Qianxi Yu, Shuo Zhang, Xinyue Zhang, Yiqi Zhang, Jiarong Zhao, Miao Zheng, Rui Zheng, Enyu Zhou, Jiazheng Zhou, Maosen Zhou, Yuhao Zhou, Tao Gui, Yining Zheng, Xinchi Chen, Jie Zhou, Siyuan Feng, Qin Chen, Liang He, Qi Zhang, Xuanjing Huang, Xipeng Qiu Published: 12.04.2025 Updated: 12.04.2025 Summary The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms — from static imitation to incentive-driven decision making. However, this transition is significantly impeded by the lack of scalable infrastructure capable of constructing high-quality interaction signals for effective policy learning. To address this, we introduce a comprehensive method designed to systematically scale the diversity and complexity of interactive environments. Our method realizes this scaling by addressing three orthogonal dimensions: (1) Complexity: NexAU, a flexible agent framework that supports building complex agent hierarchies via simple configurations; (2) Diversity: NexA4A automatically generates diverse agent hierarchies from natural language to cover infinite domains; and (3) Fidelity: NexGAP bridges the simulation-reality gap by integrating dynamic real-world environment for grounded trajectories synthesis. We train Nex-N1 upon the diverse and complex interactive environments established by our infrastructure. Empirical results on benchmarks such as SWE-bench and tau2 demonstrate that Nex-N1 consistently outperforms SOTA open-source models and achieves competitive performance against frontier proprietary models on complex agentic tasks. We open-source the Nex ecosystem and model weights to facilitate further research. Source arXiv: 2512.04987v1
Study of the reaction $Ξ^{0}nrightarrowΛΛX$ using $Ξ^{0}$-nucleus scattering Authors BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, X. Y. Chen, Y. B. Chen, Y. Q. Chen, Y. Q. Chen, Z. Chen, Z. J. Chen, Z. K. Chen, J. C. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. Cottee-Meldrum, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, L. Feng, Q. X. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. Gao, Y. N. Gao, Y. N. Gao, Y. Y. Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. J. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, C. K. Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, K. L. Li, L. J. Li, Lei Li, M. H. Li, M. R. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. X. Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. G. Li, Y. P. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, L. Q. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Liu, K. Y. Liu, Ke Liu, L. C. Liu, Lu Liu, M. H. Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, W. T. Liu, X. Liu, X. Liu, X. K. Liu, X. L. Liu, X. Y. Liu, Y. Liu, Y. Liu, Yuan Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, F. C. Ma, H. L. Ma, Heng Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Q. A. Malik, H. X. Mao, Y. J. Mao, Z. P. Mao, S. Marcello, A. Marshall, F. M. Melendi, Y. H. Meng, Z. X. Meng, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, C. Normand, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, X. J. Peng, Y. Y. Peng, K. Peters, K. Petridis, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. R. Qi, M. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, J. Rademacker, K. Ravindran, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, J. L. Shi, J. Y. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, Zirong Song, S. Sosio, S. Spataro, S. Stansilaus, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, J. J. Tang, L. F. Tang, Y. A. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, B. Wang, B. Wang, Bo Wang, C. Wang, C. Wang, Cong Wang, D. Y. Wang, H. J. Wang, J. J. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, M. Wang, N. Y. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, Wei Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. J. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. L. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, D. H. Wei, H. R. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. Wu, X. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, T. D. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, H. Y. Yan, L. Yan, W. B. Yan, W. C. Yan, W. H. Yan, W. P. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, R. J. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. H. Yang, Y. Q. Yang, Y. X. Yang, Y. Z. Yang, M. Ye, M. H. Ye, Z. J. Ye, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, L. Q. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, H. Yuan, J. Yuan, J. Yuan, L. Yuan, S. C. Yuan, S. H. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, S. H. Zeng, X. Zeng, Y. Zeng, Yujie Zeng, Y. J. Zeng, X. Y. Zhai, Y. H. Zhan, Shunan Zhang, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, G. Y. Zhang, H. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, Jin Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Y. P. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Z. Zhang, Zh. Zh. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, L. Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, H. Zhou, J. Q. Zhou, J. Y. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. X. Zhou, Y. Z. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, J. H. Zou, J. Zu Published: 12.04.2025 Updated: 12.04.2025 Summary Using $(10087pm44)times10^{6}$$J/ψ$ events collected with the BESIII detector operating at the BEPCII storage ring in $2009$, $2012$, $2018$, and $2019$, we perform a search for the reaction $Ξ^0nrightarrowΛΛX$, where $X$ denotes any additional final particles. Given the highly suppressed phase space for producing extra pions, the $X$ consists of either nothing or a photon, corresponding to the processes $Ξ^0 n rightarrow ΛΛ$ and $Ξ^{0}nrightarrowΛΣ^0rightarrowΛΛγ$. The $Ξ^0$ comes from the decay of $J/ψrightarrowΞ^0barΞ^0$, while the neutron originates from material of the beam pipe. A signal is observed for the first time with a statistical significance of 6.4$σ$. The cross section for the reaction $Ξ^0+{^9rm{Be}}rightarrowΛ+Λ+X$ is measured to be $(43.6pm10.5_{text{stat}}pm11.1_{text{syst}})$ mb at $P_{Ξ^0}approx0.818$ GeV/$c$, where the first uncertainty is statistical and the second systematic. No significant $H$-dibaryon signal is observed in the $ΛΛ$ final state. Source arXiv: 2512.04701v1
FLEX: Leveraging FPGA-CPU Synergy for Mixed-Cell-Height Legalization Acceleration Authors Xingyu Liu, Jiawei Liang, Linfeng Du, Yipu Zhang, Chaofang Ma, Hanwei Fan, Jiang Xu, Wei Zhang Published: 12.04.2025 Updated: 12.04.2025 Summary In this work, we present FLEX, an FPGA-CPU accelerator for mixed-cell-height legalization tasks. We address challenges from the following perspectives. First, we optimize the task assignment strategy and perform an efficient task partition between FPGA and CPU to exploit their complementary strengths. Second, a multi-granularity pipelining technique is employed to accelerate the most time-consuming step, finding optimal placement position (FOP), in legalization. At last, we particularly target the computationally intensive cell shifting process in FOP, optimizing the design to align it seamlessly with the multi-granularity pipelining framework for further speedup. Experimental results show that FLEX achieves up to 18.3x and 5.4x speedups compared to state-of-the-art CPU-GPU and multi-threaded CPU legalizers with better scalability, while improving legalization quality by 4% and 1%. Source arXiv: 2512.04527v1
SmartAlert: Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Lab Utilization Reduction Authors April S. Liang, Fatemeh Amrollahi, Yixing Jiang, Conor K. Corbin, Grace Y. E. Kim, David Mui, Trevor Crowell, Aakash Acharya, Sreedevi Mony, Soumya Punnathanam, Jack McKeown, Margaret Smith, Steven Lin, Arnold Milstein, Kevin Schulman, Jason Hom, Michael A. Pfeffer, Tho D. Pham, David Svec, Weihan Chu, Lisa Shieh, Christopher Sharp, Stephen P. Ma, Jonathan H. Chen Published: 12.04.2025 Updated: 12.04.2025 Summary Repetitive laboratory testing unlikely to yield clinically useful information is a common practice that burdens patients and increases healthcare costs. Education and feedback interventions have limited success, while general test ordering restrictions and electronic alerts impede appropriate clinical care. We introduce and evaluate SmartAlert, a machine learning (ML)-driven clinical decision support (CDS) system integrated into the electronic health record that predicts stable laboratory results to reduce unnecessary repeat testing. This case study describes the implementation process, challenges, and lessons learned from deploying SmartAlert targeting complete blood count (CBC) utilization in a randomized controlled pilot across 9270 admissions in eight acute care units across two hospitals between August 15, 2024, and March 15, 2025. Results show significant decrease in number of CBC results within 52 hours of SmartAlert display (1.54 vs 1.82, p <0.01) without adverse effect on secondary safety outcomes, representing a 15% relative reduction in repetitive testing. Implementation lessons learned include interpretation of probabilistic model predictions in clinical contexts, stakeholder engagement to define acceptable model behavior, governance processes for deploying a complex model in a clinical environment, user interface design considerations, alignment with clinical operational priorities, and the value of qualitative feedback from end users. In conclusion, a machine learning-driven CDS system backed by a deliberate implementation and governance process can provide precision guidance on inpatient laboratory testing to safely reduce unnecessary repetitive testing. Source arXiv: 2512.04354v1
Measurement of the hyperon weak radiative decay $Ξ^0toγΣ^0$ at BESIII Authors BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, M. R. An, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, T. T. Chang, W. L. Chang, G. R. Che, G. Chelkov, C. Chen, Chao Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, W. S. Cheng, S. K. Choi, X. Chu, G. Cibinetto, S. C. Coen, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, K Fischer, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, W. Y. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, N Hüsken, N. in der Wiesche, M. Irshad, J. Jackson, S. Jaeger, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, H. J. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, T. Johansson, X. K., S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, A. Khoukaz, R. Kiuchi, R. Kliemt, O. B. Kolcu, B. Kopf, M. Kuessner, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, A. Lavania, L. Lavezzi, T. T. Lei, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, J. W. Li, K. L. Li, Ke Li, L. J Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. X. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Xiaoyu Li, Y. G. Li, Z. J. Li, Z. X. Li, C. Liang, H. Liang, H. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, Y. P. Liao, J. Libby, A. Limphirat, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. L. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, X. Liu, Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. J. Qin, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, R. S. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. Sun, Y. J. Sun, Y. Z. Sun, Z. T. Sun, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, C. W. Wang, D. Y. Wang, F. Wang, H. J. Wang, H. P. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, Meng Wang, S. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. Wei, D. H. Wei, F. Weidner, S. P. Wen, C. W. Wenzel, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, C. Z. Yuan, L. Yuan, S. C. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. H. Zhang, H. Q. Zhang, H. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. M. Zhang, L. Q. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Xuyan Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Yan Zhang, Yao Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, Z. Y. Zhang, G. Zhao, J. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, L. P. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, J. Zu Published: 12.03.2025 Updated: 12.03.2025 Summary The hyperon weak radiative decay $Ξ^0toγΣ^0$ is measured via the process $J/ψtoΞ^0barΞ^0$. The absolute branching fraction of $Ξ^0toγΣ^0$ is determined to be $(3.69pm 0.21_{text{stat}}pm0.12_{text{syst}})times 10^{-3}$, based on $(10.087pm 0.044)times 10^{9}$ $J/ψ$ events collected with the BESIII detector operating at the BEPCII collider. The decay asymmetry parameter is measured, with a complete angular analysis of the cascade decay chain, to be $α_γ = -0.807pm 0.095_{text{stat}}pm 0.011_{text{syst}}$. Source arXiv: 2512.03877v1
A Comprehensive Survey of 3GPP Release 19 ISAC Channel Modeling: From Empirical Features to Unified Methodology and Standardized Simulator Authors Yameng Liu, Yuxiang Zhang, Jianhua Zhang, Yuanpeng Pei, Changsheng Zhao, Shilin Luo, Lei Tian, Yingyang Li, Wei Hong, Jianming Wu, Guangyi Liu, Yan Li, Tao Jiang, Chuangxin Jiang, Junchen Liu, Yongqiang Fei, Woo-Suk Ko, Jing Xu, Bin Liang, Takahiro Tomie Published: 12.03.2025 Updated: 12.03.2025 Summary Integrated Sensing and Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP. Channel measurement and modeling is a prerequisite for ISAC system design and has attracted widespread attention from both academia and industry. 3GPP Release 19 initiated the ISAC channel study item in December 2023 and finalized its modeling specification in May 2025 after extensive technical discussions. However, a comprehensive survey that provides a systematic overview,from empirical channel features to modeling methodologies and standardized simulators,remains unavailable. In this paper, the key requirements and challenges in ISAC channel research are first analyzed, followed by a structured overview of the standardization workflow throughout the 3GPP Release 19 process. Then, critical aspects of ISAC channels, including physical objects, target channels, and background channels, are examined in depth, together with additional features such as spatial consistency, environment objects, Doppler characteristics, and shared clusters, supported by measurement-based analysis. To establish a unified ISAC channel modeling framework, an Extended Geometry-based Stochastic Model (E-GBSM) is proposed, incorporating all the aforementioned ISAC channel characteristics. Finally, a standardized simulator is developed based on E-GBSM, and a two-phase calibration procedure aligned with 3GPP Release 19 is conducted to validate both the model and the simulator, demonstrating close agreement with industrial reference results. Overall, this paper provides a systematic survey of 3GPP Release 19 ISAC channel standardization and offers insights into best practices for new feature characterization, unified modeling methodology, and standardized simulator implementation, which can effectively supporting ISAC technology evaluation and future 6G standardization. Source arXiv: 2512.03506v1
Tunable giant Purcell enhancement of quantum light emitters by means of acoustic graphene plasmons Authors Justin Gruber, Mahtab A. Khan, Dirk R. Englund, Michael N. Leuenberger Published: 12.02.2025 Updated: 12.02.2025 Summary Inspired by the remarkable ability of plasmons to boost radiative emission rates, we propose leveraging acoustic graphene plasmons (AGPs) to realize tunable, giant Purcell enhancements for single-photon, entangled-photon, and multipolar quantum emitters. These AGPs are localized inside a cavity defined by a graphene sheet and a metallic nanocube and filled with a dielectric of thickness of a few nanometers and consisting of stacked layers of 2D materials, containing impurities or defects that act as quantum light emitters. Through finite-difference time domain (FDTD) calculations, we show that this geometry can achieve giant Purcell enhancement factors over a large portion of the infrared (IR) spectrum, up to 6 orders of magnitude in the mid-IR and up to 4 orders of magnitude at telecommunications wavelengths, reaching quantum efficiencies of 95% and 89%, respectively, with high-mobility graphene. We obtain Purcell enhancement factors for single-photon electric dipole (E1), electric quadrupole (E2), and electric octupole (E3) transitions and two-photon spontaneous emission (2PSE) transitions, of the orders of $10^{4}$, $10^{7}$, $10^{9}$, and $10^9$, respectively, and a quantum efficiency of 79% for entangled-photon emission with high-mobility graphene at a wavelength of $λ=1.55$ $μ$m. Importantly, AGP mode frequencies depend on the graphene Fermi energy, which can be tuned via electrostatic gating to modulate fluorescence enhancement in real time. As an example, we consider the Purcell enhancement of spontaneous single- and two-photon emissions from an erbium atom inside single-layer (SL) WS$_2$. Our results could be useful for electrically tunable quantum emitter devices with applications in quantum communication and quantum information processing. Source arXiv: 2512.02907v1
DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models Authors DeepSeek-AI, Aixin Liu, Aoxue Mei, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Chaofan Lin, Chen Dong, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenhao Xu, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Erhang Li, Fangqi Zhou, Fangyun Lin, Fucong Dai, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Hanwei Xu, Hao Li, Haofen Liang, Haoran Wei, Haowei Zhang, Haowen Luo, Haozhe Ji, Honghui Ding, Hongxuan Tang, Huanqi Cao, Huazuo Gao, Hui Qu, Hui Zeng, Jialiang Huang, Jiashi Li, Jiaxin Xu, Jiewen Hu, Jingchang Chen, Jingting Xiang, Jingyang Yuan, Jingyuan Cheng, Jinhua Zhu, Jun Ran, Junguang Jiang, Junjie Qiu, Junlong Li, Junxiao Song, Kai Dong, Kaige Gao, Kang Guan, Kexin Huang, Kexing Zhou, Kezhao Huang, Kuai Yu, Lean Wang, Lecong Zhang, Lei Wang, Liang Zhao, Liangsheng Yin, Lihua Guo, Lingxiao Luo, Linwang Ma, Litong Wang, Liyue Zhang, M. S. Di, M. Y Xu, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingxu Zhou, Panpan Huang, Peixin Cong, Peiyi Wang, Qiancheng Wang, Qihao Zhu, Qingyang Li, Qinyu Chen, Qiushi Du, Ruiling Xu, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, Runqiu Yin, Runxin Xu, Ruomeng Shen, Ruoyu Zhang, S. H. Liu, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaofei Cai, Shaoyuan Chen, Shengding Hu, Shengyu Liu, Shiqiang Hu, Shirong Ma, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, Songyang Zhou, Tao Ni, Tao Yun, Tian Pei, Tian Ye, Tianyuan Yue, Wangding Zeng, Wen Liu, Wenfeng Liang, Wenjie Pang, Wenjing Luo, Wenjun Gao, Wentao Zhang, Xi Gao, Xiangwen Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaokang Zhang, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xingkai Yu, Xingyou Li, Xinyu Yang, Xinyuan Li, Xu Chen, Xuecheng Su, Xuehai Pan, Xuheng Lin, Xuwei Fu, Y. Q. Wang, Yang Zhang, Yanhong Xu, Yanru Ma, Yao Li, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Qian, Yi Yu, Yichao Zhang, Yifan Ding, Yifan Shi, Yiliang Xiong, Ying He, Ying Zhou, Yinmin Zhong, Yishi Piao, Yisong Wang, Yixiao Chen, Yixuan Tan, Yixuan Wei, Yiyang Ma, Yiyuan Liu, Yonglun Yang, Yongqiang Guo, Yongtong Wu, Yu Wu, Yuan Cheng, Yuan Ou, Yuanfan Xu, Yuduan Wang, Yue Gong, Yuhan Wu, Yuheng Zou, Yukun Li, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Z. F. Wu, Z. Z. Ren, Zehua Zhao, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhibin Gou, Zhicheng Ma, Zhigang Yan, Zhihong Shao, Zhixian Huang, Zhiyu Wu, Zhuoshu Li, Zhuping Zhang, Zian Xu, Zihao Wang, Zihui Gu, Zijia Zhu, Zilin Li, Zipeng Zhang, Ziwei Xie, Ziyi Gao, Zizheng Pan, Zongqing Yao, Bei Feng, Hui Li, J. L. Cai, Jiaqi Ni, Lei Xu, Meng Li, Ning Tian, R. J. Chen, R. L. Jin, S. S. Li, Shuang Zhou, Tianyu Sun, X. Q. Li, Xiangyue Jin, Xiaojin Shen, Xiaosha Chen, Xinnan Song, Xinyi Zhou, Y. X. Zhu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, Dongjie Ji, Jian Liang, Jianzhong Guo, Jin Chen, Leyi Xia, Miaojun Wang, Mingming Li, Peng Zhang, Ruyi Chen, Shangmian Sun, Shaoqing Wu, Shengfeng Ye, T. Wang, W. L. Xiao, Wei An, Xianzu Wang, Xiaowen Sun, Xiaoxiang Wang, Ying Tang, Yukun Zha, Zekai Zhang, Zhe Ju, Zhen Zhang, Zihua Qu Published: 12.02.2025 Updated: 12.02.2025 Summary We introduce DeepSeek-V3.2, a model that harmonizes high computational efficiency with superior reasoning and agent performance. The key technical breakthroughs of DeepSeek-V3.2 are as follows: (1) DeepSeek Sparse Attention (DSA): We introduce DSA, an efficient attention mechanism that substantially reduces computational complexity while preserving model performance in long-context scenarios. (2) Scalable Reinforcement Learning Framework: By implementing a robust reinforcement learning protocol and scaling post-training compute, DeepSeek-V3.2 performs comparably to GPT-5. Notably, our high-compute variant, DeepSeek-V3.2-Speciale, surpasses GPT-5 and exhibits reasoning proficiency on par with Gemini-3.0-Pro, achieving gold-medal performance in both the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI). (3) Large-Scale Agentic Task Synthesis Pipeline: To integrate reasoning into tool-use scenarios, we developed a novel synthesis pipeline that systematically generates training data at scale. This methodology facilitates scalable agentic post-training, yielding substantial improvements in generalization and instruction-following robustness within complex, interactive environments. Source arXiv: 2512.02556v1
GUI Exploration Lab: Enhancing Screen Navigation in Agents via Multi-Turn Reinforcement Learning Authors Haolong Yan, Yeqing Shen, Xin Huang, Jia Wang, Kaijun Tan, Zhixuan Liang, Hongxin Li, Zheng Ge, Osamu Yoshie, Si Li, Xiangyu Zhang, Daxin Jiang Published: 12.02.2025 Updated: 12.02.2025 Summary With the rapid development of Large Vision Language Models, the focus of Graphical User Interface (GUI) agent tasks shifts from single-screen tasks to complex screen navigation challenges. However, real-world GUI environments, such as PC software and mobile Apps, are often complex and proprietary, making it difficult to obtain the comprehensive environment information needed for agent training and evaluation. This limitation hinders systematic investigation and benchmarking of agent navigation capabilities. To address this limitation, we introduce GUI Exploration Lab, a simulation environment engine for GUI agent navigation research that enables flexible definition and composition of screens, icons, and navigation graphs, while providing full access to environment information for comprehensive agent training and evaluation. Through extensive experiments, we find that supervised fine-tuning enables effective memorization of fundamental knowledge, serving as a crucial foundation for subsequent training. Building on this, single-turn reinforcement learning further enhances generalization to unseen scenarios. Finally, multi-turn reinforcement learning encourages the development of exploration strategies through interactive trial and error, leading to further improvements in screen navigation performance. We validate our methods on both static and interactive benchmarks, demonstrating that our findings generalize effectively to real-world scenarios. These findings demonstrate the advantages of reinforcement learning approaches in GUI navigation and offer practical guidance for building more capable and generalizable GUI agents. Source arXiv: 2512.02423v1
Quantum-Classical Separation in Bounded-Resource Tasks Arising from Measurement Contextuality Authors Shashwat Kumar, Eliott Rosenberg, Alejandro Grajales Dau, Rodrigo Cortinas, Dmitri Maslov, Richard Oliver, Adam Zalcman, Matthew Neeley, Alice Pagano, Aaron Szasz, Ilya Drozdov, Zlatko Minev, Craig Gidney, Noureldin Yosri, Stijn J. de Graaf, Aniket Maiti, Dmitry Abanin, Rajeev Acharya, Laleh Aghababaie Beni, Georg Aigeldinger, Ross Alcaraz, Sayra Alcaraz, Trond I. Andersen, Markus Ansmann, Frank Arute, Kunal Arya, Walt Askew, Nikita Astrakhantsev, Juan Atalaya, Ryan Babbush, Brian Ballard, Joseph C. Bardin, Hector Bates, Andreas Bengtsson, Martin Bigdeli, Alexander Bilmes, Simon Bilodeau, Felix Borjans, Alexandre Bourassa, Jenna Bovaird, Dylan Bowers, Leon Brill, Peter Brooks, Michael Broughton, David A. Browne, Brett Buchea, Bob B. Buckley, Tim Burger, Brian Burkett, Nicholas Bushnell, Jamal Busnaina, Anthony Cabrera, Juan Campero, Hung-Shen Chang, Silas Chen, Zijun Chen, Ben Chiaro, Liang-Ying Chih, Jahan Claes, Agnetta Y. Cleland, Bryan Cochrane, Matt Cockrell, Josh Cogan, Roberto Collins, Paul Conner, Harold Cook, William Courtney, Alexander L. Crook, Ben Curtin, Sayan Das, Laura De Lorenzo, Sean Demura, Agustin Di Paolo, Paul Donohoe, Andrew Dunsworth, Valerie Ehimhen, Alec Eickbusch, Aviv Moshe Elbag, Lior Ella, Mahmoud Elzouka, David Enriquez, Catherine Erickson, Vinicius S. Ferreira, Marcos Flores, Leslie Flores Burgos, Ebrahim Forati, Jeremiah Ford, Austin G. Fowler, Brooks Foxen, Masaya Fukami, Alan Wing Lun Fung, Lenny Fuste, Suhas Ganjam, Gonzalo Garcia, Christopher Garrick, Robert Gasca, Helge Gehring, Élie Genois, William Giang, Dar Gilboa, James E. Goeders, Ed Gonzales, Raja Gosula, Dietrich Graumann, Joel Grebel, Alex Greene, Jonathan A. Gross, Jose Guerrero, Tan Ha, Steve Habegger, Tanner Hadick, Monica Hansen, Matthew P. Harrigan, Sean D. Harrington, Jeanne Hartshorn, Stephen Heslin, Paula Heu, Oscar Higgott, Reno Hiltermann, Jeremy Hilton, Hsin-Yuan Huang, Mike Hucka, Ashley Huff, William J. Huggins, Evan Jeffrey, Shaun Jevons, Zhang Jiang, Xiaoxuan Jin, Cody Jones, Chaitali Joshi, Pavol Juhas, Andreas Kabel, Dvir Kafri, Hui Kang, Amir H. Karamlou, Ryan Kaufman, Kostyantyn Kechedzhi, Trupti Khaire, Tanuj Khattar, Mostafa Khezri, Seon Kim, Paul V. Klimov, Can M. Knaut, Bryce Kobrin, Alexander N. Korotkov, Fedor Kostritsa, John Mark Kreikebaum, Ryuho Kudo, Ben Kueffler, Arun Kumar, Vladislav D. Kurilovich, Vitali Kutsko, David Landhuis, Tiano Lange-Dei, Brandon W. Langley, Pavel Laptev, Kim-Ming Lau, Loïck Le Guevel, Emma Leavell, Justin Ledford, Joy Lee, Kenny Lee, Brian J. Lester, Wendy Leung, Lily L Li, Wing Yan Li, Alexander T. Lill, William P. Livingston, Matthew T. Lloyd, Aditya Locharla, Daniel Lundahl, Aaron Lunt, Sid Madhuk, Ashley Maloney, Salvatore Mandrà, Leigh S. Martin, Orion Martin, Eric Mascot, Paul Masih Das, Cameron Maxfield, Jarrod R. McClean, Matt McEwen, Seneca Meeks, Anthony Megrant, Kevin C. Miao, Reza Molavi, Sebastian Molina, Shirin Montazeri, Charles Neill, Michael Newman, Anthony Nguyen, Murray Nguyen, Chia-Hung Ni, Murphy Yuezhen Niu, Logan Oas, William D. Oliver, Raymond Orosco, Kristoffer Ottosson, Sherman Peek, David Peterson, Alex Pizzuto, Rebecca Potter, Orion Pritchard, Michael Qian, Chris Quintana, Ganesh Ramachandran, Arpit Ranadive, Matthew J. Reagor, Rachel Resnick, David M. Rhodes, Daniel Riley, Gabrielle Roberts, Roberto Rodriguez, Emma Ropes, Emma Rosenfeld, Dario Rosenstock, Elizabeth Rossi, David A. Rower, Kannan Sankaragomathi, Murat Can Sarihan, Kevin J. Satzinger, Sebastian Schroeder, Henry F. Schurkus, Aria Shahingohar, Michael J. Shearn, Aaron Shorter, Noah Shutty, Vladimir Shvarts, Volodymyr Sivak, Spencer Small, W. Clarke Smith, David A. Sobel, Barrett Spells, Sofia Springer, George Sterling, Jordan Suchard, Alexander Sztein, Madeline Taylor, Jothi Priyanka Thiruraman, Douglas Thor, Dogan Timucin, Eifu Tomita, Alfredo Torres, M. Mert Torunbalci, Hao Tran, Abeer Vaishnav, Justin Vargas, Sergey Vdovichev, Guifre Vidal, Catherine Vollgraff Heidweiller, Meghan Voorhees, Steven Waltman, Jonathan Waltz, Shannon X. Wang, Brayden Ware, James D. Watson, Travis Weidel, Theodore White, Kristi Wong, Bryan W. K. Woo, Christopher J. Wood, Maddy Woodson, Cheng Xing, Z. Jamie Yao, Ping Yeh, Bicheng Ying, Juhwan Yoo, Elliot Young, Grayson Young, Ran Zhang, Yaxing Zhang, Ningfeng Zhu, Nicholas Zobrist, Zhenjie Zou, Shruti Puri, Erik Lucero, Julian Kelly, Sergio Boixo, Yu Chen, Vadim Smelyanskiy, Hartmut Neven, Pedram Roushan, Michel Devoret Published: 12.01.2025 Updated: 12.01.2025 Summary The prevailing view is that quantum phenomena can be harnessed to tackle certain problems beyond the reach of classical approaches. Quantifying this capability as a quantum-classical separation and demonstrating it on current quantum processors has remained elusive. Using a superconducting qubit processor, we show that quantum contextuality enables certain tasks to be performed with success probabilities beyond classical limits. With a few qubits, we illustrate quantum contextuality with the magic square game, as well as quantify it through a Kochen–Specker–Bell inequality violation. To examine many-body contextuality, we implement the N-player GHZ game and separately solve a 2D hidden linear function problem, exceeding classical success rate in both. Our work proposes novel ways to benchmark quantum processors using contextuality-based algorithms. Source arXiv: 2512.02284v1
AirSim360: A Panoramic Simulation Platform within Drone View Authors Xian Ge, Yuling Pan, Yuhang Zhang, Xiang Li, Weijun Zhang, Dizhe Zhang, Zhaoliang Wan, Xin Lin, Xiangkai Zhang, Juntao Liang, Jason Li, Wenjie Jiang, Bo Du, Ming-Hsuan Yang, Lu Qi Published: 12.01.2025 Updated: 12.01.2025 Summary The field of 360-degree omnidirectional understanding has been receiving increasing attention for advancing spatial intelligence. However, the lack of large-scale and diverse data remains a major limitation. In this work, we propose AirSim360, a simulation platform for omnidirectional data from aerial viewpoints, enabling wide-ranging scene sampling with drones. Specifically, AirSim360 focuses on three key aspects: a render-aligned data and labeling paradigm for pixel-level geometric, semantic, and entity-level understanding; an interactive pedestrian-aware system for modeling human behavior; and an automated trajectory generation paradigm to support navigation tasks. Furthermore, we collect more than 60K panoramic samples and conduct extensive experiments across various tasks to demonstrate the effectiveness of our simulator. Unlike existing simulators, our work is the first to systematically model the 4D real world under an omnidirectional setting. The entire platform, including the toolkit, plugins, and collected datasets, will be made publicly available at https://insta360-research-team.github.io/AirSim360-website. Source arXiv: 2512.02009v1
On-chip high-order parametric downconversion in the excitonic Mott insulator Nb$_3$Cl$_8$ for programmable multiphoton entangled states Authors Dmitry Skachkov, Dirk R. Englund, Michael N. Leuenberger Published: 12.01.2025 Updated: 12.01.2025 Summary Spontaneous parametric downconversion (SPDC) and four-wave mixing in $χ^{(2)}$ and $χ^{(3)}$ media underpin most entangled-photon sources, but direct generation of higher-order entangled multiphoton states by $n$-th order parametric downconversion remains extremely challenging because conventional materials exhibit tiny high-order nonlinearities. Here we show that single-layer Nb$_3$Cl$_8$, an excitonic Mott insulator on a breathing Kagome lattice, supports exceptionally large nonlinear susceptibilities up to seventh order. Many-body GW–Bethe–Salpeter and time-dependent BSE / Kadanoff–Baym simulations yield resonant $χ^{(2)}$–$χ^{(7)}$ for monolayer Nb$_3$Cl$_8$, with $|χ^{(4)}|$ and $|χ^{(5)}|$ surpassing values in prototypical transition metal dichalcogenides by 5–9 orders of magnitude. We trace this enhancement to flat bands and strongly bound Frenkel excitons with ferroelectrically aligned out-of-plane dipoles. Building on experimentally demonstrated 1$times N$ integrated beam splitters with arbitrary power ratios, we propose an on-chip architecture where each output arm hosts an Nb$_3$Cl$_8$ patch, optionally gated by graphene to tune the complex $n$-photon amplitudes. Using the ab-initio $χ^{(3)}$ and $χ^{(4)}$ values, we predict that three-photon GHZ$_3$ and four-photon cluster-state sources in this platform can achieve $n$-photon generation rates up to $sim 10^8$ and $sim 10^6$ times larger, respectively, than silica-fiber- and MoS$_2$-based implementations with comparable geometry. We derive the quantum Hamiltonian and explicit $n$-photon generation rates for this platform, and show how suitable interferometric networks enable electrically and spectrally tunable GHZ, $W$, and cluster states based on genuine high-order nonlinear processes in a 2D excitonic Mott insulator. Source arXiv: 2512.01874v1
Observation of the rare baryonic decay B^+ -> p antiLambda and measurement of its weak decay parameter Authors LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, A. Artamonov, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J. A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P. B. Battista, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. A. Behling, S. Belin, A. Bellavista, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, J. Bhom, M. S. Bieker, N. V. Biesuz, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. Bolognani, R. Bolzonella, R. B. Bonacci, N. Bondar, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, E. Bottalico, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, J. D. Brandenburg, A. Brea Rodriguez, N. Breer, J. Brodzicka, J. Brown, D. Brundu, E. Buchanan, M. Burgos Marcos, A. T. Burke, C. Burr, C. Buti, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, S. Calderon Ramirez, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, A. F. Campoverde Quezada, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A. J. Chadwick, I. Chahrour, H. Chang, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, A. Chernov, S. Chernyshenko, X. Chiotopoulos, V. Chobanova, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, X. Dai, E. Dall'Occo, J. Dalseno, C. D'Ambrosio, J. Daniel, G. Darze, A. Davidson, J. E. Davies, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, H. -P. Dembinski, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, Y. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, A. Doheny, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, J. Eschle, S. Esen, T. Evans, F. Fabiano, S. Faghih, L. N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, L. A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Führing, T. Fulghesu, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, Y. Gao, Y. Gao, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M. A. Giza, F. C. Glaser, V. V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, D. Golubkov, A. Golutvin, S. Gomez Fernandez, W. Gomulka, I. Gonçales Vaz, F. Goncalves Abrantes, M. Goncerz, G. Gong, J. A. Gooding, I. V. Gorelov, C. Gotti, E. Govorkova, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, N. A. Grieser, L. Grillo, S. Gromov, C. Gu, M. Guarise, L. Guerry, A. -K. Guseinov, E. Gushchin, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P. M. Hamilton, J. Hammerich, Q. Han, X. Han, S. Hansmann-Menzemer, L. Hao, N. Harnew, T. H. Harris, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, L. Henry, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D. C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R. J. Hunter, M. Hushchyn, D. Hutchcroft, M. Idzik, D. Ilin, P. Ilten, A. Iniukhin, A. Iohner, A. Ishteev, K. Ivshin, H. Jage, S. J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, B. K. Jashal, A. Jawahery, C. Jayaweera, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, A. Kharisova, S. Kholodenko, G. Khreich, T. Kirn, V. S. Kirsebom, O. Kitouni, S. Klaver, N. Kleijne, A. Kleimenova, D. K. Klekots, K. Klimaszewski, M. R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, M. Korolev, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, A. Kozachuk, P. Kravchenko, L. Kravchuk, O. Kravcov, M. Kreps, P. Krokovny, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, V. Kudryavtsev, E. Kulikova, A. Kupsc, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, G. Lanfranchi, C. Langenbruch, J. Langer, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, A. Leflat, S. Legotin, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, M. Li, P. Li, P. -R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, C. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. Liu, Y. L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F. C. L. Lopes, J. H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, S. López Soliño, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, E. Luppi, K. Lynch, X. -R. Lyu, G. M. Ma, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, D. Magdalinski, D. Maisuzenko, J. J. Malczewski, S. Malde, L. Malentacca, A. Malinin, T. Maltsev, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, F. M. Manganella, D. Manuzzi, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, V. Matiunin, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, M. McCann, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, G. Meier, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R. D. Moise, E. F. Molina Cardenas, T. Mombächer, M. Monk, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. Moro, J. Moron, W. Morren, A. B. Morris, A. G. Morris, R. Mountain, Z. M. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, I. Nasteva, M. Needham, E. Nekrasova, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, L. Nisi, Q. Niu, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, A. Okhotnikov, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R. H. O'Neil, J. S. Ordonez Soto, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, G. Panshin, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, D. Pereima, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M. J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, G. Pietrzyk, R. N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V. M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, N. Polukhina, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, S. Poslavskii, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J. R. Pybus, Q. Q. Qian, W. Qian, N. Qin, R. Quagliani, R. I. Rabadan Trejo, R. Racz, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogachev, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, S. R. Roy, M. S. Rudolph, M. Ruiz Diaz, R. A. Ruiz Fernandez, J. Ruiz Vidal, J. J. Saavedra-Arias, J. J. Saborido Silva, S. E. R. Sacha Emile R., N. Sagidova, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, D. Saranin, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, D. Savrina, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M. H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, A. Semennikov, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, A. Seuthe, B. Sevilla Sanjuan, Y. Shang, D. M. Shangase, M. Shapkin, R. S. Sharma, I. Shchemerov, L. Shchutska, T. Shears, L. Shekhtman, Z. Shen, S. Sheng, V. Shevchenko, B. Shi, Q. Shi, W. S. Shi, Y. Shimizu, E. Shmanin, R. Shorkin, J. D. Shupperd, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, N. Skidmore, T. Skwarnicki, M. W. Slater, E. Smith, K. Smith, M. Smith, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, A. Solovev, K. Solovieva, N. S. Sommerfeld, R. Song, Y. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, K. M. Sowa, E. Spadaro Norella, E. Spedicato, J. G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, E. N. Stein, O. Steinkamp, D. Strekalina, Y. Su, F. Suljik, J. Sun, J. Sun, L. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y. T. Tang, M. D. Tat, J. A. Teijeiro Jimenez, A. Terentev, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, A. R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T. T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, D. Torres Machado, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, D. J. Unverzagt, A. Upadhyay, B. Urbach, A. Usachov, A. Ustyuzhanin, U. Uwer, V. Vagnoni, A. Vaitkevicius, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F. C. Volle, D. vom Bruch, N. Voropaev, K. Vos, C. Vrahas, J. Wagner, J. Walsh, E. J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. Wang, X. W. Wang, Y. Wang, Y. Wang, Y. H. Wang, Z. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, M. A. Wiegertjes, C. Wild, G. Wilkinson, M. K. Wilkinson, M. Williams, M. J. Williams, M. R. J. Williams, R. Williams, S. Williams, Z. Williams, F. F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S. A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T. X. Xing, A. Xu, L. Xu, M. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, X. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, J. A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, R. Zhang, S. Zhang, S. L. Zhang, Y. Zhang, Y. Z. Zhang, Z. Zhang, Y. Zhao, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, Q. Zou, D. Zuliani, G. Zunica Published: 11.29.2025 Updated: 11.29.2025 Summary The first observation of the decay $Bprightarrow proton Lbar$ is presented, using proton-proton collision data collected by the LHCb experiment between 2016 and 2018 at a center-of-mass energy of $13tev$, corresponding to an integrated luminosity of $5.4invfb$. The signal significance exceeds seven standard deviations. Using the $Bp rightarrow KS pip$ decay as a normalization channel, the branching fraction is measured and combined with previous LHCb results based on data collected at 7 and $8tev$ in 2011 and 2012, yielding begin{eqnarray} BF(Bprightarrow proton Lbar) = (1.24pm0.17pm0.05pm0.03)times10^{-7},,nonumber end{eqnarray} where the first uncertainty is statistical, the second is systematic, and the third comes from the uncertainty on the branching fraction of the normalization channel. The $Bprightarrow proton Lbar$ weak decay parameter is measured to be $α_B = 0.87_{-0.29}^{+0.26} pm 0.09$, indicating the presence of comparable S-wave and P-wave decay amplitudes. Source arXiv: 2512.00328v1
Observation of the rare baryonic decay $B^{+}rightarrow p bar{itΛ}$ and measurement of its weak decay parameter Authors LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, A. Artamonov, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J. A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P. B. Battista, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. A. Behling, S. Belin, A. Bellavista, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, J. Bhom, M. S. Bieker, N. V. Biesuz, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. Bolognani, R. Bolzonella, R. B. Bonacci, N. Bondar, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, E. Bottalico, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, J. D. Brandenburg, A. Brea Rodriguez, N. Breer, J. Brodzicka, J. Brown, D. Brundu, E. Buchanan, M. Burgos Marcos, A. T. Burke, C. Burr, C. Buti, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, S. Calderon Ramirez, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, A. F. Campoverde Quezada, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A. J. Chadwick, I. Chahrour, H. Chang, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, A. Chernov, S. Chernyshenko, X. Chiotopoulos, V. Chobanova, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, X. Dai, E. Dall'Occo, J. Dalseno, C. D'Ambrosio, J. Daniel, G. Darze, A. Davidson, J. E. Davies, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, H. -P. Dembinski, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, Y. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, A. Doheny, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, J. Eschle, S. Esen, T. Evans, F. Fabiano, S. Faghih, L. N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, L. A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Führing, T. Fulghesu, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, Y. Gao, Y. Gao, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M. A. Giza, F. C. Glaser, V. V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, D. Golubkov, A. Golutvin, S. Gomez Fernandez, W. Gomulka, I. Gonçales Vaz, F. Goncalves Abrantes, M. Goncerz, G. Gong, J. A. Gooding, I. V. Gorelov, C. Gotti, E. Govorkova, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, N. A. Grieser, L. Grillo, S. Gromov, C. Gu, M. Guarise, L. Guerry, A. -K. Guseinov, E. Gushchin, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P. M. Hamilton, J. Hammerich, Q. Han, X. Han, S. Hansmann-Menzemer, L. Hao, N. Harnew, T. H. Harris, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, L. Henry, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D. C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R. J. Hunter, M. Hushchyn, D. Hutchcroft, M. Idzik, D. Ilin, P. Ilten, A. Iniukhin, A. Iohner, A. Ishteev, K. Ivshin, H. Jage, S. J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, B. K. Jashal, A. Jawahery, C. Jayaweera, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, A. Kharisova, S. Kholodenko, G. Khreich, T. Kirn, V. S. Kirsebom, O. Kitouni, S. Klaver, N. Kleijne, A. Kleimenova, D. K. Klekots, K. Klimaszewski, M. R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, M. Korolev, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, A. Kozachuk, P. Kravchenko, L. Kravchuk, O. Kravcov, M. Kreps, P. Krokovny, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, V. Kudryavtsev, E. Kulikova, A. Kupsc, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, G. Lanfranchi, C. Langenbruch, J. Langer, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, A. Leflat, S. Legotin, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, M. Li, P. Li, P. -R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, C. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. Liu, Y. L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F. C. L. Lopes, J. H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, S. López Soliño, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, E. Luppi, K. Lynch, X. -R. Lyu, G. M. Ma, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, D. Magdalinski, D. Maisuzenko, J. J. Malczewski, S. Malde, L. Malentacca, A. Malinin, T. Maltsev, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, F. M. Manganella, D. Manuzzi, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, V. Matiunin, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, M. McCann, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, G. Meier, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R. D. Moise, E. F. Molina Cardenas, T. Mombächer, M. Monk, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. Moro, J. Moron, W. Morren, A. B. Morris, A. G. Morris, R. Mountain, Z. M. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, I. Nasteva, M. Needham, E. Nekrasova, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, L. Nisi, Q. Niu, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, A. Okhotnikov, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R. H. O'Neil, J. S. Ordonez Soto, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, G. Panshin, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, D. Pereima, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M. J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, G. Pietrzyk, R. N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V. M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, N. Polukhina, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, S. Poslavskii, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J. R. Pybus, Q. Q. Qian, W. Qian, N. Qin, R. Quagliani, R. I. Rabadan Trejo, R. Racz, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogachev, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, S. R. Roy, M. S. Rudolph, M. Ruiz Diaz, R. A. Ruiz Fernandez, J. Ruiz Vidal, J. J. Saavedra-Arias, J. J. Saborido Silva, S. E. R. Sacha Emile R., N. Sagidova, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, D. Saranin, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, D. Savrina, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M. H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, A. Semennikov, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, A. Seuthe, B. Sevilla Sanjuan, Y. Shang, D. M. Shangase, M. Shapkin, R. S. Sharma, I. Shchemerov, L. Shchutska, T. Shears, L. Shekhtman, Z. Shen, S. Sheng, V. Shevchenko, B. Shi, Q. Shi, W. S. Shi, Y. Shimizu, E. Shmanin, R. Shorkin, J. D. Shupperd, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, N. Skidmore, T. Skwarnicki, M. W. Slater, E. Smith, K. Smith, M. Smith, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, A. Solovev, K. Solovieva, N. S. Sommerfeld, R. Song, Y. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, K. M. Sowa, E. Spadaro Norella, E. Spedicato, J. G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, E. N. Stein, O. Steinkamp, D. Strekalina, Y. Su, F. Suljik, J. Sun, J. Sun, L. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y. T. Tang, M. D. Tat, J. A. Teijeiro Jimenez, A. Terentev, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, A. R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T. T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, D. Torres Machado, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, D. J. Unverzagt, A. Upadhyay, B. Urbach, A. Usachov, A. Ustyuzhanin, U. Uwer, V. Vagnoni, A. Vaitkevicius, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F. C. Volle, D. vom Bruch, N. Voropaev, K. Vos, C. Vrahas, J. Wagner, J. Walsh, E. J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. Wang, X. W. Wang, Y. Wang, Y. Wang, Y. H. Wang, Z. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, M. A. Wiegertjes, C. Wild, G. Wilkinson, M. K. Wilkinson, M. Williams, M. J. Williams, M. R. J. Williams, R. Williams, S. Williams, Z. Williams, F. F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S. A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T. X. Xing, A. Xu, L. Xu, M. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, X. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, J. A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, R. Zhang, S. Zhang, S. L. Zhang, Y. Zhang, Y. Z. Zhang, Z. Zhang, Y. Zhao, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, Q. Zou, D. Zuliani, G. Zunica Published: 11.29.2025 Updated: 12.03.2025 Summary The first observation of the decay $B^{+}rightarrow p bar{itΛ}$ is presented, using proton-proton collision data collected by the LHCb experiment between 2016 and 2018 at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of $5.4,mathrm{fb}^{-1}$. The signal significance exceeds seven standard deviations. Using the $B^{+} rightarrow K^{mathrm{0}}_{mathrm{S}} π^{+}$ decay as a normalization channel, the branching fraction is measured and combined with previous LHCb results based on data collected at 7 and 8 TeV in 2011 and 2012, yielding $mathcal{B}(B^{+}rightarrow p bar{itΛ})=(1.24pm 0.17pm 0.05pm 0.03)times 10^{-7}$, where the first uncertainty is statistical, the second is systematic, and the third comes from the uncertainty on the branching fraction of the normalization channel. The $B^{+}rightarrow p bar{itΛ}$ weak decay parameter is measured to be $α_B = 0.87_{-0.29}^{+0.26} pm 0.09$, indicating the presence of comparable S-wave and P-wave decay amplitudes. Source arXiv: 2512.00328v2
Thinking by Doing: Building Efficient World Model Reasoning in LLMs via Multi-turn Interaction Authors Bao Shu, Yan Cai, Jianjian Sun, Chunrui Han, En Yu, Liang Zhao, Jingcheng Hu, Yinmin Zhang, Haoran Lv, Yuang Peng, Zheng Ge, Xiangyu Zhang, Daxin Jiang, Xiangyu Yue Published: 11.28.2025 Updated: 11.28.2025 Summary Developing robust world model reasoning is crucial for large language model (LLM) agents to plan and interact in complex environments. While multi-turn interaction offers a superior understanding of environmental dynamics via authentic feedback, current approaches often impose a rigid reasoning process, which constrains the model’s active learning, ultimately hindering efficient world model reasoning. To address these issues, we explore world-model internalization through efficient interaction and active reasoning (WMAct), which liberates the model from structured reasoning, allowing the model to shape thinking directly through its doing, and achieves effective and efficient world model reasoning with two key mechanisms: (1) a reward rescaling mechanism adjusting outcome reward based on action efficacy to incentivize redundancy reduction and purposeful interaction; (2) an interaction frequency annealing strategy to progressively reduce the maximum allowed interaction turns, which compels the model to condense its learning and internalize environmental dynamics rather than over-relying on environmental cues. Our experiments on Sokoban, Maze, and Taxi show that WMAct yields effective world model reasoning capable of resolving tasks in a single turn that previously required multiple interactions and fosters strong transferability to complex environments, improving performance on a suite of reasoning benchmarks. Source arXiv: 2511.23476v1
All-Optical Photonic Crystal Bolometer with Ultra-Low Heat Capacity for Scalable Thermal Imaging Authors Louis Follet, Jordan Goldstein, Christopher L. Panuski, Ian Christen, Sivan Trajtenberg-Mills, Dirk R. Englund Published: 11.27.2025 Updated: 11.27.2025 Summary High-speed thermal imaging in the long-wave infrared (LWIR) is critical for applications from autonomous navigation to medical screening, yet existing uncooled detectors are fundamentally constrained. Resistive bolometers are limited by electronic noise and the parasitic thermal load of wired readouts, while state-of-the-art nanomechanical resonators require vacuum packaging to maintain the mechanical $Q$ needed for sensitivity, preventing simultaneous fast and high-sensitivity operation. Here, we introduce and demonstrate an uncooled thermal detector that addresses these challenges via an all-optical transduction mechanism. The heterogeneously integrated pixel is engineered for minimal thermal mass, combining pyrolytic carbon absorbers for broadband LWIR absorption, hollow zirconia structures for ultra-low-conductance thermal isolation — leaving gas-mediated convection as the dominant tunable pathway — and a silicon photonic crystal cavity that serves as a high-$Q$ optical thermometer. Operating at ambient temperature and pressure, we measure a specific detectivity of $1.1times10^{7}$ Jones and a thermal time constant of $34~μmathrm{s}$, corresponding to a speed that surpasses typical high-sensitivity uncooled technologies by an order of magnitude. While the demonstrated sensitivity is instrument-limited, our analysis shows that the device architecture itself supports a >25-fold performance enhancement toward its fundamental thermorefractive-noise-limited value ($3.1times10^{8}$ Jones). Crucially, because the optical readout is pressure-insensitive, the operating point can be tuned along the speed-sensitivity trade-off. We expect this architecture to provide a general route toward scalable, high-performance thermal imaging systems. Source arXiv: 2511.22006v1
All-Optical Photonic Crystal Bolometer with Ultra-Low Heat Capacity for Scalable Thermal Imaging Authors Louis Follet, Jordan Goldstein, Christopher L. Panuski, Ian Christen, Sivan Trajtenberg-Mills, Dirk R. Englund Published: 11.27.2025 Updated: 12.19.2025 Summary High-speed thermal imaging in the long-wave infrared (LWIR) is critical for applications from autonomous navigation to medical screening, yet existing uncooled detectors are fundamentally constrained. Resistive bolometers are limited by electronic noise and the parasitic thermal load of wired readouts, while state-of-the-art nanomechanical resonators typically rely on vacuum packaging to maintain the mechanical $Q$ needed for sensitivity. Here, we introduce and demonstrate an uncooled thermal detector that addresses these challenges via an all-optical transduction mechanism. The heterogeneously integrated pixel is engineered for minimal thermal mass, combining pyrolytic carbon absorbers for broadband LWIR absorption, hollow zirconia structures for ultra-low-conductance thermal isolation, and a silicon photonic crystal cavity that serves as a high-$Q$ optical thermometer. Operating at ambient temperature and pressure, we measure a specific detectivity of $1.1times10^{7}$ Jones and a thermal time constant of $27~μmathrm{s}$, corresponding to a speed that surpasses typical high-sensitivity uncooled technologies by an order of magnitude. Based on this detectivity measurement, which is limited by the noise floor of the external optical detection electronics, a physics-based model predicts a $>25text{-fold}$ performance enhancement to its fundamental thermorefractive-noise-limited value ($3times10^{8}$ Jones). The optical readout remains functional across ambient and vacuum environments. We expect this architecture to provide a general route toward scalable, high-performance thermal imaging systems. Source arXiv: 2511.22006v2
Study of the reactions $bar{n} p to 2π^{+}π^{-}$, $2π^{+}π^{-}π^{0}$, and $2π^{+}π^{-}2π^{0}$ using $J/ψto p π^{-}bar{n}$ Authors BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, X. L. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, T. T. Chang, G. R. Che, Y. Z. Che, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, W. Chen, X. R. Chen, X. T. Chen, X. Y. Chen, Y. B. Chen, Y. Q. Chen, Z. K. Chen, J. Cheng, L. N. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. Cottee-Meldrum, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denisenko, M. Destefanis, F. De Mori, X. X. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, S. X. Du, X. L. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, L. Feng, Q. X. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Y. Gao, Y. N. Gao, Y. N. Gao, Y. Y. Gao, Z. Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, J. Gollub, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. D. Gu, M. H. Gu, C. Y. Guan, A. Q. Guo, J. N. Guo, L. B. Guo, M. J. Guo, R. P. Guo, X. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, T. T. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, C. Z. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. J. Jiang, X. S. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, L. C. L. Jin, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, X. L. Kang, X. S. Kang, B. C. Ke, V. Khachatryan, A. Khoukaz, O. B. Kolcu, B. Kopf, L. Kröger, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, C. K. Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. L. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, J. W. Li, K. Li, K. L. Li, L. J. Li, Lei Li, M. H. Li, M. R. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. X. Li, Shanshan Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. K. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. G. Li, Y. P. Li, Z. H. Li, Z. J. Li, Z. X. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, D. X. Lin, L. Q. Lin, T. Lin, B. J. Liu, B. X. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, W. M. Liu, W. T. Liu, X. Liu, X. K. Liu, X. L. Liu, X. Y. Liu, Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, Z. Y. Liu, X. C. Lou, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, F. C. Ma, H. L. Ma, Heng Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Q. A. Malik, H. X. Mao, Y. J. Mao, Z. P. Mao, S. Marcello, A. Marshall, F. M. Melendi, Y. H. Meng, Z. X. Meng, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, H. Neuwirth, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, C. Normand, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, X. J. Peng, Y. Y. Peng, K. Peters, K. Petridis, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. R. Qi, M. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, J. Rademacker, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, J. L. Shi, J. Y. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, M. H. Song, T. Z. Song, W. M. Song, Y. X. Song, Zirong Song, S. Sosio, S. Spataro, S. Stansilaus, F. Stieler, M. Stolte, S. S Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, R. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, J. J. Tang, L. F. Tang, Y. A. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, E. van der Smagt, B. Wang, B. Wang, Bo Wang, C. Wang, C. Wang, Cong Wang, D. Y. Wang, H. J. Wang, H. R. Wang, J. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, M. Wang, N. Y. Wang, S. Wang, Shun Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. L. Wang, X. N. Wang, Xin Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. J. Wang, Y. L. Wang, Y. N. Wang, Y. N. Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, Ziyi Wang, D. Wei, D. H. Wei, H. R. Wei, F. Weidner, S. P. Wen, U. Wiedner, G. Wilkinson, M. Wolke, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. W. Wu, Y. J. Wu, Z. Wu, L. Xia, B. H. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. J. Xu, G. F. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, T. D. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, W. H. Yan, W. P. Yan, X. Q. Yan, X. Q. Yan, Y. Y. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, R. J. Yang, Y. Yang, Y. H. Yang, Y. Q. Yang, Y. Z. Yang, Z. P. Yao, M. Ye, M. H. Ye, Z. J. Ye, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, L. W. Yu, T. Yu, X. D. Yu, Y. C. Yu, Y. C. Yu, C. Z. Yuan, H. Yuan, J. Yuan, J. Yuan, L. Yuan, M. K. Yuan, S. H. Yuan, Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Yujie Zeng, Y. J. Zeng, Y. C. Zhai, Y. H. Zhan, Shunan Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, G. Y. Zhang, H. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. P. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Y. Zhang, Zh. Zh. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, L. Zhao, M. G. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. P. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, H. Zhou, J. Q. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. X. Zhou, Y. Z. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. X. Zhu, Lin Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, J. H. Zou Published: 11.26.2025 Updated: 11.26.2025 Summary We report an experimental investigation of the reactions $bar{n} p to 2π^{+}π^{-}$, $bar{n} p to 2π^{+}π^{-}π^{0}$, and $bar{n} p to 2π^{+}π^{-}2π^{0}$ using $(10.087 pm 0.044) times 10^{9}$ $J/ψ$ events collected with the BESIII detector at the BEPCII storage ring. The antineutron ($bar{n}$) is produced in the decay $J/ψto p π^{-} bar{n}$ with studied momentum from 200~MeV/$c$ to 1174~MeV/$c$, while the target proton originates from the hydrogen nuclei in the cooling oil of the beam pipe. This novel method pioneers the study of $bar{n}$-nucleon interactions at an $e^{+}e^{-}$ collider, providing the first experimental data for $bar{n}$ momenta exceeding 800~MeV/$c$. Source arXiv: 2511.21462v1
AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios Authors Chenglizhao Chen, Shaofeng Liang, Runwei Guan, Xiaolou Sun, Haocheng Zhao, Haiyun Jiang, Tao Huang, Henghui Ding, Qing-Long Han Published: 11.26.2025 Updated: 12.01.2025 Summary Referring Multi-Object Tracking (RMOT) aims to achieve precise object detection and tracking through natural language instructions, representing a fundamental capability for intelligent robotic systems. However, current RMOT research remains mostly confined to ground-level scenarios, which constrains their ability to capture broad-scale scene contexts and perform comprehensive tracking and path planning. In contrast, Unmanned Aerial Vehicles (UAVs) leverage their expansive aerial perspectives and superior maneuverability to enable wide-area surveillance. Moreover, UAVs have emerged as critical platforms for Embodied Intelligence, which has given rise to an unprecedented demand for intelligent aerial systems capable of natural language interaction. To this end, we introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios, which aims to bridge this research gap. To facilitate its construction, we develop an innovative semi-automated collaborative agent-based labeling assistant (COALA) framework that significantly reduces labor costs while maintaining annotation quality. Furthermore, we propose HawkEyeTrack (HETrack), a novel method that collaboratively enhances vision-language representation learning and improves the perception of UAV scenarios. Comprehensive experiments validated the challenging nature of our dataset and the effectiveness of our method. Source arXiv: 2511.21053v2
AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios Authors Chenglizhao Chen, Shaofeng Liang, Runwei Guan, Xiaolou Sun, Haocheng Zhao, Haiyun Jiang, Tao Huang, Henghui Ding, Qing-Long Han Published: 11.26.2025 Updated: 11.26.2025 Summary Referring Multi-Object Tracking (RMOT) aims to achieve precise object detection and tracking through natural language instructions, representing a fundamental capability for intelligent robotic systems. However, current RMOT research remains mostly confined to ground-level scenarios, which constrains their ability to capture broad-scale scene contexts and perform comprehensive tracking and path planning. In contrast, Unmanned Aerial Vehicles (UAVs) leverage their expansive aerial perspectives and superior maneuverability to enable wide-area surveillance. Moreover, UAVs have emerged as critical platforms for Embodied Intelligence, which has given rise to an unprecedented demand for intelligent aerial systems capable of natural language interaction. To this end, we introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios, which aims to bridge this research gap. To facilitate its construction, we develop an innovative semi-automated collaborative agent-based labeling assistant (COALA) framework that significantly reduces labor costs while maintaining annotation quality. Furthermore, we propose HawkEyeTrack (HETrack), a novel method that collaboratively enhances vision-language representation learning and improves the perception of UAV scenarios. Comprehensive experiments validated the challenging nature of our dataset and the effectiveness of our method. Source arXiv: 2511.21053v1
Routing in Non-Isotonic Quantum Networks Authors Maxwell Tang, Garrett Hinkley, Kenneth Goodenough, Stefan Krastanov, Guus Avis Published: 11.25.2025 Updated: 11.25.2025 Summary Optimal routing in quantum-repeater networks requires finding the best path that connects a pair of end nodes. Most previous work on routing in quantum networks assumes utility functions that are isotonic, meaning that the ordering of two paths does not change when extending both with the same edge. However, we show that utility functions that take into account both the rate and quality of the entanglement generation (e.g., the secret-key rate) are often non-isotonic. This makes pathfinding difficult as classical algorithms such as Dijkstra’s become unsuitable, with the state of the art for quantum networks being an exhaustive search over all possible paths. In this work we present improved algorithms. First, we present two best-first-search algorithms that use destination-aware merit functions for faster convergence. One of these provably finds the best path, while the other uses heuristics to achieve an effectively sublinear scaling of the query count in the network size while in practice always finding a close-to-optimal path. Second, we present metaheuristic algorithms (simulated annealing and a genetic algorithm) that enable tuning a tradeoff between path quality and computational overhead. While we focus on swap-ASAP quantum repeaters for concreteness, our algorithms are readily generalized to different repeater schemes and models. Source arXiv: 2511.20628v1
First observation of the $overline{B}_{s}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$ decay and evidence for the $overline{B}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$ decay Authors LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, M. Akthar, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, A. Artamonov, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J. A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P. B. Battista, A. Bavarchee, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. A. Behling, S. Belin, A. Bellavista, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, M. S. Bieker, N. V. Biesuz, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. Bolognani, R. Bolzonella, R. B. Bonacci, N. Bondar, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, E. Bottalico, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, J. D. Brandenburg, A. Brea Rodriguez, N. Breer, J. Brodzicka, J. Brown, D. Brundu, E. Buchanan, M. Burgos Marcos, A. T. Burke, C. Burr, C. Buti, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, S. Calderon Ramirez, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, A. C. Campos, A. F. Campoverde Quezada, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A. J. Chadwick, I. Chahrour, H. Chang, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, A. Chernov, S. Chernyshenko, X. Chiotopoulos, V. Chobanova, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, M. Cubero Campos, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, X. Dai, E. Dall'Occo, J. Dalseno, C. D'Ambrosio, J. Daniel, G. Darze, A. Davidson, J. E. Davies, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, Y. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, A. Doheny, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, J. Eschle, T. Evans, F. Fabiano, S. Faghih, L. N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, L. A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Führing, T. Fulghesu, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, Y. Gao, Y. Gao, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M. A. Giza, F. C. Glaser, V. V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, D. Golubkov, A. Golutvin, S. Gomez Fernandez, W. Gomulka, I. Gonçales Vaz, F. Goncalves Abrantes, M. Goncerz, G. Gong, J. A. Gooding, I. V. Gorelov, C. Gotti, E. Govorkova, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, N. A. Grieser, L. Grillo, S. Gromov, C. Gu, M. Guarise, L. Guerry, A. -K. Guseinov, E. Gushchin, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P. M. Hamilton, J. Hammerich, Q. Han, X. Han, S. Hansmann-Menzemer, L. Hao, N. Harnew, T. H. Harris, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, L. Henry, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D. C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R. J. Hunter, M. Hushchyn, D. Hutchcroft, M. Idzik, D. Ilin, P. Ilten, A. Iniukhin, A. Iohner, A. Ishteev, K. Ivshin, H. Jage, S. J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, B. K. Jashal, A. Jawahery, C. Jayaweera, A. Jelavic, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, A. Kharisova, S. Kholodenko, G. Khreich, F. Kiraz, T. Kirn, V. S. Kirsebom, S. Klaver, N. Kleijne, A. Kleimenova, D. K. Klekots, K. Klimaszewski, M. R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, A. Kozachuk, P. Kravchenko, L. Kravchuk, O. Kravcov, M. Kreps, P. Krokovny, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, V. Kudryavtsev, E. Kulikova, A. Kupsc, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, G. Lanfranchi, C. Langenbruch, J. Langer, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, A. Leflat, S. Legotin, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, M. Li, P. Li, P. -R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, C. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. Liu, Y. L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F. C. L. Lopes, J. H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, S. López Soliño, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, E. Luppi, K. Lynch, X. -R. Lyu, G. M. Ma, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, D. Magdalinski, D. Maisuzenko, J. J. Malczewski, S. Malde, L. Malentacca, A. Malinin, T. Maltsev, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, F. M. Manganella, D. Manuzzi, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, V. Matiunin, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, D. Mazzanti Tarancon, M. McCann, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R. D. Moise, E. F. Molina Cardenas, T. Mombächer, M. Monk, T. Monnard, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. Moro, J. Moron, W. Morren, A. B. Morris, A. G. Morris, R. Mountain, Z. M. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, G. Napoletano, I. Nasteva, M. Needham, E. Nekrasova, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, L. Nisi, Q. Niu, B. K. Njoki, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, A. Okhotnikov, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R. H. O'Neil, J. S. Ordonez Soto, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, G. Panshin, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, D. Pereima, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M. J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, G. Pietrzyk, R. N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V. M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, N. Polukhina, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, S. Poslavskii, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J. R. Pybus, Q. Q. Qian, W. Qian, N. Qin, R. Quagliani, R. I. Rabadan Trejo, R. Racz, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogachev, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, S. R. Roy, M. S. Rudolph, M. Ruiz Diaz, R. A. Ruiz Fernandez, J. Ruiz Vidal, J. J. Saavedra-Arias, J. J. Saborido Silva, S. E. R. Sacha Emile R., N. Sagidova, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, D. Saranin, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, D. Savrina, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M. H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, A. Semennikov, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, A. Seuthe, B. Sevilla Sanjuan, Y. Shang, D. M. Shangase, M. Shapkin, R. S. Sharma, I. Shchemerov, L. Shchutska, T. Shears, L. Shekhtman, Z. Shen, S. Sheng, V. Shevchenko, B. Shi, J. Shi, Q. Shi, W. S. Shi, Y. Shimizu, E. Shmanin, R. Shorkin, J. D. Shupperd, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, I. Siral, N. Skidmore, T. Skwarnicki, M. W. Slater, E. Smith, M. Smith, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, A. Solovev, K. Solovieva, N. S. Sommerfeld, R. Song, Y. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, K. M. Sowa, E. Spadaro Norella, E. Spedicato, J. G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, O. Steinkamp, D. Strekalina, Y. Su, F. Suljik, J. Sun, J. Sun, L. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y. T. Tang, M. D. Tat, J. A. Teijeiro Jimenez, A. Terentev, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, A. R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T. T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, D. J. Unverzagt, A. Upadhyay, B. Urbach, A. Usachov, A. Ustyuzhanin, U. Uwer, V. Vagnoni, A. Vaitkevicius, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F. C. Volle, D. vom Bruch, N. Voropaev, K. Vos, C. Vrahas, J. Wagner, J. Walsh, E. J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. Wang, X. W. Wang, Y. Wang, Y. Wang, Y. H. Wang, Z. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, M. A. Wiegertjes, C. Wild, G. Wilkinson, M. K. Wilkinson, M. Williams, M. J. Williams, M. R. J. Williams, R. Williams, S. Williams, Z. Williams, F. F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S. A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T. X. Xing, A. Xu, L. Xu, M. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, X. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, J. A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, R. Zhang, S. Zhang, S. L. Zhang, Y. Zhang, Y. Z. Zhang, Z. Zhang, Y. Zhao, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, D. Zuliani, G. Zunica Published: 11.25.2025 Updated: 11.25.2025 Summary A search is presented for the two-body charmed baryonic decays $overline{B}_{(s)}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$, using a data sample collected by the LHCb experiment during 2011–2012 and 2015–2018 corresponding to an integrated luminosity of $9,mathrm{fb}^{-1}$. The first observation of the $overline{B}_{s}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$ decay is reported with $6.2σ$ significance, along with $4.3σ$ evidence for the $overline{B}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$ decay. The branching fractions are measured to be $cal{B}(overline{B}^{0}toΛ_{c}^{+}overlineΛ_{c}^{-}) = (1.01^{+0.27}_{-0.28} pm 0.08 pm 0.15) times 10^{-5}$ and $cal{B}(overline{B}_{s}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}) = (5.0 pm 1.3 pm 0.5 pm 0.8) times 10^{-5}$, where the first uncertainty is statistical, the second systematic, and the third due to external inputs. These results provide novel experimental inputs for the theoretical framework describing two-body baryonic decays of $B$ mesons via $W$-emission and $W$-exchange mechanisms. Source arXiv: 2511.20476v1
First observation of the $overline{B}_{s}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$ decay and evidence for the $overline{B}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$ decay Authors LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, M. Akthar, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, A. Artamonov, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J. A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P. B. Battista, A. Bavarchee, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. A. Behling, S. Belin, A. Bellavista, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, M. S. Bieker, N. V. Biesuz, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. Bolognani, R. Bolzonella, R. B. Bonacci, N. Bondar, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, E. Bottalico, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, J. D. Brandenburg, A. Brea Rodriguez, N. Breer, J. Brodzicka, J. Brown, D. Brundu, E. Buchanan, M. Burgos Marcos, A. T. Burke, C. Burr, C. Buti, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, S. Calderon Ramirez, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, A. C. Campos, A. F. Campoverde Quezada, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A. J. Chadwick, I. Chahrour, H. Chang, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, A. Chernov, S. Chernyshenko, X. Chiotopoulos, V. Chobanova, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, M. Cubero Campos, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, X. Dai, E. Dall'Occo, J. Dalseno, C. D'Ambrosio, J. Daniel, G. Darze, A. Davidson, J. E. Davies, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, Y. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, A. Doheny, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, J. Eschle, T. Evans, F. Fabiano, S. Faghih, L. N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, L. A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Führing, T. Fulghesu, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, Y. Gao, Y. Gao, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M. A. Giza, F. C. Glaser, V. V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, D. Golubkov, A. Golutvin, S. Gomez Fernandez, W. Gomulka, I. Gonçales Vaz, F. Goncalves Abrantes, M. Goncerz, G. Gong, J. A. Gooding, I. V. Gorelov, C. Gotti, E. Govorkova, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, N. A. Grieser, L. Grillo, S. Gromov, C. Gu, M. Guarise, L. Guerry, A. -K. Guseinov, E. Gushchin, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P. M. Hamilton, J. Hammerich, Q. Han, X. Han, S. Hansmann-Menzemer, L. Hao, N. Harnew, T. H. Harris, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, L. Henry, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D. C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R. J. Hunter, M. Hushchyn, D. Hutchcroft, M. Idzik, D. Ilin, P. Ilten, A. Iniukhin, A. Iohner, A. Ishteev, K. Ivshin, H. Jage, S. J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, B. K. Jashal, A. Jawahery, C. Jayaweera, A. Jelavic, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, A. Kharisova, S. Kholodenko, G. Khreich, F. Kiraz, T. Kirn, V. S. Kirsebom, S. Klaver, N. Kleijne, A. Kleimenova, D. K. Klekots, K. Klimaszewski, M. R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, A. Kozachuk, P. Kravchenko, L. Kravchuk, O. Kravcov, M. Kreps, P. Krokovny, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, V. Kudryavtsev, E. Kulikova, A. Kupsc, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, G. Lanfranchi, C. Langenbruch, J. Langer, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, A. Leflat, S. Legotin, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, M. Li, P. Li, P. -R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, C. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. Liu, Y. L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F. C. L. Lopes, J. H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, S. López Soliño, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, E. Luppi, K. Lynch, X. -R. Lyu, G. M. Ma, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, D. Magdalinski, D. Maisuzenko, J. J. Malczewski, S. Malde, L. Malentacca, A. Malinin, T. Maltsev, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, F. M. Manganella, D. Manuzzi, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, V. Matiunin, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, D. Mazzanti Tarancon, M. McCann, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R. D. Moise, E. F. Molina Cardenas, T. Mombächer, M. Monk, T. Monnard, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. Moro, J. Moron, W. Morren, A. B. Morris, A. G. Morris, R. Mountain, Z. M. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, G. Napoletano, I. Nasteva, M. Needham, E. Nekrasova, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, L. Nisi, Q. Niu, B. K. Njoki, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, A. Okhotnikov, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R. H. O'Neil, J. S. Ordonez Soto, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, G. Panshin, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, D. Pereima, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M. J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, G. Pietrzyk, R. N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V. M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, N. Polukhina, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, S. Poslavskii, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J. R. Pybus, Q. Q. Qian, W. Qian, N. Qin, R. Quagliani, R. I. Rabadan Trejo, R. Racz, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogachev, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, S. R. Roy, M. S. Rudolph, M. Ruiz Diaz, R. A. Ruiz Fernandez, J. Ruiz Vidal, J. J. Saavedra-Arias, J. J. Saborido Silva, S. E. R. Sacha Emile R., N. Sagidova, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, D. Saranin, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, D. Savrina, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M. H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, A. Semennikov, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, A. Seuthe, B. Sevilla Sanjuan, Y. Shang, D. M. Shangase, M. Shapkin, R. S. Sharma, I. Shchemerov, L. Shchutska, T. Shears, L. Shekhtman, Z. Shen, S. Sheng, V. Shevchenko, B. Shi, J. Shi, Q. Shi, W. S. Shi, Y. Shimizu, E. Shmanin, R. Shorkin, J. D. Shupperd, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, I. Siral, N. Skidmore, T. Skwarnicki, M. W. Slater, E. Smith, M. Smith, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, A. Solovev, K. Solovieva, N. S. Sommerfeld, R. Song, Y. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, K. M. Sowa, E. Spadaro Norella, E. Spedicato, J. G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, O. Steinkamp, D. Strekalina, Y. Su, F. Suljik, J. Sun, J. Sun, L. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y. T. Tang, M. D. Tat, J. A. Teijeiro Jimenez, A. Terentev, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, A. R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T. T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, D. J. Unverzagt, A. Upadhyay, B. Urbach, A. Usachov, A. Ustyuzhanin, U. Uwer, V. Vagnoni, A. Vaitkevicius, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F. C. Volle, D. vom Bruch, N. Voropaev, K. Vos, C. Vrahas, J. Wagner, J. Walsh, E. J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. Wang, X. W. Wang, Y. Wang, Y. Wang, Y. H. Wang, Z. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, M. A. Wiegertjes, C. Wild, G. Wilkinson, M. K. Wilkinson, M. Williams, M. J. Williams, M. R. J. Williams, R. Williams, S. Williams, Z. Williams, F. F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S. A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T. X. Xing, A. Xu, L. Xu, M. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, X. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, J. A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, R. Zhang, S. Zhang, S. L. Zhang, Y. Zhang, Y. Z. Zhang, Z. Zhang, Y. Zhao, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, D. Zuliani, G. Zunica Published: 11.25.2025 Updated: 11.27.2025 Summary A search is presented for the two-body charmed baryonic decays $overline{B}_{(s)}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$, using a data sample collected by the LHCb experiment during 2011–2012 and 2015–2018 corresponding to an integrated luminosity of $9,mathrm{fb}^{-1}$. The first observation of the $overline{B}_{s}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$ decay is reported with $6.2σ$ significance, along with $4.3σ$ evidence for the $overline{B}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}$ decay. The branching fractions are measured to be $mathcal{B}{}(overline{B}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}) = (1.01^{+0.27}_{-0.28} pm 0.08 pm 0.15) times 10^{-5}$ and $mathcal{B}{}(overline{B}_{s}^{0}toΛ_{c}^{+}overlineΛ_{c}{}^{-}) = (5.0 pm 1.3 pm 0.5 pm 0.8) times 10^{-5}$, where the first uncertainty is statistical, the second systematic, and the third due to external inputs. These results provide novel experimental inputs for the theoretical framework describing two-body baryonic decays of $B$ mesons via $W$-emission and $W$-exchange mechanisms. Source arXiv: 2511.20476v2
Observation and investigation of the $T_{cbar{c}1}(4430)^{+}$ structure in $B^{+} to ψ(2S) K_{text{S}}^{0} π^{+}$ decays Authors LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, M. Akthar, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, A. Artamonov, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J. A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P. B. Battista, A. Bavarchee, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. A. Behling, S. Belin, A. Bellavista, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, M. S. Bieker, N. V. Biesuz, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. Bolognani, R. Bolzonella, R. B. Bonacci, N. Bondar, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, E. Bottalico, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, J. D. Brandenburg, A. Brea Rodriguez, N. Breer, J. Brodzicka, J. Brown, D. Brundu, E. Buchanan, M. Burgos Marcos, A. T. Burke, C. Burr, C. Buti, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, S. Calderon Ramirez, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, A. C. Campos, A. F. Campoverde Quezada, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A. J. Chadwick, I. Chahrour, H. Chang, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, A. Chernov, S. Chernyshenko, X. Chiotopoulos, V. Chobanova, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, M. Cubero Campos, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, X. Dai, E. Dall'Occo, J. Dalseno, C. D'Ambrosio, J. Daniel, G. Darze, A. Davidson, J. E. Davies, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, Y. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, A. Doheny, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, D. Elizondo Blanco, J. Ellbracht, S. Ely, A. Ene, J. Eschle, T. Evans, F. Fabiano, S. Faghih, L. N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, F. Fassin, D. Fazzini, L. Felkowski, M. Feng, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, L. A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Führing, T. Fulghesu, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, Y. Gao, Y. Gao, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M. A. Giza, F. C. Glaser, V. V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, D. Golubkov, A. Golutvin, S. Gomez Fernandez, W. Gomulka, I. Gonçales Vaz, F. Goncalves Abrantes, M. Goncerz, G. Gong, J. A. Gooding, I. V. Gorelov, C. Gotti, E. Govorkova, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, N. A. Grieser, L. Grillo, S. Gromov, C. Gu, M. Guarise, L. Guerry, A. -K. Guseinov, E. Gushchin, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P. M. Hamilton, J. Hammerich, Q. Han, X. Han, S. Hansmann-Menzemer, L. Hao, N. Harnew, T. H. Harris, M. Hartmann, S. Hashmi, J. He, N. Heatley, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, L. Henry, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D. C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R. J. Hunter, M. Hushchyn, D. Hutchcroft, M. Idzik, D. Ilin, P. Ilten, A. Iniukhin, A. Iohner, A. Ishteev, K. Ivshin, H. Jage, S. J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, B. K. Jashal, A. Jawahery, C. Jayaweera, A. Jelavic, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, A. Kharisova, S. Kholodenko, G. Khreich, F. Kiraz, T. Kirn, V. S. Kirsebom, S. Klaver, N. Kleijne, A. Kleimenova, D. K. Klekots, K. Klimaszewski, M. R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, A. Kozachuk, P. Kravchenko, L. Kravchuk, O. Kravcov, M. Kreps, P. Krokovny, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, V. Kudryavtsev, E. Kulikova, A. Kupsc, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, G. Lanfranchi, C. Langenbruch, J. Langer, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, A. Leflat, S. Legotin, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, M. Li, P. Li, P. -R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, C. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. Liu, Y. L. Liu, G. Loachamin Ordonez, I. Lobo, A. Lobo Salvia, A. Loi, T. Long, F. C. L. Lopes, J. H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, S. López Soliño, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, E. Luppi, K. Lynch, X. -R. Lyu, G. M. Ma, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, D. Magdalinski, D. Maisuzenko, J. J. Malczewski, S. Malde, L. Malentacca, A. Malinin, T. Maltsev, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, F. M. Manganella, D. Manuzzi, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, V. Matiunin, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, D. Mazzanti Tarancon, M. McCann, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R. D. Moise, E. F. Molina Cardenas, T. Mombächer, M. Monk, T. Monnard, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. Moro, J. Moron, W. Morren, A. B. Morris, A. G. Morris, R. Mountain, Z. M. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, G. Napoletano, I. Nasteva, M. Needham, E. Nekrasova, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, L. Nisi, Q. Niu, B. K. Njoki, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, A. Okhotnikov, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R. H. O'Neil, J. S. Ordonez Soto, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, S. Paniskaki, G. Panshin, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, D. Pereima, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M. J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, G. Pietrzyk, R. N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V. M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, N. Polukhina, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, K. Popp, S. Poslavskii, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J. R. Pybus, Q. Q. Qian, W. Qian, N. Qin, R. Quagliani, R. I. Rabadan Trejo, R. Racz, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogachev, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, S. R. Roy, M. S. Rudolph, M. Ruiz Diaz, R. A. Ruiz Fernandez, J. Ruiz Vidal, J. J. Saavedra-Arias, J. J. Saborido Silva, S. E. R. Sacha Emile R., N. Sagidova, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, D. Saranin, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, D. Savrina, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M. H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, A. Semennikov, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, A. Seuthe, B. Sevilla Sanjuan, Y. Shang, D. M. Shangase, M. Shapkin, R. S. Sharma, I. Shchemerov, L. Shchutska, T. Shears, L. Shekhtman, Z. Shen, S. Sheng, V. Shevchenko, B. Shi, J. Shi, Q. Shi, W. S. Shi, Y. Shimizu, E. Shmanin, R. Shorkin, J. D. Shupperd, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, I. Siral, N. Skidmore, T. Skwarnicki, M. W. Slater, E. Smith, M. Smith, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, A. Solovev, K. Solovieva, N. S. Sommerfeld, R. Song, Y. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, K. M. Sowa, E. Spadaro Norella, E. Spedicato, J. G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, O. Steinkamp, D. Strekalina, Y. Su, F. Suljik, J. Sun, J. Sun, L. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y. T. Tang, M. D. Tat, J. A. Teijeiro Jimenez, A. Terentev, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, A. R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T. T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, D. J. Unverzagt, A. Upadhyay, B. Urbach, A. Usachov, A. Ustyuzhanin, U. Uwer, V. Vagnoni, A. Vaitkevicius, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F. C. Volle, D. vom Bruch, N. Voropaev, K. Vos, C. Vrahas, J. Wagner, J. Walsh, E. J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. Wang, X. W. Wang, Y. Wang, Y. Wang, Y. H. Wang, Z. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, M. A. Wiegertjes, C. Wild, G. Wilkinson, M. K. Wilkinson, M. Williams, M. J. Williams, M. R. J. Williams, R. Williams, S. Williams, Z. Williams, F. F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S. A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T. X. Xing, A. Xu, L. Xu, M. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, X. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, J. A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, R. Zhang, S. Zhang, S. L. Zhang, Y. Zhang, Y. Z. Zhang, Z. Zhang, Y. Zhao, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, D. Zuliani, G. Zunica Published: 11.25.2025 Updated: 11.26.2025 Summary The first four-dimensional amplitude analysis of the $B^{+} to ψ(2S) K_{text{S}}^{0} π^{+}$ decay is performed with proton-proton collision data collected by the LHCb experiment at $sqrt{s} = 13~rm{TeV}$, corresponding to an integrated luminosity of $5.4~rm{fb^{-1}}$. The data cannot be fully explained by $B^{+} to ψ(2S) K^{*+}$ contributions alone. A significantly better description of the data is obtained by adding a $T_{cbar{c}}^{+}$ contribution decaying to $ψ(2S)π^{+}$. The properties of the $T_{cbar{c}}^{+}$ structure are consistent with the exotic state $T_{cbar{c}1}(4430)^{+}$ reported in the isospin-related $bar{B}^{0} to ψ(2S) K^{-} π^{+}$ decay. Effects of a possible $T_{cbar{c}1}(4430)^{+} to bar{D}_{1}^{*}(2600)^{0} D^{+}$ decay mode on the $T_{cbar{c}1}(4430)^{+} to ψ(2S)π^{+}$ mass distribution are investigated through a Flatté parametrization, providing constraints on the relative decay strength. A description of the $T_{cbar{c}1}(4430)^{+}$ structure using the triangle singularity mechanism is studied and also found to be consistent with the data. Source arXiv: 2511.20428v2
A Comprehensive Characterization of the Vacuum Beam Guide and Its Applications Authors Yuexun Huang, Delaney Smith, Pei Zeng, Debayan Bandyopadhyay, Junyu Liu, Rana X Adhikari, Liang Jiang Published: 11.25.2025 Updated: 11.25.2025 Summary The proposed vacuum beam guide (VBG) represents an innovation in the field of quantum channel technology, guaranteeing an ultra-low level of attenuation and a broad transmission linewidth, which offers an unprecedented quantum capacity exceeding Tera-qubits per second on a continental scale. However, its stability in terms of interferometry remains unexamined. To address this gap, we have developed a comprehensive error model that captures the intrinsic phase noise power spectral density associated with VBG, thereby revealing the advantages of VBG for interferometry over existing techniques. This model facilitates a comprehensive characterization of VBG as a photonic quantum channel, thereby facilitating a detailed investigation of its potential. Our theoretical analysis demonstrates the feasibility of VBG and its expected performance in a wide range of quantum applications. Source arXiv: 2511.20031v1
Scaling Quantum Networks via Phase-Stable Vacuum Beam Guide: Architectural Blueprint and Benchmark Authors Yuexun Huang, Delaney Smith, Pei Zeng, Debayan Bandyopadhyay, Junyu Liu, Rana X Adhikari, Liang Jiang Published: 11.25.2025 Updated: 03.03.2026 Summary Scaling quantum networks to continental distances requires physical infrastructure capable of overcoming both exponential attenuation and severe phase decoherence. While the concept of vacuum beam guide (VBG) has recently emerged as a promising low-loss solution, we move beyond it by proposing a rigorous physical-layer architectural blueprint anchored by empirical data and methods from Advanced LIGO, particularly regarding its interferometric stability in dynamic environments. To evaluate its system-level viability, we benchmark this architecture against various protocols across quantum communication, metrology, and computation. Ultimately, we identify no fundamental technical blockers to scaling this infrastructure. Source arXiv: 2511.20031v3
A Comprehensive Characterization of the Vacuum Beam Guide and Its Applications Authors Yuexun Huang, Delaney Smith, Pei Zeng, Debayan Bandyopadhyay, Junyu Liu, Rana X Adhikari, Liang Jiang Published: 11.25.2025 Updated: 01.24.2026 Summary The proposed vacuum beam guide (VBG) represents a transformative advance in quantum channel technology, guaranteeing an ultra-low level of attenuation and a broad transmission linewidth, which offers an unprecedented quantum capacity, exceeding Tera-qubits per second on a continental scale. However, the stability of the VBG in terms of interferometry remains unexamined. To address this gap, we have developed a comprehensive noise budget for sources of phase noise in the VBG, highlighting its capabilities for precision interferometry. This model facilitates a comprehensive characterization of the VBG as a photonic quantum channel, thereby allowing a detailed investigation of its potential. Our theoretical analysis finds no technical or fundamental blockers for a VBG and predicts its expected performance in a wide range of quantum applications. Source arXiv: 2511.20031v2
Error-structure-tailored early fault-tolerant quantum computing Authors Pei Zeng, Guo Zheng, Qian Xu, Liang Jiang Published: 11.25.2025 Updated: 11.25.2025 Summary Fault tolerance is widely regarded as indispensable for achieving scalable and reliable quantum computing. However, the spacetime overhead required for fault-tolerant quantum computating remains prohibitively large. A critical challenge arises in many quantum algorithms with Clifford + $varphi$ compiling, where logical rotation gates $R_{Z_L}(varphi)$ serve as essential components. The Eastin-Knill theorem prevents their transversal implementation in quantum error correction codes and necessitating resource-intensive workarounds through T-gate compilation combined with magic state distillation and injection. In this work, we consider error-structure-tailored fault tolerance, where fault-tolerance conditions are analyzed by combining perturbative analysis of realistic dissipative noise processes with the structural properties of stabilizer codes. Based on this framework, we design 1-fault-tolerant continuous-angle rotation gates in stabilizer codes, implemented via dispersive-coupling Hamiltonians. Our approach could circumvent the need for T-gate compilation and distillation, offering a hardware-efficient solution that maintains simplicity, minimizes physical footprint, and requires only nearest-neighbor interactions. Integrating with recent small-angle-state preparation techniques, we can suppress the gate error to $91|varphi| p^2$ for small rotation angle (where p denotes the physical error rate). For current achievable hardware parameters ($p=10^{-3}$), this enables reliable execution of over $10^7$ small-angle rotations when $|varphi|approx 10^{-3}$, meeting the requirements of many near-term quantum applications. Compared to the 15-to-1 magic state distillation and magic state cultivation approaches, our method reduces spacetime resource costs by factors of 1337.5 and 43.6, respectively, for a Heisenberg Hamiltonian simulation task under realistic hardware assumptions. Source arXiv: 2511.19983v1
KOM: A Multi-Agent Artificial Intelligence System for Precision Management of Knee Osteoarthritis (KOA) Authors Weizhi Liu, Xi Chen, Zekun Jiang, Liang Zhao, Kunyuan Jiang, Ruisi Tang, Li Wang, Mingke You, Hanyu Zhou, Hongyu Chen, Qiankun Xiong, Yong Nie, Kang Li, Jian Li Published: 11.24.2025 Updated: 11.24.2025 Summary Knee osteoarthritis (KOA) affects more than 600 million individuals globally and is associated with significant pain, functional impairment, and disability. While personalized multidisciplinary interventions have the potential to slow disease progression and enhance quality of life, they typically require substantial medical resources and expertise, making them difficult to implement in resource-limited settings. To address this challenge, we developed KOM, a multi-agent system designed to automate KOA evaluation, risk prediction, and treatment prescription. This system assists clinicians in performing essential tasks across the KOA care pathway and supports the generation of tailored management plans based on individual patient profiles, disease status, risk factors, and contraindications. In benchmark experiments, KOM demonstrated superior performance compared to several general-purpose large language models in imaging analysis and prescription generation. A randomized three-arm simulation study further revealed that collaboration between KOM and clinicians reduced total diagnostic and planning time by 38.5% and resulted in improved treatment quality compared to each approach used independently. These findings indicate that KOM could help facilitate automated KOA management and, when integrated into clinical workflows, has the potential to enhance care efficiency. The modular architecture of KOM may also offer valuable insights for developing AI-assisted management systems for other chronic conditions. Source arXiv: 2511.19798v1
HunyuanOCR Technical Report Authors Hunyuan Vision Team, Pengyuan Lyu, Xingyu Wan, Gengluo Li, Shangpin Peng, Weinong Wang, Liang Wu, Huawen Shen, Yu Zhou, Canhui Tang, Qi Yang, Qiming Peng, Bin Luo, Hower Yang, Houwen Peng, Hongming Yang, Senhao Xie, Binghong Wu, Mana Yang, Sergey Wang, Raccoon Liu, Dick Zhu, Jie Jiang, Linus, Han Hu, Chengquan Zhang Published: 11.24.2025 Updated: 11.24.2025 Summary This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Native Vision Transformer (ViT) and a lightweight LLM connected via an MLP adapter. HunyuanOCR demonstrates superior performance, outperforming commercial APIs, traditional pipelines, and larger models (e.g., Qwen3-VL-4B). Specifically, it surpasses current public solutions in perception tasks (Text Spotting, Parsing) and excels in semantic tasks (IE, Text Image Translation), securing first place in the ICDAR 2025 DIMT Challenge (Small Model Track). Furthermore, it achieves state-of-the-art (SOTA) results on OCRBench among VLMs with fewer than 3B parameters. HunyuanOCR achieves breakthroughs in three key aspects: 1) Unifying Versatility and Efficiency: We implement comprehensive support for core capabilities including spotting, parsing, IE, VQA, and translation within a lightweight framework. This addresses the limitations of narrow “OCR expert models” and inefficient “General VLMs”. 2) Streamlined End-to-End Architecture: Adopting a pure end-to-end paradigm eliminates dependencies on pre-processing modules (e.g., layout analysis). This fundamentally resolves error propagation common in traditional pipelines and simplifies system deployment. 3) Data-Driven and RL Strategies: We confirm the critical role of high-quality data and, for the first time in the industry, demonstrate that Reinforcement Learning (RL) strategies yield significant performance gains in OCR tasks. HunyuanOCR is officially open-sourced on HuggingFace. We also provide a high-performance deployment solution based on vLLM, placing its production efficiency in the top tier. We hope this model will advance frontier research and provide a solid foundation for industrial applications. Source arXiv: 2511.19575v1
Manipulation of photonic topological edge and corner states via trivial claddings Authors Hai-Xiao Wang, Li Liang, Shuai Shao, Shiwei Tang, Junhui Hu, Yin Poo, Jian-Hua Jiang Published: 11.24.2025 Updated: 11.24.2025 Summary Crystalline symmetry offers a powerful tool to realize photonic topological phases, in which additional trivial claddings are typically required to confine topological boundary states. However, the utility of the trivial cladding in manipulating topological waves is often overlooked. Here, we demonstrate two topologically distinct kagome photonic crystals (KPCs) based on different crystalline symmetries: mathbit{C}_mathbf{6}- symmetric KPCs exhibit a quantum spin Hall phase, while mathbit{C}_mathbf{3}-symmetric KPCs serve as trivial cladding. By tuning the geometric parameter of the trivial cladding, we observe that a pair of topological interface states featured with pseudospin-momentum locking undergoes a phase transition, accompanied by the appearance and disappearance of corner states in a finite hexagonal supercell. Such a geometry-induced band inversion is characterized by a sign change in the Dirac mass of the topological interface states and holds potential for applications such as rainbow trapping. Furthermore, we experimentally demonstrate the corner states, which is a hallmark of higher-order topology, also depend critically on the trivial cladding. Our work highlights the crucial role of trivial claddings on the formation of topological boundary states, and offers a novel approach for their manipulation. Source arXiv: 2511.18705v1
Budget-Aware Tool-Use Enables Effective Agent Scaling Authors Tengxiao Liu, Zifeng Wang, Jin Miao, I-Hung Hsu, Jun Yan, Jiefeng Chen, Rujun Han, Fangyuan Xu, Yanfei Chen, Ke Jiang, Samira Daruki, Yi Liang, William Yang Wang, Tomas Pfister, Chen-Yu Lee Published: 11.21.2025 Updated: 11.21.2025 Summary Scaling test-time computation improves performance across different tasks on large language models (LLMs), which has also been extended to tool-augmented agents. For these agents, scaling involves not only “thinking” in tokens but also “acting” via tool calls. The number of tool calls directly bounds the agent’s interaction with the external environment. However, we find that simply granting agents a larger tool-call budget fails to improve performance, as they lack “budget awareness” and quickly hit a performance ceiling. To address this, we study how to scale such agents effectively under explicit tool-call budgets, focusing on web search agents. We first introduce the Budget Tracker, a lightweight plug-in that provides the agent with continuous budget awareness, enabling simple yet effective scaling. We further develop BATS (Budget Aware Test-time Scaling), an advanced framework that leverages this awareness to dynamically adapt its planning and verification strategy, deciding whether to “dig deeper” on a promising lead or “pivot” to new paths based on remaining resources. To analyze cost-performance scaling in a controlled manner, we formalize a unified cost metric that jointly accounts for token and tool consumption. We provide the first systematic study on budget-constrained agents, showing that budget-aware methods produce more favorable scaling curves and push the cost-performance Pareto frontier. Our work offers empirical insights toward a more transparent and principled understanding of scaling in tool-augmented agents. Source arXiv: 2511.17006v1
LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment Authors Xi Wang, Xianyao Ling, Kun Li, Gang Yin, Liang Zhang, Jiang Wu, Annie Wang, Weizhe Wang Published: 11.21.2025 Updated: 11.21.2025 Summary The rapid progress in Generative AI and Agent technologies is profoundly transforming enterprise data management and analytics. Traditional database applications and system deployment are fundamentally impacted by AI-driven tools, such as Retrieval-Augmented Generation (RAG) and vector database technologies, which provide new pathways for semantic querying over enterprise knowledge bases. In the meantime, data security and compliance are top priorities for organizations adopting AI technologies. For enterprise data analysis, SQL generations powered by large language models (LLMs) and AI agents, has emerged as a key bridge connecting natural language with structured data, effectively lowering the barrier to enterprise data access and improving analytical efficiency. This paper focuses on enterprise data analysis applications and system deployment, covering a range of innovative frameworks, enabling complex query understanding, multi-agent collaboration, security verification, and computational efficiency. Through representative use cases, key challenges related to distributed deployment, data security, and inherent difficulties in SQL generation tasks are discussed. Source arXiv: 2511.17676v1
Differential decay rate of $B^+ to J/ψK^+$ with the LHCb Upgrade I experiment Authors LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta, F. Abudinén, T. Ackernley, A. A. Adefisoye, B. Adeva, M. Adinolfi, P. Adlarson, C. Agapopoulou, C. A. Aidala, Z. Ajaltouni, S. Akar, K. Akiba, M. Akthar, P. Albicocco, J. Albrecht, R. Aleksiejunas, F. Alessio, P. Alvarez Cartelle, R. Amalric, S. Amato, J. L. Amey, Y. Amhis, L. An, L. Anderlini, M. Andersson, P. Andreola, M. Andreotti, S. Andres Estrada, A. Anelli, D. Ao, C. Arata, F. Archilli, Z. Areg, M. Argenton, S. Arguedas Cuendis, L. Arnone, A. Artamonov, M. Artuso, E. Aslanides, R. Ataíde Da Silva, M. Atzeni, B. Audurier, J. A. Authier, D. Bacher, I. Bachiller Perea, S. Bachmann, M. Bachmayer, J. J. Back, P. Baladron Rodriguez, V. Balagura, A. Balboni, W. Baldini, Z. Baldwin, L. Balzani, H. Bao, J. Baptista de Souza Leite, C. Barbero Pretel, M. Barbetti, I. R. Barbosa, R. J. Barlow, M. Barnyakov, S. Barsuk, W. Barter, J. Bartz, S. Bashir, B. Batsukh, P. B. Battista, A. Bay, A. Beck, M. Becker, F. Bedeschi, I. B. Bediaga, N. A. Behling, S. Belin, A. Bellavista, K. Belous, I. Belov, I. Belyaev, G. Benane, G. Bencivenni, E. Ben-Haim, A. Berezhnoy, R. Bernet, S. Bernet Andres, A. Bertolin, F. Betti, J. Bex, O. Bezshyyko, S. Bhattacharya, J. Bhom, M. S. Bieker, N. V. Biesuz, A. Biolchini, M. Birch, F. C. R. Bishop, A. Bitadze, A. Bizzeti, T. Blake, F. Blanc, J. E. Blank, S. Blusk, V. Bocharnikov, J. A. Boelhauve, O. Boente Garcia, T. Boettcher, A. Bohare, A. Boldyrev, C. Bolognani, R. Bolzonella, R. B. Bonacci, N. Bondar, A. Bordelius, F. Borgato, S. Borghi, M. Borsato, J. T. Borsuk, E. Bottalico, S. A. Bouchiba, M. Bovill, T. J. V. Bowcock, A. Boyer, C. Bozzi, J. D. Brandenburg, A. Brea Rodriguez, N. Breer, J. Brodzicka, J. Brown, D. Brundu, E. Buchanan, M. Burgos Marcos, A. T. Burke, C. Burr, C. Buti, J. S. Butter, J. Buytaert, W. Byczynski, S. Cadeddu, H. Cai, Y. Cai, A. Caillet, R. Calabrese, S. Calderon Ramirez, L. Calefice, M. Calvi, M. Calvo Gomez, P. Camargo Magalhaes, J. I. Cambon Bouzas, P. Campana, A. F. Campoverde Quezada, S. Capelli, M. Caporale, L. Capriotti, R. Caravaca-Mora, A. Carbone, L. Carcedo Salgado, R. Cardinale, A. Cardini, P. Carniti, L. Carus, A. Casais Vidal, R. Caspary, G. Casse, M. Cattaneo, G. Cavallero, V. Cavallini, S. Celani, I. Celestino, S. Cesare, A. J. Chadwick, I. Chahrour, H. Chang, M. Charles, Ph. Charpentier, E. Chatzianagnostou, R. Cheaib, M. Chefdeville, C. Chen, J. Chen, S. Chen, Z. Chen, A. Chen Hu, M. Cherif, A. Chernov, S. Chernyshenko, X. Chiotopoulos, V. Chobanova, M. Chrzaszcz, A. Chubykin, V. Chulikov, P. Ciambrone, X. Cid Vidal, G. Ciezarek, P. Cifra, P. E. L. Clarke, M. Clemencic, H. V. Cliff, J. Closier, C. Cocha Toapaxi, V. Coco, J. Cogan, E. Cogneras, L. Cojocariu, S. Collaviti, P. Collins, T. Colombo, M. Colonna, A. Comerma-Montells, L. Congedo, J. Connaughton, A. Contu, N. Cooke, G. Cordova, C. Coronel, I. Corredoira, A. Correia, G. Corti, J. Cottee Meldrum, B. Couturier, D. C. Craik, M. Cruz Torres, E. Curras Rivera, R. Currie, C. L. Da Silva, S. Dadabaev, L. Dai, X. Dai, E. Dall'Occo, J. Dalseno, C. D'Ambrosio, J. Daniel, G. Darze, A. Davidson, J. E. Davies, O. De Aguiar Francisco, C. De Angelis, F. De Benedetti, J. de Boer, K. De Bruyn, S. De Capua, M. De Cian, U. De Freitas Carneiro Da Graca, E. De Lucia, J. M. De Miranda, L. De Paula, M. De Serio, P. De Simone, F. De Vellis, J. A. de Vries, F. Debernardis, D. Decamp, S. Dekkers, L. Del Buono, B. Delaney, H. -P. Dembinski, J. Deng, V. Denysenko, O. Deschamps, F. Dettori, B. Dey, P. Di Nezza, I. Diachkov, S. Didenko, S. Ding, Y. Ding, L. Dittmann, V. Dobishuk, A. D. Docheva, A. Doheny, C. Dong, A. M. Donohoe, F. Dordei, A. C. dos Reis, A. D. Dowling, L. Dreyfus, W. Duan, P. Duda, L. Dufour, V. Duk, P. Durante, M. M. Duras, J. M. Durham, O. D. Durmus, A. Dziurda, A. Dzyuba, S. Easo, E. Eckstein, U. Egede, A. Egorychev, V. Egorychev, S. Eisenhardt, E. Ejopu, L. Eklund, M. Elashri, J. Ellbracht, S. Ely, A. Ene, J. Eschle, S. Esen, T. Evans, F. Fabiano, S. Faghih, L. N. Falcao, B. Fang, R. Fantechi, L. Fantini, M. Faria, K. Farmer, D. Fazzini, L. Felkowski, M. Feng, M. Feo, A. Fernandez Casani, M. Fernandez Gomez, A. D. Fernez, F. Ferrari, F. Ferreira Rodrigues, M. Ferrillo, M. Ferro-Luzzi, S. Filippov, R. A. Fini, M. Fiorini, M. Firlej, K. L. Fischer, D. S. Fitzgerald, C. Fitzpatrick, T. Fiutowski, F. Fleuret, A. Fomin, M. Fontana, L. A. Foreman, R. Forty, D. Foulds-Holt, V. Franco Lima, M. Franco Sevilla, M. Frank, E. Franzoso, G. Frau, C. Frei, D. A. Friday, J. Fu, Q. Führing, T. Fulghesu, G. Galati, M. D. Galati, A. Gallas Torreira, D. Galli, S. Gambetta, M. Gandelman, P. Gandini, B. Ganie, H. Gao, R. Gao, T. Q. Gao, Y. Gao, Y. Gao, Y. Gao, L. M. Garcia Martin, P. Garcia Moreno, J. García Pardiñas, P. Gardner, L. Garrido, C. Gaspar, A. Gavrikov, L. L. Gerken, E. Gersabeck, M. Gersabeck, T. Gershon, S. Ghizzo, Z. Ghorbanimoghaddam, F. I. Giasemis, V. Gibson, H. K. Giemza, A. L. Gilman, M. Giovannetti, A. Gioventù, L. Girardey, M. A. Giza, F. C. Glaser, V. V. Gligorov, C. Göbel, L. Golinka-Bezshyyko, E. Golobardes, D. Golubkov, A. Golutvin, S. Gomez Fernandez, W. Gomulka, I. Gonçales Vaz, F. Goncalves Abrantes, M. Goncerz, G. Gong, J. A. Gooding, I. V. Gorelov, C. Gotti, E. Govorkova, J. P. Grabowski, L. A. Granado Cardoso, E. Graugés, E. Graverini, L. Grazette, G. Graziani, A. T. Grecu, N. A. Grieser, L. Grillo, S. Gromov, C. Gu, M. Guarise, L. Guerry, A. -K. Guseinov, E. Gushchin, Y. Guz, T. Gys, K. Habermann, T. Hadavizadeh, C. Hadjivasiliou, G. Haefeli, C. Haen, S. Haken, G. Hallett, P. M. Hamilton, J. Hammerich, Q. Han, X. Han, S. Hansmann-Menzemer, L. Hao, N. Harnew, T. H. Harris, M. Hartmann, S. Hashmi, J. He, A. Hedes, F. Hemmer, C. Henderson, R. Henderson, R. D. L. Henderson, A. M. Hennequin, K. Hennessy, L. Henry, J. Herd, P. Herrero Gascon, J. Heuel, A. Heyn, A. Hicheur, G. Hijano Mendizabal, J. Horswill, R. Hou, Y. Hou, D. C. Houston, N. Howarth, W. Hu, X. Hu, W. Hulsbergen, R. J. Hunter, M. Hushchyn, D. Hutchcroft, M. Idzik, D. Ilin, P. Ilten, A. Iniukhin, A. Iohner, A. Ishteev, K. Ivshin, H. Jage, S. J. Jaimes Elles, S. Jakobsen, T. Jakoubek, E. Jans, B. K. Jashal, A. Jawahery, C. Jayaweera, V. Jevtic, Z. Jia, E. Jiang, X. Jiang, Y. Jiang, Y. J. Jiang, E. Jimenez Moya, N. Jindal, M. John, A. John Rubesh Rajan, D. Johnson, C. R. Jones, S. Joshi, B. Jost, J. Juan Castella, N. Jurik, I. Juszczak, K. Kalecinska, D. Kaminaris, S. Kandybei, M. Kane, Y. Kang, C. Kar, M. Karacson, A. Kauniskangas, J. W. Kautz, M. K. Kazanecki, F. Keizer, M. Kenzie, T. Ketel, B. Khanji, A. Kharisova, S. Kholodenko, G. Khreich, T. Kirn, V. S. Kirsebom, O. Kitouni, S. Klaver, N. Kleijne, D. K. Klekots, K. Klimaszewski, M. R. Kmiec, T. Knospe, R. Kolb, S. Koliiev, L. Kolk, A. Konoplyannikov, P. Kopciewicz, P. Koppenburg, A. Korchin, M. Korolev, I. Kostiuk, O. Kot, S. Kotriakhova, E. Kowalczyk, A. Kozachuk, P. Kravchenko, L. Kravchuk, O. Kravcov, M. Kreps, P. Krokovny, W. Krupa, W. Krzemien, O. Kshyvanskyi, S. Kubis, M. Kucharczyk, V. Kudryavtsev, E. Kulikova, A. Kupsc, V. Kushnir, B. Kutsenko, J. Kvapil, I. Kyryllin, D. Lacarrere, P. Laguarta Gonzalez, A. Lai, A. Lampis, D. Lancierini, C. Landesa Gomez, J. J. Lane, G. Lanfranchi, C. Langenbruch, J. Langer, T. Latham, F. Lazzari, C. Lazzeroni, R. Le Gac, H. Lee, R. Lefèvre, A. Leflat, S. Legotin, M. Lehuraux, E. Lemos Cid, O. Leroy, T. Lesiak, E. D. Lesser, B. Leverington, A. Li, C. Li, C. Li, H. Li, J. Li, K. Li, L. Li, M. Li, P. Li, P. -R. Li, Q. Li, T. Li, T. Li, Y. Li, Y. Li, Y. Li, Z. Lian, Q. Liang, X. Liang, Z. Liang, S. Libralon, A. Lightbody, C. Lin, T. Lin, R. Lindner, H. Linton, R. Litvinov, D. Liu, F. L. Liu, G. Liu, K. Liu, S. Liu, W. Liu, Y. Liu, Y. Liu, Y. L. Liu, G. Loachamin Ordonez, A. Lobo Salvia, A. Loi, T. Long, F. C. L. Lopes, J. H. Lopes, A. Lopez Huertas, C. Lopez Iribarnegaray, S. López Soliño, Q. Lu, C. Lucarelli, D. Lucchesi, M. Lucio Martinez, Y. Luo, A. Lupato, E. Luppi, K. Lynch, X. -R. Lyu, G. M. Ma, H. Ma, S. Maccolini, F. Machefert, F. Maciuc, B. Mack, I. Mackay, L. M. Mackey, L. R. Madhan Mohan, M. J. Madurai, D. Magdalinski, D. Maisuzenko, J. J. Malczewski, S. Malde, L. Malentacca, A. Malinin, T. Maltsev, G. Manca, G. Mancinelli, C. Mancuso, R. Manera Escalero, F. M. Manganella, D. Manuzzi, D. Marangotto, J. F. Marchand, R. Marchevski, U. Marconi, E. Mariani, S. Mariani, C. Marin Benito, J. Marks, A. M. Marshall, L. Martel, G. Martelli, G. Martellotti, L. Martinazzoli, M. Martinelli, D. Martinez Gomez, D. Martinez Santos, F. Martinez Vidal, A. Martorell i Granollers, A. Massafferri, R. Matev, A. Mathad, V. Matiunin, C. Matteuzzi, K. R. Mattioli, A. Mauri, E. Maurice, J. Mauricio, P. Mayencourt, J. Mazorra de Cos, M. Mazurek, M. McCann, N. T. McHugh, A. McNab, R. McNulty, B. Meadows, G. Meier, D. Melnychuk, D. Mendoza Granada, P. Menendez Valdes Perez, F. M. Meng, M. Merk, A. Merli, L. Meyer Garcia, D. Miao, H. Miao, M. Mikhasenko, D. A. Milanes, A. Minotti, E. Minucci, T. Miralles, B. Mitreska, D. S. Mitzel, R. Mocanu, A. Modak, L. Moeser, R. D. Moise, E. F. Molina Cardenas, T. Mombächer, M. Monk, S. Monteil, A. Morcillo Gomez, G. Morello, M. J. Morello, M. P. Morgenthaler, A. Moro, J. Moron, W. Morren, A. B. Morris, A. G. Morris, R. Mountain, H. Mu, Z. M. Mu, E. Muhammad, F. Muheim, M. Mulder, K. Müller, F. Muñoz-Rojas, R. Murta, V. Mytrochenko, P. Naik, T. Nakada, R. Nandakumar, T. Nanut, I. Nasteva, E. Nekrasova, N. Neri, S. Neubert, N. Neufeld, P. Neustroev, J. Nicolini, D. Nicotra, E. M. Niel, N. Nikitin, L. Nisi, Q. Niu, P. Nogarolli, P. Nogga, C. Normand, J. Novoa Fernandez, G. Nowak, C. Nunez, H. N. Nur, A. Oblakowska-Mucha, V. Obraztsov, T. Oeser, A. Okhotnikov, O. Okhrimenko, R. Oldeman, F. Oliva, E. Olivart Pino, M. Olocco, R. H. O'Neil, J. S. Ordonez Soto, D. Osthues, J. M. Otalora Goicochea, P. Owen, A. Oyanguren, O. Ozcelik, F. Paciolla, A. Padee, K. O. Padeken, B. Pagare, T. Pajero, A. Palano, L. Palini, M. Palutan, C. Pan, X. Pan, S. Panebianco, G. Panshin, L. Paolucci, A. Papanestis, M. Pappagallo, L. L. Pappalardo, C. Pappenheimer, C. Parkes, D. Parmar, G. Passaleva, D. Passaro, A. Pastore, M. Patel, J. Patoc, C. Patrignani, A. Paul, C. J. Pawley, A. Pellegrino, J. Peng, X. Peng, M. Pepe Altarelli, S. Perazzini, D. Pereima, H. Pereira Da Costa, M. Pereira Martinez, A. Pereiro Castro, C. Perez, P. Perret, A. Perrevoort, A. Perro, M. J. Peters, K. Petridis, A. Petrolini, S. Pezzulo, J. P. Pfaller, H. Pham, L. Pica, M. Piccini, L. Piccolo, B. Pietrzyk, G. Pietrzyk, R. N. Pilato, D. Pinci, F. Pisani, M. Pizzichemi, V. M. Placinta, M. Plo Casasus, T. Poeschl, F. Polci, M. Poli Lener, A. Poluektov, N. Polukhina, I. Polyakov, E. Polycarpo, S. Ponce, D. Popov, S. Poslavskii, K. Prasanth, C. Prouve, D. Provenzano, V. Pugatch, A. Puicercus Gomez, G. Punzi, J. R. Pybus, Q. Q. Qian, W. Qian, N. Qin, S. Qu, R. Quagliani, R. I. Rabadan Trejo, R. Racz, J. H. Rademacker, M. Rama, M. Ramírez García, V. Ramos De Oliveira, M. Ramos Pernas, M. S. Rangel, F. Ratnikov, G. Raven, M. Rebollo De Miguel, F. Redi, J. Reich, F. Reiss, Z. Ren, P. K. Resmi, M. Ribalda Galvez, R. Ribatti, G. Ricart, D. Riccardi, S. Ricciardi, K. Richardson, M. Richardson-Slipper, F. Riehn, K. Rinnert, P. Robbe, G. Robertson, E. Rodrigues, A. Rodriguez Alvarez, E. Rodriguez Fernandez, J. A. Rodriguez Lopez, E. Rodriguez Rodriguez, J. Roensch, A. Rogachev, A. Rogovskiy, D. L. Rolf, P. Roloff, V. Romanovskiy, A. Romero Vidal, G. Romolini, F. Ronchetti, T. Rong, M. Rotondo, S. R. Roy, M. S. Rudolph, M. Ruiz Diaz, R. A. Ruiz Fernandez, J. Ruiz Vidal, J. J. Saavedra-Arias, J. J. Saborido Silva, S. E. R. Sacha Emile R., N. Sagidova, D. Sahoo, N. Sahoo, B. Saitta, M. Salomoni, I. Sanderswood, R. Santacesaria, C. Santamarina Rios, M. Santimaria, L. Santoro, E. Santovetti, A. Saputi, D. Saranin, A. Sarnatskiy, G. Sarpis, M. Sarpis, C. Satriano, A. Satta, M. Saur, D. Savrina, H. Sazak, F. Sborzacchi, A. Scarabotto, S. Schael, S. Scherl, M. Schiller, H. Schindler, M. Schmelling, B. Schmidt, N. Schmidt, S. Schmitt, H. Schmitz, O. Schneider, A. Schopper, N. Schulte, M. H. Schune, G. Schwering, B. Sciascia, A. Sciuccati, G. Scriven, I. Segal, S. Sellam, A. Semennikov, T. Senger, M. Senghi Soares, A. Sergi, N. Serra, L. Sestini, A. Seuthe, B. Sevilla Sanjuan, Y. Shang, D. M. Shangase, M. Shapkin, R. S. Sharma, I. Shchemerov, L. Shchutska, T. Shears, L. Shekhtman, Z. Shen, S. Sheng, V. Shevchenko, B. Shi, Q. Shi, W. S. Shi, Y. Shimizu, E. Shmanin, R. Shorkin, J. D. Shupperd, R. Silva Coutinho, G. Simi, S. Simone, M. Singha, N. Skidmore, T. Skwarnicki, M. W. Slater, E. Smith, K. Smith, M. Smith, L. Soares Lavra, M. D. Sokoloff, F. J. P. Soler, A. Solomin, A. Solovev, K. Solovieva, N. S. Sommerfeld, R. Song, Y. Song, Y. Song, Y. S. Song, F. L. Souza De Almeida, B. Souza De Paula, K. M. Sowa, E. Spadaro Norella, E. Spedicato, J. G. Speer, P. Spradlin, F. Stagni, M. Stahl, S. Stahl, S. Stanislaus, M. Stefaniak, E. N. Stein, O. Steinkamp, D. Strekalina, Y. Su, F. Suljik, J. Sun, J. Sun, L. Sun, D. Sundfeld, W. Sutcliffe, P. Svihra, V. Svintozelskyi, K. Swientek, F. Swystun, A. Szabelski, T. Szumlak, Y. Tan, Y. Tang, Y. T. Tang, M. D. Tat, J. A. Teijeiro Jimenez, A. Terentev, F. Terzuoli, F. Teubert, E. Thomas, D. J. D. Thompson, A. R. Thomson-Strong, H. Tilquin, V. Tisserand, S. T'Jampens, M. Tobin, T. T. Todorov, L. Tomassetti, G. Tonani, X. Tong, T. Tork, D. Torres Machado, L. Toscano, D. Y. Tou, C. Trippl, G. Tuci, N. Tuning, L. H. Uecker, A. Ukleja, D. J. Unverzagt, A. Upadhyay, B. Urbach, A. Usachov, A. Ustyuzhanin, U. Uwer, V. Vagnoni, V. Valcarce Cadenas, G. Valenti, N. Valls Canudas, J. van Eldik, H. Van Hecke, E. van Herwijnen, C. B. Van Hulse, R. Van Laak, M. van Veghel, G. Vasquez, R. Vazquez Gomez, P. Vazquez Regueiro, C. Vázquez Sierra, S. Vecchi, J. Velilla Serna, J. J. Velthuis, M. Veltri, A. Venkateswaran, M. Verdoglia, M. Vesterinen, W. Vetens, D. Vico Benet, P. Vidrier Villalba, M. Vieites Diaz, X. Vilasis-Cardona, E. Vilella Figueras, A. Villa, P. Vincent, B. Vivacqua, F. C. Volle, D. vom Bruch, N. Voropaev, K. Vos, C. Vrahas, J. Wagner, J. Walsh, E. J. Walton, G. Wan, A. Wang, B. Wang, C. Wang, G. Wang, H. Wang, J. Wang, J. Wang, J. Wang, J. Wang, M. Wang, N. W. Wang, R. Wang, X. Wang, X. Wang, X. W. Wang, Y. Wang, Y. Wang, Y. H. Wang, Z. Wang, Z. Wang, J. A. Ward, M. Waterlaat, N. K. Watson, D. Websdale, Y. Wei, Z. Weida, J. Wendel, B. D. C. Westhenry, C. White, M. Whitehead, E. Whiter, A. R. Wiederhold, D. Wiedner, M. A. Wiegertjes, C. Wild, G. Wilkinson, M. K. Wilkinson, M. Williams, M. J. Williams, M. R. J. Williams, R. Williams, S. Williams, Z. Williams, F. F. Wilson, M. Winn, W. Wislicki, M. Witek, L. Witola, T. Wolf, E. Wood, G. Wormser, S. A. Wotton, H. Wu, J. Wu, X. Wu, Y. Wu, Z. Wu, K. Wyllie, S. Xian, Z. Xiang, Y. Xie, T. X. Xing, A. Xu, L. Xu, L. Xu, M. Xu, Z. Xu, Z. Xu, Z. Xu, S. Yadav, K. Yang, X. Yang, Y. Yang, Y. Yang, Z. Yang, V. Yeroshenko, H. Yeung, H. Yin, X. Yin, C. Y. Yu, J. Yu, X. Yuan, Y Yuan, E. Zaffaroni, J. A. Zamora Saa, M. Zavertyaev, M. Zdybal, F. Zenesini, C. Zeng, M. Zeng, C. Zhang, D. Zhang, J. Zhang, L. Zhang, R. Zhang, S. Zhang, S. L. Zhang, Y. Zhang, Y. Z. Zhang, Z. Zhang, Y. Zhao, A. Zhelezov, S. Z. Zheng, X. Z. Zheng, Y. Zheng, T. Zhou, X. Zhou, Y. Zhou, V. Zhovkovska, L. Z. Zhu, X. Zhu, X. Zhu, Y. Zhu, V. Zhukov, J. Zhuo, Q. Zou, D. Zuliani, G. Zunica Published: 11.20.2025 Updated: 11.20.2025 Summary The normalised decay rate of $B^+ to J/ψ(to μ^+μ^-) K^+$ is measured as a function of the lepton helicity angle using a data sample corresponding to an integrated luminosity of $1.1 text{fb}^{-1}$ collected during October 2024 with the upgraded (Upgrade I) LHCb detector. This angular distribution can be parameterised by two coefficients, the forward-backward asymmetry, $A_{FB}$, and the flatness parameter, $F_{H}$, whose values are constrained by conservation of angular momentum. These coefficients are measured both integrated and differentially across various kinematic and detector-response variables, and the results are found to be in good agreement with expectations. These measurements show that the detector response of the LHCb Upgrade I experiment is understood to the precision required to reliably extract the angular coefficients associated with rare $b to s μ^+μ^-$ and $b to d μ^+μ^-$ transitions, which are particularly sensitive to physics beyond the Standard Model. Source arXiv: 2511.16564v1
Search for the charmonium weak decay $J/ψtobar{D}^0bar{K}^{*0}+{rm c.c.}$ Authors BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, X. Y. Chen, Y. B. Chen, Y. Q. Chen, Y. Q. Chen, Z. J. Chen, Z. K. Chen, J. C. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. Cottee-Meldrum, J. J. Cui, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, L. Feng, Q. X. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Y. Gao, Y. N. Gao, Y. N. Gao, Y. Y. Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, C. Z. He, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. J. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, C. K. Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, K. L. Li, L. J. Li, Lei Li, M. H. Li, M. R. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. X. Li, Shanshan Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. K. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. G. Li, Y. P. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, L. Q. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Liu, K. Y. Liu, Ke Liu, L. C. Liu, Lu Liu, M. H. Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, W. T. Liu, X. Liu, X. Liu, X. K. Liu, X. L. Liu, X. Y. Liu, Y. Liu, Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, F. C. Ma, H. L. Ma, Heng Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Q. A. Malik, H. X. Mao, Y. J. Mao, Z. P. Mao, S. Marcello, A. Marshall, F. M. Melendi, Y. H. Meng, Z. X. Meng, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, W. D. Niu, C. Normand, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, X. J. Peng, K. Peters, K. Petridis, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. R. Qi, M. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, J. Rademacker, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, J. L. Shi, J. Y. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, Zirong Song, S. Sosio, S. Spataro, S Stansilaus, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, J. J. Tang, L. F. Tang, Y. A. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, B. Wang, B. Wang, Bo Wang, C. Wang, C. Wang, Cong Wang, D. Y. Wang, H. J. Wang, J. J. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, M. Wang, N. Y. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Xin Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. J. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. L. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, D. H. Wei, H. R. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. Wu, X. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, T. D. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, H. Y. Yan, L. Yan, W. B. Yan, W. C. Yan, W. H. Yan, W. P. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, R. J. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. H. Yang, Y. Q. Yang, Y. X. Yang, Y. Z. Yang, M. Ye, M. H. Ye, Z. J. Ye, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, L. Q. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, H. Yuan, J. Yuan, J. Yuan, L. Yuan, S. C. Yuan, S. H. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, Y. J. Zeng, X. Y. Zhai, Y. H. Zhan, Zhang, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, G. Y. Zhang, H. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Y. P. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Z. Zhang, Zh. Zh. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, L. Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, H. Zhou, J. Q. Zhou, J. Y. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. X. Zhou, Y. Z. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, J. H. Zou, J. Zu Published: 11.20.2025 Updated: 11.20.2025 Summary Based on a sample of $(10087pm44)times10^6$ $J/ψ$ events collected at the center-of-mass energy $sqrt{s}$ = 3.0969 GeV with the BESIII detector, we search for the charmonium rare weak decay $J/ψtobar{D}^0bar{K}^{*0}+{rm c.c.}$. No significant signal is observed, and the upper limit on its decay branching fraction at the 90% confidence level is set as $1.9times10^{-7}$, improving the sensitivity of the previous best limit by an order of magnitude. Source arXiv: 2511.16083v1
Cosmological Constraints on the Phenomenological Interacting Dark Energy Model with Fermi Gamma-Ray Bursts and DESI DR2 Authors Ziyan Zhu, Qingquan Jiang, Yu Liu, Puxun Wu, Nan Liang Published: 11.20.2025 Updated: 11.20.2025 Summary In this work, we constrain the phenomenological interacting dark energy (IDE) model using emph{Fermi} gamma-ray burst (GRB) dataset and the latest baryon acoustic oscillation (BAO) data from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2). Through a joint Bayesian analysis, we perform a cosmological comparative assessment of the $Λ$CDM, $w$CDM, and CPL models with the phenomenological IDE model. For the phenomenological IDE model in a flat universe, we obtain: $ξ=2.644^{+1.144}_{-0.933}$, $ξ+ 3w_X = -1.001^{+2.827}_{-3.153}$ with the GOLD sample ($z geq 1.4$) and DESI DR2; and $ξ=2.879^{+0.995}_{-0.990}$, $ξ+ 3w_X = 0.086^{+1.365}_{-1.440}$ with the FULL sample ($z geq 1.4$) and DESI DR2. Our analysis shows that the $Λ$CDM model without interaction are consistent with the latest emph{Fermi} sample and DESI DR2 at $1σ$ confidence level. Source arXiv: 2511.16032v1
A Scalable NorthPole System with End-to-End Vertical Integration for Low-Latency and Energy-Efficient LLM Inference Authors Michael V. DeBole, Rathinakumar Appuswamy, Neil McGlohon, Brian Taba, Steven K. Esser, Filipp Akopyan, John V. Arthur, Arnon Amir, Alexander Andreopoulos, Peter J. Carlson, Andrew S. Cassidy, Pallab Datta, Myron D. Flickner, Rajamohan Gandhasri, Guillaume J. Garreau, Megumi Ito, Jennifer L. Klamo, Jeffrey A. Kusnitz, Nathaniel J. McClatchey, Jeffrey L. McKinstry, Tapan K. Nayak, Carlos Ortega Otero, Hartmut Penner, William P. Risk, Jun Sawada, Jay Sivagnaname, Daniel F. Smith, Rafael Sousa, Ignacio Terrizzano, Takanori Ueda, Trent Gray-Donald, David Cox, Dharmendra S. Modha Published: 11.20.2025 Updated: 11.20.2025 Summary A vertically integrated, end-to-end, research prototype system combines 288 NorthPole neural inference accelerator cards, offline training algorithms, a high-performance runtime stack, and a containerized inference pipeline to deliver a scalable and efficient cloud inference service. The system delivers 115 peta-ops at 4-bit integer precision and 3.7 PB/s of memory bandwidth across 18 2U servers, while consuming only 30 kW of power and weighing 730 kg in a 0.67 m^2 42U rack footprint. The system can run 3 simultaneous instances of the 8-billion-parameter open-source IBM Granite-3.3-8b-instruct model at 2,048 context length with 28 simultaneous users and a per-user inter-token latency of 2.8 ms. The system is scalable, modular, and reconfigurable, supporting various model sizes and context lengths, and is ideal for deploying agentic workflows for enterprise AI applications in existing data center (cloud, on-prem) environments. For example, the system can support 18 instances of a 3-billion-parameter model or a single instance of a 70-billion-parameter model. Source arXiv: 2511.15950v1
