Showing 1–50 of 2,929 results for author: Xia, Z

Large Scale Entanglement Structure Detection in 100-Qubit Systems via Local Joint Measurements

Authors: Rui Li, Yuhang Wang, Chunxiao Du, Shikun Zhang, Zheng Qin, Wenxiu Li, Hao Zhang, Zhisong Xiao

Abstract: Identifying the entanglement structure of a many-body quantum state, namely how its constituents partition into unentangled blocks, is a central task in quantum information science, yet conventional tomography scales exponentially with system size. Here we introduce a scalable framework that recognizes large-scale entanglement structures directly from local correlation fingerprints. By choosing a… ▽ More Identifying the entanglement structure of a many-body quantum state, namely how its constituents partition into unentangled blocks, is a central task in quantum information science, yet conventional tomography scales exponentially with system size. Here we introduce a scalable framework that recognizes large-scale entanglement structures directly from local correlation fingerprints. By choosing a representative local Pauli basis that satisfies a boundary-matching condition p_1 = p_R, the entire chain is read out in a single measurement configuration, keeping the measurement effort independent of system size. In noisy simulations, this single-basis protocol classifies GHZ-, W-, and cluster-type structures among 30 candidate partitions with a mean accuracy exceeding 95% for systems of up to 100 qubits. We further validate the protocol on a superconducting quantum processor, where it reliably classifies block structures for systems of up to 13 qubits before noise- and depth-induced degradation sets in at larger sizes. By mapping these failure modes explicitly, our results delineate the boundary of hardware-level scalability and point to a concrete strategy for characterizing entanglement structure on near-term quantum devices. △ Less

Submitted 20 August, 2026; originally announced August 2026.

Beyond Placement and Articulation: Usage-Driven Code Scenes for Embodied Interaction

Authors: Zijian Xiao, Zipeng Ye, Jinkun Hao, Xiong Yang, Yuchen Xie, Ran Yi

Abstract: Indoor scene synthesis provides essential environments for embodied AI, robotic manipulation, and simulation-based policy learning. Recent code-based scene generation methods produce editable and extensible environments, yet they remain focused on visual construction and object-level articulation, leaving the functional usage of scenes largely unmodeled. To address this problem, we present RoomWri… ▽ More Indoor scene synthesis provides essential environments for embodied AI, robotic manipulation, and simulation-based policy learning. Recent code-based scene generation methods produce editable and extensible environments, yet they remain focused on visual construction and object-level articulation, leaving the functional usage of scenes largely unmodeled. To address this problem, we present RoomWright, an agentic usage-driven framework for generating 3D scenes represented entirely as code for embodied interaction. RoomWright performs usage-driven object reasoning, which treats each anchor as a task centre and admits task-required objects and their affordances. A code agent further enables multi-part interaction by compiling each interaction into a trigger, condition, effect rule that updates structured object states, capturing causal dependencies across objects. Moreover, since manipuland orientation is ambiguous and hard to recover from pixels, RoomWright alleviates this via annotation-informed usage-guided orientation. Extensive experiments demonstrate the effectiveness of our method. The resulting scenes are executable, editable, and simulation-ready, providing interactive environments for embodied AI and policy learning. △ Less

Submitted 19 August, 2026; originally announced August 2026.

PILOT Technical Report

Authors: Jiuning Lin, Ruiquan Lan, Xiaodong Zhu, Bin Zhang, Chengyu Lai, Chuxin Chen, Dimin Wang, Han Zhu, Hongtao Cheng, Jialin Zhu, Lingqing Zhang, Shuai Zhong, Tao Wang, Weipeng Huang, Yinjiang Cai, Yinnan Song, Yuan Liu, Zhibo Xiao, Zhixin Ma, Zihong Huang

Abstract: Existing agentic approaches for recommendation system optimization remain fundamentally reactive: they adjust parameters in response to observed metric changes but lack the ability to proactively design controlled experiments, personalize strategies at the user-segment level, or accumulate reusable experimental methodology across tasks. We present PILOT (Proactive Insight Learner for Online Tree-E… ▽ More Existing agentic approaches for recommendation system optimization remain fundamentally reactive: they adjust parameters in response to observed metric changes but lack the ability to proactively design controlled experiments, personalize strategies at the user-segment level, or accumulate reusable experimental methodology across tasks. We present PILOT (Proactive Insight Learner for Online Tree-Experiments), an LLM-agent framework that organizes three roles within a constrained control loop where deterministic services enforce all safety, statistical, and permission boundaries: (1) an Experiment Manager that drives the full experiment lifecycle -- task intake, observation governance, anomaly recovery, and postmortem -- by selecting only from a rule-generated legal-command envelope; (2) a Search Planner that proposes candidate decision trees for user-segment-level personalization, invoked only when the Manager requests planning; and (3) a Memory Curator that asynchronously distills experiment outcomes into strategy-level domain knowledge and provenance-tracked methodology, failure-isolated from the main loop. The Manager makes the agent proactive, the Planner enables population-level personalization beyond global tuning, and the Curator turns every completed task into a learning opportunity for the next. Deployed on Taobao's platform with 5 experimental buckets, PILOT is compared against ROAM(Reactive Optimization with Agent-driven Moves), a free-exploration agent without lifecycle governance or structured hypothesis testing. PILOT achieves up to +1.40% IPV, +1.60% Core IPV, +0.96% transaction count, and +1.50% transaction amount, improving over ROAM's best results (+1.00% IPV, +0.90% Core IPV, +0.60% transaction count, +1.13% transaction amount) while raising search efficiency from 53.3% to 93.3% (+40 pp), with no human intervention throughout the experimental cycle. △ Less

Submitted 19 August, 2026; v1 submitted 19 August, 2026; originally announced August 2026.

Comments: Technical Report, 42 pages, 10 figures

Reconfiguration-Complete Motion Primitives with Constructive Planning for Deformable Planar Modular Robots

Authors: Jie Gu, Tingting Wang, Hongrun Gao, Yirun Sun, Zhihao Xia, Chunxu Tian, Dan Zhang

Abstract: The continuously deformable geometry of modular robots makes it difficult to define a fixed representation for reconfiguration planning and analysis. This letter introduces a square-cell abstraction that maps deformable rhombus modules to fixed-size grid cells while retaining physically interpretable local motions through two primitives, pivoting and shearing. Under this abstraction, we prove that… ▽ More The continuously deformable geometry of modular robots makes it difficult to define a fixed representation for reconfiguration planning and analysis. This letter introduces a square-cell abstraction that maps deformable rhombus modules to fixed-size grid cells while retaining physically interpretable local motions through two primitives, pivoting and shearing. Under this abstraction, we prove that every non-straight edge-connected configuration with $N \geq 7$ can be transformed to a fixed canonical staircase using only admissible primitive motions. Since these motions are reversible, any two configurations in this class are mutually reconfigurable. The proof is constructive and directly yields a staircase-canonicalization planner that transports removable boundary modules while preserving connectivity. As a practical enhancement, we further introduce a boundary-to-delivery lookahead selector that ranks admissible high level choices without affecting the completeness guarantee. Experiments demonstrate the constructive reconfiguration process and show that the selector substantially reduces planning time, while reference comparisons indicate lower planning times than the prior framework over the shared module counts. △ Less

Submitted 17 August, 2026; originally announced August 2026.

Comments: Jie Gu and Tingting Wang contributed equally to this work

Channel Knowledge Map Enabled Low-Complexity Dynamic Radio Environment Reconstruction

Authors: Yujun Lin, Zhiqiang Xiao, Hao Wu, Xiaoqiang Qiao, Fayu Wan, Tao Zhang

Abstract: Accurate and timely radio environment reconstruction is important but challenging under particularly dynamic transmitter configurations. The conventional methods such as compressed sensing (CS), Kriging method or U-Net typically require environment measurements and reconstruction overhead for radio environment updating as the transmitter locations or radiation patterns change. In this paper, we pr… ▽ More Accurate and timely radio environment reconstruction is important but challenging under particularly dynamic transmitter configurations. The conventional methods such as compressed sensing (CS), Kriging method or U-Net typically require environment measurements and reconstruction overhead for radio environment updating as the transmitter locations or radiation patterns change. In this paper, we propose a novel channel knowledge map (CKM)-enabled dynamic radio environment reconstruction method for efficient radio map updating. Specifically, the recently proposed CKM can store reusable path-level propagation knowledge that is decoupled from the transmitter-side radiation characteristics. We can leverage CKM for lightweight forward radio map generation as the transmitter locations and radiation patterns are known, without requiring new target-map measurements. Simulation results show that the proposed method outperforms CS, Kriging, and U-Net in reconstruction accuracy and exhibits strong robustness performance under dynamic transmitter configurations, which demonstrates the potential of the proposed method for flexible and efficient radio environment reconstruction in dynamic wireless networks. △ Less

Submitted 17 August, 2026; originally announced August 2026.

Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL

Authors: Yunhao Yang, Yuexin Bian, Yunjie Tian, Di Fu, Tianjin Huang, Yuanyuan Shi, Ziang Xiao, Nuno Vasconcelos, Yijiang Li

Abstract: Reinforcement learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its strongest successes still depend heavily on ground-truth supervision (e.g., verifiable reward). Such annotations are costly to obtain and become increasingly scarce as reasoning capabilities advance beyond what humans can reliably evaluate. Self-rewarding RL reduce… ▽ More Reinforcement learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its strongest successes still depend heavily on ground-truth supervision (e.g., verifiable reward). Such annotations are costly to obtain and become increasingly scarce as reasoning capabilities advance beyond what humans can reliably evaluate. Self-rewarding RL reduces this dependence by enabling models to derive reward signals from their own completions. However, training solely on self-generated feedback can reinforce existing biases and suboptimal behaviors, reduce response diversity, and ultimately lead to homogenized responses and training collapse. In this work, we show that unsupervised reasoning can emerge through cooperative multi-agent training. We introduce Co-RL, a framework in which multiple decoupled models, sharing no parameters, are simultaneously optimized through RL using rewards derived from their peers. We further show that increasing cohort diversity, through heterogeneous model families, sizes, and rephrased training samples, reduces the correlated errors that drive self-reinforcing feedback loops. This diversity consistently improves reasoning performance, maintains behavioral diversity, and mitigates training collapse. Across text-only and multimodal domains, Co-RL consistently outperforms the base models and prior label-free approaches, while matching or surpassing supervised methods, without access to any ground-truth labels. Concretely, Co-RL yields average gains of 3.0-8.6% across seven text-only benchmarks for LLMs and 2.3-7.2% across four multimodal benchmarks for VLMs. Code is available at https://github.com/DrStranded/Co-RL. △ Less

Submitted 19 August, 2026; v1 submitted 17 August, 2026; originally announced August 2026.

Comments: 30 pages, 5 figures, 11 tables

DPNet: Efficient Dead-End Prediction and Avoidance for Vision-Based UAV Navigation

Authors: Ruibin Zhang, Lun Pan, Zelong Xia, Jialiang Hou, Fei Gao

Abstract: Vision-based Unmanned Aerial Vehicles (UAVs) often suffer from navigation failures in dead ends due to limited sensing accuracy and range. To address this challenge, this paper proposes a systematic solution for efficient dead-end prediction and avoidance. The proposed method introduces a lightweight neural network to predict the relative distance and bearing of potential dead ends within the curr… ▽ More Vision-based Unmanned Aerial Vehicles (UAVs) often suffer from navigation failures in dead ends due to limited sensing accuracy and range. To address this challenge, this paper proposes a systematic solution for efficient dead-end prediction and avoidance. The proposed method introduces a lightweight neural network to predict the relative distance and bearing of potential dead ends within the current field of view using RGB-D inputs. These predictions prune a predefined, compact trajectory library, enabling the planner to proactively avoid dead ends while maintaining navigational smoothness. Notably, our approach transfers across real-world scenarios without manual annotation or fine-tuning on real-world data. The system achieves high-frequency replanning at 50 Hz onboard. Extensive simulation benchmarks demonstrate superior performance in success rate, flight time, and trajectory length, and real-world experiments further validate its effectiveness in complex scenarios. △ Less

Submitted 17 August, 2026; originally announced August 2026.

A Shop Floor Production Scheduling Case based on RFID-supported Smart Factory

Authors: Zhihui Chen, Yize Sun, Yuhao Dong, Zeyu Xiao, Ray Y. Zhong

Abstract: Radio frequency identification (RFID) technology has been widely implemented for real-time data collection in manufacturing shop floors, which, in turn, can be used to support dynamic shop floor production planning and scheduling. Within such an environment, uncertainty in operation and production processes collectively contribute to the dynamicity in manufacturing, thereby hampering the schedulin… ▽ More Radio frequency identification (RFID) technology has been widely implemented for real-time data collection in manufacturing shop floors, which, in turn, can be used to support dynamic shop floor production planning and scheduling. Within such an environment, uncertainty in operation and production processes collectively contribute to the dynamicity in manufacturing, thereby hampering the scheduling system from achieving maximal utility. To highlight the importance of handling such uncertainty, this paper addresses the problem of dynamic shop floor scheduling for a real-life case smart factory equipped with RFID technology. Feasible production sequence mining and real-time processing rate estimation are conducted on RFID-collected production data to quantify the operation and production uncertainties. A deep reinforcement learning approach based on the RFID data analysis is then presented for shop floor production scheduling. Simulation studies based on real-life case data have demonstrated the feasibility and practicality of the proposed dynamic production scheduling framework. Specifically, it is observed that the proposed framework outperforms existing dispatch methods in terms of minimizing operation makespan, including first in first out (FIFO), last in first out (LIFO) and deep Q network (DQN). △ Less

Submitted 17 August, 2026; originally announced August 2026.

First measurements of the branching fractions of $J/ψ$ and $ψ(3686) \to Σ^{0} \barΣ^{0}η$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko , et al. (750 additional authors not shown)

Abstract: Based on $(10087 \pm 44) \times 10^6$ $J/ψ$ and $(2712 \pm 14) \times 10^6$ $ψ(3686)$ events collected with the BESIII detector at the BEPCII collider, the hadronic decays $J/ψ\to Σ^{0} \barΣ^{0} η$ and $ψ(3686) \to Σ^{0} \barΣ^{0} η$ are observed for the first time. The corresponding branching fractions are measured to be… ▽ More Based on $(10087 \pm 44) \times 10^6$ $J/ψ$ and $(2712 \pm 14) \times 10^6$ $ψ(3686)$ events collected with the BESIII detector at the BEPCII collider, the hadronic decays $J/ψ\to Σ^{0} \barΣ^{0} η$ and $ψ(3686) \to Σ^{0} \barΣ^{0} η$ are observed for the first time. The corresponding branching fractions are measured to be $\mathcal{B}(J/ψ\to Σ^{0} \barΣ^{0}η)= (7.5 \pm 0.3 \pm 0.8) \times 10^{-5}$ and $\mathcal{B}(ψ(3686) \to Σ^{0} \barΣ^{0}η)= (1.3\pm 0.1 \pm 0.1) \times 10^{-5}$, respectively, where the first uncertainties are statistical, and the second systematic. The ratio $\text{Q} \approx \frac{\mathcal{B}(ψ(3686) \to Σ^{0} \barΣ^{0} η)}{\mathcal{B}(J/ψ\to Σ^{0} \barΣ^{0} η)}$ is determined to be $(17.3 \pm 1.5 \pm 1.7)\%$, which is con sistent with the 12\%-rule within 3.0$σ$.~No significant intermediate states or threshold enhancements are observed in the $Σ^0$($\barΣ^{0}$)$η$ and $Σ^0$$\barΣ^{0}$ invariant mass spectra. △ Less

Submitted 17 August, 2026; originally announced August 2026.

Measurement of Branching Fraction and Transition Magnetic Moment of the Hyperon Dalitz Decay $Σ^0 \rightarrow Λe^+e^-$

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. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai , et al. (683 additional authors not shown)

Abstract: Based on a data sample of 10 billion $J/ψ$ events collected with the BESIII detector operating at the BEPCII collider, the Dalitz decay $Σ^0 \rightarrow Λe^+e^-$ is studied experimentally for the first time. The $Σ^0$ hyperons are produced through the process $J/ψ\rightarrow Σ^0\barΣ^0$ and analyzed using a double-tag method. The absolute branching fraction is measured to be… ▽ More Based on a data sample of 10 billion $J/ψ$ events collected with the BESIII detector operating at the BEPCII collider, the Dalitz decay $Σ^0 \rightarrow Λe^+e^-$ is studied experimentally for the first time. The $Σ^0$ hyperons are produced through the process $J/ψ\rightarrow Σ^0\barΣ^0$ and analyzed using a double-tag method. The absolute branching fraction is measured to be $\mathcal{B}(Σ^0 \rightarrow Λe^+e^-) = (6.34 \pm 0.25_{\rm stat.} \pm 0.23_{\rm syst.}) \times 10^{-3}$. This result shows a $2σ$ discrepancy from the theoretical calculation quoted in the PDG, where the uncertainties are statistical and systematic, respectively. In addition to the branching fraction, the transition magnetic moment $μ$ is determined to be $(1.74 \pm 0.03_{\rm stat.} \pm 0.09_{\rm syst.})\,μ_N$, where $μ_N=e/(2m_p)$ represents the nucleon magnetic moment, providing valuable insight into the intrinsic structure of the $Σ^0$ hyperon. △ Less

Submitted 17 August, 2026; originally announced August 2026.

Comments: 10 pages, 3 figures; supplemental material included

TR-GS: High-Fidelity Sparse-View CT Volumetric Rendering via t-Distribution Gaussian Splatting and Ray-Confidence Modeling

Authors: Zedong Xiao, Yiren Wang, Zhou Liu, Xiaolin Liu, Zhangji Lu

Abstract: High-fidelity 3D medical visualization supports applications such as clinical assessment and surgical planning. Sparse-view computed tomography (CT) can reduce projection requirements and associated radiation exposure, but limited observations may introduce structural artifacts and reconstruction uncertainty. Although 3D Gaussian Splatting (3DGS) provides an efficient explicit representation for v… ▽ More High-fidelity 3D medical visualization supports applications such as clinical assessment and surgical planning. Sparse-view computed tomography (CT) can reduce projection requirements and associated radiation exposure, but limited observations may introduce structural artifacts and reconstruction uncertainty. Although 3D Gaussian Splatting (3DGS) provides an efficient explicit representation for volumetric rendering, existing CT methods based on standard Gaussian primitives may be sensitive to unreliable observations under sparse-view acquisition. We present TR-GS, a Gaussian-splatting framework for sparse view CT volumetric rendering. TR-GS replaces standard Gaussian primitives with projectable Student's t-distribution primitives and introduces a ray-confidence model that regulates their degrees of freedom according to local ray observability. Confidence-guided 3D wavelet regularization is further used to balance high-frequency detail preservation and noise suppression. This work is licensed under a Creative Commons Attribution 4.0 International License. Experiments on synthetic and real-world datasets show that TR-GS improves over representative baselines in most evaluated settings and remains competitive in the remaining cases. The resulting volumetric representations may support downstream medical multimedia applications, including XR-based visualization and interactive clinical rendering. △ Less

Submitted 16 August, 2026; originally announced August 2026.

Journal ref: ACM Multimedia 2026

CETalk: Continuous Valence-Arousal Control for Audio-Driven 3D Talking Head Generation

Authors: Peng Jia, Li Dai, Zhen Xiao, Xueliang Liu, Jia Li

Abstract: Emotional 3D talking head generation aims to synthesize expressive facial animations with accurate lip synchronization. However, existing methods often rely on discrete emotion categories, which fail to capture the continuous evolution of affect. They also overlook the temporal frequency mismatch between audio articulation and emotional expression. In this paper, we propose CETalk, an audio-driven… ▽ More Emotional 3D talking head generation aims to synthesize expressive facial animations with accurate lip synchronization. However, existing methods often rely on discrete emotion categories, which fail to capture the continuous evolution of affect. They also overlook the temporal frequency mismatch between audio articulation and emotional expression. In this paper, we propose CETalk, an audio-driven 3D facial animation framework conditioned on continuous Valence--Arousal (VA) representations for fine-grained emotion control. CETalk predicts a sequence of FLAME parameters through three key components: a Dynamic Emotion Modulation Module that adaptively scales emotional intensity using audio-derived cues; a Multi-Scale Temporal Modeling mechanism that employs parallel branches to decouple high-frequency articulatory movements from low-frequency emotional dynamics; and a Dynamic Fusion Mechanism that integrates these multi-scale features via an adaptive gating network. To support training and evaluation, we construct 3D-VA-MEAD, a large-scale dataset with automatically estimated VA annotations and reconstructed 3D facial motions. Extensive experiments demonstrate that CETalk outperforms state-of-the-art methods in both lip-sync accuracy and emotional expressiveness, while enabling smooth and controllable emotion transitions. △ Less

Submitted 15 August, 2026; originally announced August 2026.

Comments: 14 pages, 6 figures, 3 tables

Causal Mediation Analysis for Network Data with Graph Neural Network

Authors: Peikai Wu, Zhiguo Xiao

Abstract: Causal mediation analysis is typically formulated under no interference, an assumption often violated in networked populations. We develop a nonparametric framework for a single large observed network that allows simultaneous treatment and mediator spillovers and high-dimensional network confounding. Exposure and mediator mappings define causal estimands without restricting the true interference m… ▽ More Causal mediation analysis is typically formulated under no interference, an assumption often violated in networked populations. We develop a nonparametric framework for a single large observed network that allows simultaneous treatment and mediator spillovers and high-dimensional network confounding. Exposure and mediator mappings define causal estimands without restricting the true interference mechanism, separating own from spillover effects without prespecified aggregation models. Under strengthened conditional independence conditions, we identify own controlled direct, natural direct, and natural indirect effects and give primitive sufficient conditions in terms of structural errors. We construct augmented inverse probability weighted estimators that are doubly robust for controlled effects and multiply robust for natural effects, using graph neural networks to learn high-dimensional nuisance functions from node features and the adjacency matrix. Under approximate neighborhood interference, weak network dependence, and suitable first-stage rates, we establish asymptotic normality of the effect estimators and consistency of a network HAC variance estimator. In simulations the graph neural network estimator outperforms machine learning methods built on hand-constructed neighborhood features, and a reanalysis of an agricultural insurance experiment in rural China finds insurance knowledge to be a substantive mediating channel while perception-based mediators are not. △ Less

Submitted 13 August, 2026; originally announced August 2026.

Comments: 86 pages

High-precision measurement of the space-like $η^\prime$ transition form factor

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, M. S. Anderson, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone , et al. (758 additional authors not shown)

Abstract: Using a data sample corresponding to an integrated luminosity of $20.3\ \text{fb}^{-1}$, collected with the BESIII detector at a center-of-mass energy of $3.773\ \text{GeV}$ at the BEPCII collider, we report a precision measurement of the product $Q^2|F(Q^2)|$, where $F(Q^2)$ is the single-virtual space-like transition form factor of the $η'$ meson and $Q^2$ is the squared momentum transfer of the… ▽ More Using a data sample corresponding to an integrated luminosity of $20.3\ \text{fb}^{-1}$, collected with the BESIII detector at a center-of-mass energy of $3.773\ \text{GeV}$ at the BEPCII collider, we report a precision measurement of the product $Q^2|F(Q^2)|$, where $F(Q^2)$ is the single-virtual space-like transition form factor of the $η'$ meson and $Q^2$ is the squared momentum transfer of the tagged virtual photon. The transition form factor is extracted from the differential Born cross section of the two-photon fusion processes $e^+e^- \to e^+e^-γγ^* \to e^+e^-η^\prime$ using a single-tag technique, where only one scattered lepton is detected. The measurement covers $Q^2 \in [0.1, 6.0]$ GeV$^2$, achieving unprecedented precision, better than $3.0\%$ for $Q^2 < 1.5$ GeV$^2$, and providing the first direct determination at $Q^2 < 0.3$ GeV$^2$. △ Less

Submitted 12 August, 2026; originally announced August 2026.

MetaStrategy: Generative Ranking with Executable LLM Strategies

Authors: Chengyu Lai, Jiuning Lin, Zhibo Xiao, Xiaodong Zhu, Ruiquan Lan, Bin Zhang, Zihong Huang, Wendong Zhang, Chuxin Chen, Yinjiang Cai, Shuai Zhong, Lingqing Zhang, Dimin Wang, Jialin Zhu, Han Zhu

Abstract: Industrial recommender systems rank heterogeneous content under coupled user, business, commercial, and experience objectives. Existing generative ranking methods typically construct item sequences directly, making them difficult to integrate with mature predictive models, operational rules, and field-level guardrails. We present MetaStrategy, a framework that instead generates a structured, execu… ▽ More Industrial recommender systems rank heterogeneous content under coupled user, business, commercial, and experience objectives. Existing generative ranking methods typically construct item sequences directly, making them difficult to integrate with mature predictive models, operational rules, and field-level guardrails. We present MetaStrategy, a framework that instead generates a structured, executable ranking strategy. Conditioned on request context, a large language model (LLM) policy emits a typed JSON bundle controlling objective weights, content and category preferences, experience constraints, and position policies. A deterministic validator and compiler instantiate an isolated Generator that competes atomically with incumbents under the list-level Evaluator of the Generator-Evaluator (GE) architecture. We train the policy in a production-path replay environment that re-executes logged requests through the current re-ranking stack without user exposure. The method combines selection, relative-rank, and baseline-lift rewards, a self-competitive curriculum that feeds frequent strategies back as competitors, and Evaluator-routed reward-augmented on-policy distillation that transfers complementary 4B-parameter Teachers into a compact 0.8B-parameter Student. We deploy MetaStrategy in Taobao Homepage Guess You Like through diff-triggered nearline generation; LLM inference remains outside synchronous ranking, with no observable increase in response time (RT). In a seven-day user-randomized online A/B test, MetaStrategy wins 27.93% of treatment-side GE calls and significantly improves click page views (click PV) by 2.11%, item-detail page views (IPV) by 3.12%, and transaction amount by 2.83%. △ Less

Submitted 10 August, 2026; originally announced August 2026.

DREAM Technical Report

Authors: Bin Zhang, Bowen Zheng, Chao Yi, Chengyu Lai, Dian Chen, Dimin Wang, Gaoyang Guo, Jialin Zhu, Jian Wu, Jing Yu, Jiuning Lin, Lingqing Zhang, Lingyun Zheng, Mao Zhang, Mingming Pan, Ruiquan Lan, Shuai Zhong, Wen Chen, Wendong Zhang, Xiaodong Zhu, Xuan Chen, Xunke Xi, Yifan Lu, Yiheng Wang, Yue Zeng , et al. (52 additional authors not shown)

Abstract: Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across modules, rely on rigid rules, and have limited awareness of real-time intent, leaving session-level shifts among browsing, comparison, and purchase insufficiently addressed. We present DREAM (Developing Recommender Engine… ▽ More Industrial recommender systems commonly use cascaded retrieval, ranking, and re-ranking pipelines. Although efficient, these pipelines fragment information and objectives across modules, rely on rigid rules, and have limited awareness of real-time intent, leaving session-level shifts among browsing, comparison, and purchase insufficiently addressed. We present DREAM (Developing Recommender Engine with Agentic Methods), an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them. DREAM has two core components. First, a three-tier Intent Engine fuses on-device signals into structured L0/L1/L2 intent representations; its edge-cloud trigger chain reduces reporting volume to approximately 8.7%. Second, a Meta Engine uses a MetaModel for layered M1-to-M2-to-M3 reasoning: intent summarization, strategy planning informed by Strategy Memory, and parameter translation. It dispatches the resulting parameters through a unified outlet with safety guardrails. A Reward Dual Loop continuously optimizes both components by combining offline simulation for strategy-space exploration with online feedback for outcome calibration, forming a cycle of generation, execution, evaluation, and experience accumulation. Large-scale A/B tests on Taobao's homepage feed show that re-ranking control alone improves IPV by 2.06%, Core IPV by 2.39%, and GMV by 0.88%. Extending control to fine ranking raises these gains to 2.71%, 3.06%, and 1.31%, respectively, while consistently improving PV by more than 1%. These gains require neither replacement of pipeline models nor compromise of serving stability, supporting agentic meta-control as a viable paradigm for industrial recommendation. △ Less

Submitted 13 August, 2026; v1 submitted 10 August, 2026; originally announced August 2026.

Comments: Technical Report

SoftmaxGRPO: Learning to Reason using Softmax Advantage Group Estimation

Authors: Jefferson Hernandez, Jaywon Koo, Zilin Xiao, Chen Wei, Vicente Ordonez

Abstract: Group-based reinforcement learning objectives such as GRPO can allocate learning signal poorly across prompt difficulty: under binary rewards, group normalization induces a divergent weighting on easy prompts. We introduce Softmax Advantage Group Estimation (SoftmaxGRPO), a drop-in alternative that replaces z-score-normalized group advantages with temperature-scaled softmax advantages, keeping wei… ▽ More Group-based reinforcement learning objectives such as GRPO can allocate learning signal poorly across prompt difficulty: under binary rewards, group normalization induces a divergent weighting on easy prompts. We introduce Softmax Advantage Group Estimation (SoftmaxGRPO), a drop-in alternative that replaces z-score-normalized group advantages with temperature-scaled softmax advantages, keeping weights bounded regardless of prompt difficulty. For binary rewards, we derive the exact finite-group population objective and identify MaxRL as its low-temperature limit. For bounded scalar rewards, we show that the large-group update exactly optimizes a log-moment-generating-function objective, while a universal finite-group scalar objective cannot exist without additional assumptions on the reward distribution. Empirically, SoftmaxGRPO reallocates measured gradient budget away from near-solved prompts and consistently improves over GRPO under identical rewards. It reaches 51.8% on DeepMath with verifiable rewards and improves a 1.5B instruction-tuned model from 35.0% to 68.0% on Poetry using only lightweight text-similarity rewards. △ Less

Submitted 10 August, 2026; originally announced August 2026.

Comments: Accepted to COLM 2026

Beyond Myopic World Models: Long-Horizon End-to-End Training for Direct Future Prediction

Authors: Xinyi Li, Zaishuo Xia, Chenjie Hao, Yubei Chen

Abstract: World models are expected to support imagination over extended temporal horizons, yet most are still trained through local few-step prediction objectives and deployed by recursively rolling out their own predictions. This creates a fundamental mismatch: few-step losses optimize local transition fidelity, while long-horizon prediction depends on how errors and gradients propagate through the entire… ▽ More World models are expected to support imagination over extended temporal horizons, yet most are still trained through local few-step prediction objectives and deployed by recursively rolling out their own predictions. This creates a fundamental mismatch: few-step losses optimize local transition fidelity, while long-horizon prediction depends on how errors and gradients propagate through the entire trajectory. As a result, transitions with different downstream influence on the endpoint are treated uniformly during training, and small local errors are amplified through recursive inference. We argue that long-horizon accuracy is better achieved by optimizing directly, through an end-to-end endpoint prediction objective. To instantiate this paradigm, we introduce the Direct Prediction World Model (DPWM), a non-recursive architecture that compresses an action sequence of arbitrary length into a single embedding and predicts the endpoint observation in a single forward pass. This design avoids recurrent rollout in both prediction and gradient propagation, making long-horizon end-to-end training practical at horizons where unrolled autoregressive training becomes unstable. Empirically, DPWM substantially improves long-horizon endpoint prediction over recursive world-model baselines on continuous-control and pixel-based benchmarks, with larger gains as the prediction horizon increases. We further show that recurrent baselines benefit similarly when retrained with the same long-horizon endpoint objective, supporting our central claim that the training objective, rather than the particular backbone choice, is the main driver of long-horizon prediction accuracy. Our results suggest that world models can benefit from being trained and evaluated at the temporal scales where they are ultimately used, shifting the focus from local transition modeling toward long-horizon predictive accuracy. △ Less

Submitted 7 August, 2026; originally announced August 2026.

MemPrism: Task-Conditioned Relational Memory Views for Long-Horizon Agents

Authors: Zhisheng Chen, Bingfan Zeng, Bangde Cao, Zhengwei Xie, Yuxuan Li, Jinhan Li, Zheng Lu, Xiangchen Guan, Zikai Xiao, Rui Qian, Jingwei Song

Abstract: Long-horizon agents rely on memory to reuse experiences, yet existing memory systems often assume that evidence can be directly consumed through a fixed representation. This leads to representation mismatch, where relevant information is available but not organized for the current decision. To this end, we propose MemPrism, a task-conditioned relational memory framework that separates persistent e… ▽ More Long-horizon agents rely on memory to reuse experiences, yet existing memory systems often assume that evidence can be directly consumed through a fixed representation. This leads to representation mismatch, where relevant information is available but not organized for the current decision. To this end, we propose MemPrism, a task-conditioned relational memory framework that separates persistent experience storage from decision-time working memory. MemPrism records interactions as the event stream and dynamically constructs relational views according to the current task context. A lightweight view policy selects the relation structure, evidence range, outcome condition, and granularity, while a deterministic composer and render transform historical facts into a temporary optical working-memory view for a frozen task policy. Experiments on long-horizon embodied and web-agent benchmarks show that MemPrism consistently improves the task performance, especially as trajectories become longer, while reducing memory token consumption. Furthermore, the learned view policy transfers across different VLMs without additional adaptation, demonstrating the effectiveness of task-conditioned relational views as a general memory interface for agents. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Dual-Faraday-laser-pumped cesium beam clock with $7.7\times 10^{-13}/\sqrtτ$ frequency stability

Authors: Xiaomin Qin, Suyang Wei, Haijun Chen, Yufei Yan, Qiang Wei, Hangbo Shi, Zhiyang Wang, Zheng Xiao, Zijie Liu, Tiantian Shi, Jingbiao Chen

Abstract: Compact cesium beam clocks are major frequency references for deployable timing systems. However, further improvement of their short-term frequency stability is limited by the clock signal-to-noise ratio (SNR). Although two-laser optical pumping can increase the effective atomic utilization, the achievable clock SNR has long been limited by laser-induced frequency-to-amplitude noise conversion. He… ▽ More Compact cesium beam clocks are major frequency references for deployable timing systems. However, further improvement of their short-term frequency stability is limited by the clock signal-to-noise ratio (SNR). Although two-laser optical pumping can increase the effective atomic utilization, the achievable clock SNR has long been limited by laser-induced frequency-to-amplitude noise conversion. Here, we demonstrate a compact dual-Faraday-laser-pumped (DFP) Cs beam clock enabled by a low-frequency-noise atom-referenced laser architecture. The intracavity Faraday anomalous dispersion optical filter provides inherent alignment to the Cs D$_2$ resonances, while modulation transfer spectroscopy offers suppressed frequency noise and drift. The resulting laser system supports robust turnkey operation with a Lorentzian linewidth of 2.12 kHz. The DFP Cs clock achieves a clock SNR of 46,365 in a 1-Hz bandwidth and a fractional Allan deviation of $7.7\times 10^{-13}/\sqrtτ$ , with Hadamard deviation reaching $7.7\times 10^{-15}$ at 10,000 s. This work pushes the fractional frequency stability of a compact Cs beam clock into the $10^{-13}/\sqrtτ$ regime, providing a pathway toward high-performance Cs frequency references for field-deployable precision timing, navigation, and synchronization. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Comments: 12 pages, 5 figures

Search for the charged lepton flavour violating decay $η'\to eμ$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, M. S. Anderson, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone , et al. (744 additional authors not shown)

Abstract: Based on $(8998\pm40)\times10^6$ $J/ψ$ events collected in $e^+e^-$ collisions at $\sqrt{s} = 3.097$ GeV with the BESIII detector, we present a search for the charged lepton flavour violating decay $η'\to eμ$ with $J/ψ\toγη'$. No significant signal is observed, and an upper limit on its decay branching fraction is set to be $6.3\times10^{-7}$ at the 90% confidence level, improving the previous bes… ▽ More Based on $(8998\pm40)\times10^6$ $J/ψ$ events collected in $e^+e^-$ collisions at $\sqrt{s} = 3.097$ GeV with the BESIII detector, we present a search for the charged lepton flavour violating decay $η'\to eμ$ with $J/ψ\toγη'$. No significant signal is observed, and an upper limit on its decay branching fraction is set to be $6.3\times10^{-7}$ at the 90% confidence level, improving the previous best result by nearly three orders of magnitude. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Comments: 20 pages, 5 figures, 3 tables

DREAM: LLM-based Dynamic Role-playing via Event-Aware Memory Graph

Authors: Zhihao Xiao, Mengting Li, Xintao Wang, Linfeng Li, Limin Shui, Mengqi Ji, Borui Cai

Abstract: Role-playing agents (RPAs) have emerged as a key application of large language models, enabling immersive and high-fidelity character simulation. Accurate role-playing of established characters requires not only stylistic imitation but also temporally consistent and causally grounded behavioral reasoning. However, existing RPAs primarily rely on static character descriptions and unstructured memor… ▽ More Role-playing agents (RPAs) have emerged as a key application of large language models, enabling immersive and high-fidelity character simulation. Accurate role-playing of established characters requires not only stylistic imitation but also temporally consistent and causally grounded behavioral reasoning. However, existing RPAs primarily rely on static character descriptions and unstructured memory, limiting their ability to maintain long-term narrative and personality coherence. We introduce DREAM, a structured memory framework for role-playing agents inspired by the Activating Event-Belief-Consequence (ABC) cognitive model. DREAM transforms unstructured literary text into an Event-aware Memory Graph (EMG) that organizes character experiences into temporally ordered and causally linked event graph. This representation enables the construction of dynamic, dual-granularity character profiles that capture both stable personality traits and event-driven behavioral evolution. We further propose the Temporal Causal Memory (TCM) benchmark to evaluate temporal consistency and long-range causal narrative coherence. DREAM achieves state-of-the-art performance across CoSER, LIFECHOICE, and TCM, outperforming multiple strong baselines. Our approach demonstrates the effectiveness of structured memory in enhancing the interpretability and consistency of role-playing agents. △ Less

Submitted 27 May, 2026; originally announced August 2026.

Comments: Accepted at KDD 2026. Camera-ready version to appear. 16 pages, 5 figures

Photonic-chip-based generation of sub-100-femtosecond optical frequency combs

Authors: Weiqiang Xie, Zhengshun Lei, Zeyu Xiao, Yudi Zhao, Xing Zou, Wenqi Wei, Zihao Wang, Ting Wang, Jianjun Zhang, Bofang Zheng, Yikai Su

Abstract: Sub-100-fs optical pulses and frequency comb sources have been revolutionizing a wide range of applications, from ultrafast optical science to optical frequency standard and measurement. To date, the leading techniques for generating such pulses in practical systems rely on tabletop mode-locked lasers, which inherently suffer from high system complexity, limited long-term reliability, and pronounc… ▽ More Sub-100-fs optical pulses and frequency comb sources have been revolutionizing a wide range of applications, from ultrafast optical science to optical frequency standard and measurement. To date, the leading techniques for generating such pulses in practical systems rely on tabletop mode-locked lasers, which inherently suffer from high system complexity, limited long-term reliability, and pronounced environmental sensitivity. Meanwhile, driven by advances in photonic integration, chip-scale approaches have sought to realize miniaturized pulse sources. However, simultaneously achieving sub-100-fs duration, ideal pulse shape, and a broadband flat-topped spectrum remains a significant challenge. Here, we address these challenges by combining two key photonic chip technologies: TFLN EO modulators for picosecond seed pulse generation, and highly nonlinear optical loop mirrors (NOLM) based on AlGaAsOI nanowaveguides for efficient temporal pulse cleaning and spectral broadening. In theoretical simulation and experiment, we show that for an input seed pulse centred at ~1550nm, a single-stage AlGaAs NOLM with a loop length of 1cm can produce flat-topped, nearly tenfold spectral broadening and over tenfold compression of pulse width, and more than 10dB suppression of pulse pedestals. Using initial EO comb pulses with ps-level durations at repetition rates of 10-20GHz, we demonstrate photonic-chip-enabled pulses with an unprecedented duration of 55fs and a flat-topped comb spectrum whose 10dB optical bandwidth exceeds 90nm. Our results highlight the remarkable potential of photonic chip technologies to realize high-repetition-rate, miniaturized sub-100-fs optical pulse generators with the prospect of superior stability and operability. The demonstrated photonic-chip-based sub-100-fs optical frequency comb sources may establish a new paradigm for both scientific research and practical applications. △ Less

Submitted 5 August, 2026; originally announced August 2026.

Perception Before Reasoning: Dynamic Latent Reasoning for Video Understanding and Question Answering

Authors: Haotian Xia, Zilin Xiao, Junbo Zou, Vicente Ordonez, Hanjie Chen

Abstract: Video question answering requires models to ground language queries in visual evidence and, when necessary, reason over that evidence across time. Existing methods typically rely on long textual chain-of-thought rationales, even though many questions can be answered as soon as the relevant object, action, or frame is localized. We propose Dynamic Latent Reasoning (DyLaR), which first grounds a que… ▽ More Video question answering requires models to ground language queries in visual evidence and, when necessary, reason over that evidence across time. Existing methods typically rely on long textual chain-of-thought rationales, even though many questions can be answered as soon as the relevant object, action, or frame is localized. We propose Dynamic Latent Reasoning (DyLaR), which first grounds a question in a short block of perception latents (continuous hidden states that encode query-relevant visual evidence), and then adaptively decides whether to append reasoning latents (continuous thoughts that reason over this evidence in latent space) before answering. DyLaR learns this behavior by grounding perception latents in verified visual evidence and distilling verified rationales into reasoning latents, followed by reinforcement learning that further refines when to reason. Across nine video benchmarks and four multimodal language model backbones, DyLaR improves average accuracy over same-backbone baselines while generating fewer than 20 tokens per query. On Qwen3-VL-4B, for example, DyLaR improves average accuracy over Qwen3-VL-4B-Thinking from 54.0 to 58.2 while reducing response length from 1,220.7 to 18.5 tokens per query. Ablations further show that grounded perception latents, rationale-supervised reasoning latents, and adaptive routing each improve accuracy. △ Less

Submitted 4 August, 2026; originally announced August 2026.

$S^3$: Improving Agent Safety through Multi-Stage Defense

Authors: Zibo Xiao, Haoyu Wang, Jun Sun

Abstract: Large Language Model (LLM) agents rely on multi-stage agentic workflows, with stages such as memory, planning, and tool execution, to accomplish complex tasks. However, risks may emerge at different stages, propagate across steps, and become difficult to detect and mitigate. Existing safety methods protect only isolated stages and are difficult to integrate, leaving agents without comprehensive pr… ▽ More Large Language Model (LLM) agents rely on multi-stage agentic workflows, with stages such as memory, planning, and tool execution, to accomplish complex tasks. However, risks may emerge at different stages, propagate across steps, and become difficult to detect and mitigate. Existing safety methods protect only isolated stages and are difficult to integrate, leaving agents without comprehensive protection throughout the workflow. To address these limitations, we introduce Stage-Specific Safety Skills, a unified abstraction that represents heterogeneous safety designs as reusable and composable components with explicit stage semantics. We further develop an automated transformation pipeline that converts existing safety designs into reusable safety skills and establish a community-driven safety skill library. Building on this abstraction, we propose $S^3$, a multi-stage defense framework in which a guard agent orchestrates stage-specific safety skills for risk detection and mitigation throughout the agentic workflow. We also construct the Multi-Stage Risk Benchmark (MSRB) to evaluate representative risks across workflow stages. Experimental results show that $S^3$ consistently outperforms representative state-of-the-art baselines in both safety effectiveness and utility preservation. These results demonstrate the potential of stage-specific safety skills as a scalable and composable foundation for building resilient and trustworthy agent systems. △ Less

Submitted 2 August, 2026; originally announced August 2026.

Expressive Power and Limitations of Multi-photon Quantum Neural Networks

Authors: Zeyu Xiao, Weixu Shi, Yizhi Wang, Lingling Lao, Junjie Wu

Abstract: Quantum neural networks (QNNs) have shown promise in leveraging quantum computation for machine learning tasks. Utilizing multiple identical photons as input, multi-photon quantum neural networks (MPQNNs) have the potential to enhance the expressivity through increasing the photon number. However, how precisely the expressivity of an MPQNN is affected by an increase in photon number, and whether i… ▽ More Quantum neural networks (QNNs) have shown promise in leveraging quantum computation for machine learning tasks. Utilizing multiple identical photons as input, multi-photon quantum neural networks (MPQNNs) have the potential to enhance the expressivity through increasing the photon number. However, how precisely the expressivity of an MPQNN is affected by an increase in photon number, and whether it can be infinitely enhanced by increasing the photon number, remains unexplored. In this work, we quantitatively estimate the expressivity of this model by deriving upper bounds on approximation error in two cases. In the case of a fixed observable, there exists a threshold that scales linearly with the mode number. Below the threshold, the expressivity of an MPQNN can be enhanced polynomially by increasing the photon number. Above the threshold, however, increasing the photon number does not affect the expressivity. In the case of a trainable observable, the expressivity can always be enhanced polynomially by increasing the photon number. These findings are then validated by numerical simulations. Our work elucidates the performance enhancement of multi-photon quantum feature in QNNs, as well as its limitations, offering guidance for leveraging multi-photon advantages in quantum machine learning. △ Less

Submitted 2 August, 2026; originally announced August 2026.

Comments: 13 pages,3 figures

LongChart VQA: A Comprehensive Benchmark for MLLMs with Complex Multi-Chart Reasoning

Authors: Ziyan Xiao, Yinghao Zhu, Wenting Zhang, Heaju Kim, Lequan Yu

Abstract: Multimodal large language models (MLLMs) are rapidly evolving with expanded context windows and stronger reasoning capabilities, enabling multi-chart understanding and multi-step inference. These abilities are increasingly important as MLLMs are adopted in complex agentic tasks. However, existing benchmarks largely emphasize single-chart perception, while simple chart-to-chart connections are insu… ▽ More Multimodal large language models (MLLMs) are rapidly evolving with expanded context windows and stronger reasoning capabilities, enabling multi-chart understanding and multi-step inference. These abilities are increasingly important as MLLMs are adopted in complex agentic tasks. However, existing benchmarks largely emphasize single-chart perception, while simple chart-to-chart connections are insufficient to evaluate these capabilities. To capture multi-chart complexity while ensuring consistency and validity, we design a synthesis pipeline supported by latent graphs. Building on this pipeline, we introduce LongChart, a benchmark whose VQA sets contain an average of 6.5 images and 31.2 questions. We evaluate 10 state-of-the-art MLLMs and examine three factors that influence performance: reasoning patterns, auxiliary tools, and robustness to image perturbations. Our results show that MLLM accuracy decreases and varies substantially as computational complexity increases, highlighting directions for future research in multi-chart reasoning. △ Less

Submitted 2 August, 2026; originally announced August 2026.

JoyAI-Talker: Full-Duplex Speech Interactive Large Model Built for Empathetic Voice Agents

Authors: Yinhao Bai, Jinming Chen, Yafeng Chen, Wei Deng, Boya Dong, Nan Duan, Yu Gu, Weisheng Han, Yankun Huang, Ming Ke, Hao Li, Jingdong Li, Xiangyu Liang, Ning Liu, Yuan Liu, Ji Miao, Jiaqi Wang, Qi Wang, Wenchao Wang, Yuxuan Wang, Zhenfang Wang, Zhangyu Xiao, Chao Xue, Hongfei Xue, Fan Yu , et al. (4 additional authors not shown)

Abstract: We present JoyAI-Talker, a full-duplex speech dialogue system that delivers robust foundation model capabilities while empowering empathetic interaction and voice agent intelligence. JoyAI-Talker adopts a modular Thinker-Talker architecture and further implements a unified speech-text joint training pipeline to mitigate the common "cognitive degradation" bottleneck, thereby largely preserving the… ▽ More We present JoyAI-Talker, a full-duplex speech dialogue system that delivers robust foundation model capabilities while empowering empathetic interaction and voice agent intelligence. JoyAI-Talker adopts a modular Thinker-Talker architecture and further implements a unified speech-text joint training pipeline to mitigate the common "cognitive degradation" bottleneck, thereby largely preserving the model's core textual reasoning, STEM, and logical capabilities while extending them to speech-based interaction. For expressive speech synthesis, the Talker module employs a text-controllable generation paradigm that enables natural-language instructions to flexibly control vocal attributes and localized paralinguistic events, such as laughter and sighs, supporting more expressive and fine-grained speech responses. To enhance conversational empathy, we introduce the Persona-Adaptive Empathetic Response (PAER) framework. PAER employs a hierarchical cognitive pipeline to extract non-verbal speaker cues, such as gender, age, and emotional state, from raw input audio, incorporate them into the Thinker's CoT reasoning, and generate context-adaptive responses that align semantically appropriate text with fine-grained control over utterance-level expressiveness and localized paralinguistic events, including sighs, speaking rate, and volume. We further integrate Joy-Duplex, a state-driven, plug-and-play full-duplex framework that functions as an efficient gating engine for real-time turn control. Extensive evaluations show that JoyAI-Talker achieves highly competitive performance on foundational T2T and S2T benchmarks. In full-duplex evaluation, the system reaches a high response rate of 0.88 under user interruptions while maintaining an extremely low false-trigger rate under background speech, demonstrating its readiness for fluid and natural speech dialogue. △ Less

Submitted 2 August, 2026; originally announced August 2026.

Ultrahigh Intrinsic Hole Mobilities in $M$N$_2$ ($M$= Mo and W) at Room Temperature

Authors: Zhongjuan Han, Rong-Tian Pang, Wu Xiong, Zhonghao Xia, Jin-Jian Zhou, Jiangang He

Abstract: High-mobility $p$-type semiconductors are essential for advanced electronic devices but remain scarce. Here, using a hierarchical screening framework that combines first-principles calculations with Boltzmann transport theory, we identify $M$N$_2$ ($M$= Mo and W) family as polar semiconductors with exceptionally high intrinsic hole mobilities. In particular, 1H-WN$_2$ exhibits a room-temperature h… ▽ More High-mobility $p$-type semiconductors are essential for advanced electronic devices but remain scarce. Here, using a hierarchical screening framework that combines first-principles calculations with Boltzmann transport theory, we identify $M$N$_2$ ($M$= Mo and W) family as polar semiconductors with exceptionally high intrinsic hole mobilities. In particular, 1H-WN$_2$ exhibits a room-temperature hole mobility exceeding $10^{4}$~$\mathrm{cm^2\,V^{-1}\,s^{-1}}$. This exceptional transport performance arises from the synergistic suppression of polar-optical-phonon and acoustic-phonon scattering, together with a reduced intervalley-scattering phase space induced by spin--valley locking. These effects arise from anomalously small Born effective charges, strong covalent N--N bonds, and orbital hybridization between N-$2p_x$/$2p_y$ and W-$5d_{xy}$/$5d_{x^2-y^2}$ in the N$_2$-dimer-based structure. Our results establish MoN$_2$ and WN$_2$ as a promising class of high-mobility polar semiconductors and introduce a crystal-structure-based strategy for concurrently suppressing multiple electron--phonon scattering channels, thereby revising design principles for high-mobility materials. △ Less

Submitted 1 August, 2026; originally announced August 2026.

Comments: 7 pages, 4 figures

Breaking the Horizontal Prior: From Long-Tailed Orientation Bias to Roll-Robust Monocular Depth Estimation

Authors: Kaihua Tang, Ziqing Xia, Xiaoxu Zheng, Xiaoxue Zhang, Michael Bi Mi, Zhan Xu, Dave Zhenyu Chen

Abstract: Despite recent advances in Monocular Depth Estimation, state-of-the-art depth foundation models remain vulnerable to robustness issues. Particularly, even slight camera rolls can result in substantial degradation in depth estimations. We attribute this problem to a previously overlooked phenomenon, termed the Horizontal Prior, which is a manifestation of long-tailed distribution bias: most trainin… ▽ More Despite recent advances in Monocular Depth Estimation, state-of-the-art depth foundation models remain vulnerable to robustness issues. Particularly, even slight camera rolls can result in substantial degradation in depth estimations. We attribute this problem to a previously overlooked phenomenon, termed the Horizontal Prior, which is a manifestation of long-tailed distribution bias: most training images are captured in approximately horizontal orientations due to human visual preferences and photographic habits. While intuitive remedies such as re-balanced data augmentation and horizon leveling provide partial improvements, they fail to fully address the issue. In this paper, we introduce Invariant Depth Constraint (ID-Constraint), a training-time supervision strategy that improves roll robustness by fine-tuning and jointly regularizing the depth backbone with a series of geometric and spatial reasoning tasks. These auxiliary objectives encourage the backbone to learn rotation-stable, depth-relevant representations, while the auxiliary prediction heads are discarded after training, leaving the original inference architecture unchanged. Extensive experiments on five benchmark datasets across four roll settings demonstrate the effectiveness of the proposed method. △ Less

Submitted 1 August, 2026; originally announced August 2026.

Comments: The code is publicly available on GitHub: https://github.com/KaihuaTang/Horizontal-Prior

Agent-Native Task-Oriented Communication with Joint Token Compression Coding and Modulation

Authors: Zhuoran Xiao, Yihang Huang, Tianyu Jiao, Xiaohua Xu, Yin Xu

Abstract: As large foundation models empower agents to become pervasive across industries and emerge as central actors in intelligent systems, a fundamental rethinking of communication paradigms toward AI-native, agent-centric designs in the post-Shannon era becomes inevitable. One essential shift is that tokens, which are the minimal semantic units natively processed by large language models (LLMs), should… ▽ More As large foundation models empower agents to become pervasive across industries and emerge as central actors in intelligent systems, a fundamental rethinking of communication paradigms toward AI-native, agent-centric designs in the post-Shannon era becomes inevitable. One essential shift is that tokens, which are the minimal semantic units natively processed by large language models (LLMs), should replace bits as the fundamental unit of communication. However, existing works in the LLMs field assume lossless token transmission over high-speed wired links and largely neglect the air-interface overhead and channel distortions inherent in wireless environments, lacking a native design for wireless token communication systems. To bridge this gap, we propose an innovative design for a token transmitter-receiver architecture that facilitates task-oriented token transmission. Specifically, we propose JTCM (Joint Token Coding and Modulation), an AI-native semantic communication framework that jointly optimizes token representation, channel coding, and modulation to maximize downstream task performance directly. Correspondingly, we propose a two-stage training scheme. In the first stage, the token encoder-decoder pair is pre-trained to enable semantic-preserving compression and reconstruction. In the second stage, it is fine-tuned end-to-end with a multi-modal foundation model under specific downstream tasks to achieve task-aware optimization. Extensive experiments demonstrate that JTCM significantly reduces transmission overhead while enhancing task accuracy and robustness compared to state-of-the-art baselines in bandwidth- and SNR-constrained wireless channels. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Comments: This paper is accepted by IEEE Globecom 2026, to appear

EEG-JEPA: Structured Latent Prediction for EEG Foundation Models

Authors: Jinhao Li, Zhiyuan Ma, Xueqiao Han, Zhongye Xia, Xinche Zhang, Shanghong Xie, Yixuan Liu, Yongjian Li, Runmin Gan, Tianlin Huo, Sen Song

Abstract: Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconstruction, but applying supervision directly to noisy EEG may encourage models to recover predictable background activity, acquisition effects, and artifacts rather than neural structure that transfers across tasks. This r… ▽ More Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconstruction, but applying supervision directly to noisy EEG may encourage models to recover predictable background activity, acquisition effects, and artifacts rather than neural structure that transfers across tasks. This raises a central question: what should an EEG foundation model predict to learn transferable representations? We introduce EEG-JEPA a structured latent-prediction framework for EEG foundation modeling. Rather than reconstructing masked voltage samples, a masked context encoder and predictor infer contextual latent states produced by an exponential-moving-average target encoder that observes the complete input. EEG-JEPA organizes target design along three complementary dimensions: target content specifies what representation is predicted, target support specifies where prediction occurs over structured electrode--time regions through Neurotopology-Aware Multi-scale Electrode-Temporal Masking (N-MET), and target depth specifies at which encoder layers supervision is applied. Together, these designs shift EEG pretraining from recovering missing measurements to inferring latent states from structured electrode--time context. We evaluate EEG-JEPA through controlled objective comparisons, frozen multitask transfer, and full fine-tuning. Under the same backbone, pretraining corpus, and training duration, EEG-JEPA improves the 14-task frozen macro balanced accuracy from 40.49% to 50.42% over CBraMod-style masked waveform reconstruction. Multi-source continuation further raises this result to 52.94%, the highest average among the EEG foundation models evaluated on EEG-FM-Bench. Under protocol-matched full fine-tuning, EEG-JEPA also improves the nine-task average balanced accuracy from 68.98% to 70.65%. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Comments: 9 pages, 6 figures

Freeze, Then Select: Structured Field Adapters and Stability-Validated Weak Selection for PDE Discovery from Sparse Observations

Authors: Juncheng Zhong, Chenghuang Shen, Jianfeng Liu, Zhengdong Xiao, Longjiu Luo, Qianrong Wang, Wenjun Xu, Wenlian Lu

Abstract: PDE discovery from sparse observations requires reconstructing a continuous field and selecting the correct differential terms. Our analysis of optimization paths in coupled neural PDE discovery reveals three behaviors: the exact support can persist to the end of training, appear only transiently, or fail to emerge. To decouple equation selection from neural optimization, we develop a freeze-then-… ▽ More PDE discovery from sparse observations requires reconstructing a continuous field and selecting the correct differential terms. Our analysis of optimization paths in coupled neural PDE discovery reveals three behaviors: the exact support can persist to the end of training, appear only transiently, or fail to emerge. To decouple equation selection from neural optimization, we develop a freeze-then-select method combining a structured field adapter with Stability-Validated Weak Selection (SVWS). Trained from observations without a PDE residual, the adapter factorizes the field into learned spatial features and temporal coefficients represented by cubic splines. After freezing the field, SVWS identifies recurrent terms across independent weak-form systems, refits candidate supports, and selects the final equation on held-out weak-form systems. Beyond fixed libraries, we apply the same principle to expressions generated by genetic programming and recover the power-law form of an unknown nonlinear diffusion function from sparse, noisy observations. Across all six sparse MDBench regimes, our method attains the highest exact support recovery rate, with its clearest gains over classical and neural baselines on challenging Kuramoto-Sivashinsky dynamics. △ Less

Submitted 31 July, 2026; originally announced July 2026.

Comments: 18 pages, 5 figures, and 17 tables; includes supplementary material

SPFM-Net: Semantic-Prior-Guided Frequency-Constrained Mamba for Invisible Watermark Attack

Authors: Chunpeng Wang, Yanan Shi, Zhiqiu Xia, Jidong Yang, Suo Gao, Qi Li

Abstract: Existing watermark attacks typically rely on predefined signal-processing operations or locally constrained restoration networks, making it difficult to capture the long-range dependencies of globally distributed watermark signals and resulting in an unfavorable trade-off between removal effectiveness and visual fidelity. In this paper, we propose SPFM-Net, a semantic-prior-guided and frequency-co… ▽ More Existing watermark attacks typically rely on predefined signal-processing operations or locally constrained restoration networks, making it difficult to capture the long-range dependencies of globally distributed watermark signals and resulting in an unfavorable trade-off between removal effectiveness and visual fidelity. In this paper, we propose SPFM-Net, a semantic-prior-guided and frequency-constrained Mamba framework for invisible watermark attack. SPFM-Net first employs high-ratio masking to disrupt the spatial coherence of invisible watermark signals, and then utilizes a partially fine-tuned pretrained Masked Autoencoder to reconstruct semantically consistent image from sparse observations while suppressing watermark-related information. A Multi-scale Residual Frequency Feature Interaction module subsequently aggregates watermark-related residual features across multiple receptive fields, while adaptively suppressing responses from watermark-irrelevant regions. To further capture the long-range dependencies of globally distributed watermark signals, a lightweight Mamba-based Global State-space Feature Modeling (GSFM) unit is introduced to separate watermark-related features from natural image content and suppress the remaining watermark traces. In addition, SPFM-Net is optimized using a multi-level objective that jointly imposes spatial-, frequency-, and edge-domain constraints, enabling effective watermark suppression while preserving perceptual quality. Extensive experiments on representative spatial-domain, transform-domain, orthogonal moment-based, and deep learning-based watermarking schemes demonstrate that SPFM-Net achieves a favorable trade-off between watermark attack effectiveness and perceptual fidelity. △ Less

Submitted 30 July, 2026; originally announced July 2026.

VeriSkill: A Self-Evolution Framework for Program Verification Skills

Authors: Changguo Jia, Tianqi Zhao, Zhiyou Xiao, Weiming Zhang, Minghui Zhou

Abstract: Automating program verification with LLM agents requires generating specifications, annotations, auxiliary lemmas, and tool invocations, all of which depend on reusable skills. A natural remedy is skill self-evolution: distilling skills from trajectories and refining them through feedback. However, existing evolution methods struggle with program verification tasks because they cannot reliably ide… ▽ More Automating program verification with LLM agents requires generating specifications, annotations, auxiliary lemmas, and tool invocations, all of which depend on reusable skills. A natural remedy is skill self-evolution: distilling skills from trajectories and refining them through feedback. However, existing evolution methods struggle with program verification tasks because they cannot reliably identify skill-specific failures or extract actionable signals from opaque verifier feedback. In this paper, we propose VeriSkill, a self-evolution framework built for program verification. It attributes verification failures to skill deficiencies, distills diagnostic signatures into reusable lessons, and iteratively refines candidate skills, admitting only revisions that improve verification performance while preserving program semantics. Experiments show that VeriSkill consistently outperforms all baselines across multiple verification tools, agent frameworks, and LLM backends. △ Less

Submitted 30 July, 2026; originally announced July 2026.

EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis

Authors: Lei Zan, Keli Zhang, Shifeng Xie, Jiale Zheng, Zehao Xiao, Zhiwei Dong, Ke Zhang, Ruichu Cai, Malik Tiomoko, Lujia Pan

Abstract: Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail. Root cause analysis (RCA) aims to identify the small set of alarms that initiate each cascade. A common approach learns a causal graph from observational logs and predicts all zero-in-degree alarms in each incident-induced subgraph. However, the learned graph remains fixed and cannot bene… ▽ More Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail. Root cause analysis (RCA) aims to identify the small set of alarms that initiate each cascade. A common approach learns a causal graph from observational logs and predicts all zero-in-degree alarms in each incident-induced subgraph. However, the learned graph remains fixed and cannot benefit from expert diagnoses of historical incidents. We close this loop with EvoCause. Expert labels constrain which alarms should be source nodes but do not specify the edge edits needed to satisfy those constraints. EvoCause uses a large language model (LLM) to propose semantically plausible graph edits, while deterministic code validates node identities and acyclicity and retains the best graph on a labeled alignment set. At test time, the refined graph alone produces transparent predictions without an LLM call. We also release TeleRCA, an expert-annotated benchmark from a production telecommunication network containing $485{,}681$ alarm events spanning $194$ alarm types over $5{,}621$ resources. On synthetic data, EvoCause initialized with the PC causal discovery algorithm outperforms the unrefined PC baseline, raising Node F1, Case EM, and Graph F1 by $11.59$, $9.40$, and $4.59$ percentage points, respectively, while reducing nSHD by $0.2379$. On TeleRCA, replacing human-readable alarm titles with anonymous identifiers lowers Node F1 and Case EM by $6.12$ and $8.04$ percentage points, respectively, indicating that alarm-name information contributes to graph refinement. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Borrowed Strength: Best-of-N Search over a Code EncodingBreaks Self-Check Jailbreak Defenses

Authors: Haoyu Zhang, Shibo Zheng, Xiangchen Guan, Zhuoxi Wang, Zijian Xiao, Mohammad Zandsalimy, Shanu Sushmita

Abstract: A self-check defense asks the target model to assess a request before answering it; SAGE, the strongest published instance, reports an average 99% defense success rate. We show it can be breached by composing two attacks that are individually harmless against it: an established code-completion encoding and an established best-of-N search, neither of which exceeds 4.7% of behaviors alone. Composed,… ▽ More A self-check defense asks the target model to assess a request before answering it; SAGE, the strongest published instance, reports an average 99% defense success rate. We show it can be breached by composing two attacks that are individually harmless against it: an established code-completion encoding and an established best-of-N search, neither of which exceeds 4.7% of behaviors alone. Composed, with the search budget spent on the encoding, they reach 67/22/15% across three open targets, and the effect persists on a 70B target. We then explain the composition rather than only reporting it. First, a self-check defense borrows its strength from the target: SAGE does not detect the attack, it asks the model to, and the four targets convert that request into an explicit refusal between 32% and 97% of the time, which orders the spread in defended coverage even though undefended reach is near-identical. Second, which attack survives is decided by the type of defense, and it inverts: against transform defenses the code encoding retains far more of its undefended reach than the character search, while against gate defenses the ordering flips. We account for this with the number of independent probes an attack delivers to a defense's decision boundary. Finally, we report a validity defect we found and repaired in our own pipeline, a deterministic attack under greedy decoding has no best-of-N variation channel at all, and give the one-line diagnostic that detects it. All claims rest on 310,000 generations scored by a human-validated judge. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Revisiting Lossy Verification in Speculative Decoding: Mechanisms, Trade-offs, and Failure Modes

Authors: Tianyu Wang, Yuxuan Zhou, Wenbin Wang, Heng Li, Zikai Xiao, Junyuan Shang

Abstract: Speculative Decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose tokens that are subsequently verified in parallel by a larger target model. Recent approaches introduce lossy verification schemes to further improve efficiency by relaxing strict distributional matching. Yet such relaxation silently rewrites the decoding distribution, and the resu… ▽ More Speculative Decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose tokens that are subsequently verified in parallel by a larger target model. Recent approaches introduce lossy verification schemes to further improve efficiency by relaxing strict distributional matching. Yet such relaxation silently rewrites the decoding distribution, and the resulting acceleration can come at the cost of unstable, sometimes severely degraded generation quality. In this work, we present a principled analysis of the distributions induced by lossy verification methods. We show that many seemingly distinct approaches differ only superficially and can be classified into two categories: truncation-based verification and collaborative verification. We further construct a diagnostic evaluation framework across curated benchmarks. For truncation-based methods, we identify a fundamental pitfall: performance can degrade significantly compared to the true truncation sampling baseline due to distributional distortion. For collaborative verification, we uncover a key principles: controlling the overshoot of draft probabilities relative to target probabilities is essential to prevent low-quality outputs. Our code is available at https://github.com/ZhouYuxuanYX/Fast-HSD. △ Less

Submitted 29 July, 2026; originally announced July 2026.

ACM Class: I.2.7

Attack Ensembles Expose a Safety-Utility Trade-off in Black-Box Guard Defenses Against Encoded VLM Jailbreaks

Authors: Haoyu Zhang, Zhuoxi Wang, Shibo Zheng, Yi Feng, Xiao Luo, Zijian Xiao, Haowen Xu, Xiangchen Guan, Mohammad Zandsalimy, Shanu Sushmita

Abstract: Safety classifiers ("guards") are the dominant black-box defense for vision-language models, yet a guard judges an input's surface form, not its meaning: a harmful request re-encoded as set theory, formal logic, a classical language, code, or text rendered inside an image slips past a guard that would block it in plain language - the decode gap. The standard fix is a preprocessor that recovers ima… ▽ More Safety classifiers ("guards") are the dominant black-box defense for vision-language models, yet a guard judges an input's surface form, not its meaning: a harmful request re-encoded as set theory, formal logic, a classical language, code, or text rendered inside an image slips past a guard that would block it in plain language - the decode gap. The standard fix is a preprocessor that recovers image content and decodes the encoding before the guard. We build one and evaluate it against an ensemble of eleven published encoding attacks, counting a behavior as broken if any attack succeeds. That metric separates two mechanisms such defenses conflate. Restoring a view the guard never had improves it on both axes at once: it blocks far more attacks, and, measured on a category-balanced benign set, it blocks fewer benign requests, because restating a request normalizes the borderline phrasing a classifier over-flags. It still does not make the system safer: against an attacker free to choose among eleven encodings, closing one channel relocates the success rather than removing it, and no ensemble contrast survives multiple-comparison correction. What does lower ensemble attack success is re-screening the recovered pre-decode surface, and that step is where the entire benign cost falls. The safety-utility trade-off is therefore not a property of recovery; it is localized to one step. Across the full guard x target x condition factorial, no configuration reaches an ensemble attack-success rate at or below 40% while holding benign over-refusal under 70%. The per-attack averages usually reported understate the attacker roughly fourfold, which is why this frontier is easy to miss. Composing across defense families is the one lever that moved the safety axis, beating every configuration we measured, and still landing far outside any deployable refusal budget. △ Less

Submitted 9 August, 2026; v1 submitted 29 July, 2026; originally announced July 2026.

Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels

Authors: Xinyu Yang, Tianxing Chen, Honghao Su, Minxuan Wang, Chenze Yu, Zhangzheng Tu, Yue Chen, Yuxiao Huo, Lingfeng Zhang, Yan Huang, Yan Qin, Shaolong Zhu, Qiwei Liang, Hekun Tian, Shujia Liu, Guangyu Chen, Junhao Gong, Zixuan Li, Wenwei Lin, Zijian Lin, Wenxuan Zhu, Eric J Chen, Yue Yuan, Qize Yu, Jiaqi Liang , et al. (16 additional authors not shown)

Abstract: Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical or operational harm, task completion alone does not establish trustworthiness. We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably under environmental and system var… ▽ More Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction. Because failures can cause immediate physical or operational harm, task completion alone does not establish trustworthiness. We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably under environmental and system variation while maintaining risk within acceptable bounds. We term this objective sustained safe success. Its supporting mechanisms are organized into four interdependent layers. The model layer generates task-competent action proposals with calibrated uncertainty and explicit safety preferences. The system layer realizes authorized actions dependably through integrated sensing, computation, control, hardware safeguards, fault containment, and fallback. The evidence layer substantiates bounded claims through evaluation, verification, validation, traceability, and structured assurance arguments. The deployment layer maintains claim validity through runtime monitoring, authority management, intervention, incident response, and controlled updates. Because assumptions and failures propagate across these layers, neither model capability, isolated safeguards, nor benchmark performance alone can establish end-to-end trustworthiness. Drawing on embodied AI, robotics, control, dependable computing, distributed systems, and autonomous driving, we further propose a non-normative hierarchy of trustworthiness levels. This hierarchy grades the strength of bounded deployment claims across task capability, safety, system assurance, operational governance, and supporting evidence, providing a basis for bounded deployment, comparative evaluation, research prioritization, and future standardization. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Comments: Website: https://xsparkai.com/sparklab/towards-trustworthy-eai

Anomalous entanglement scaling from eigenvector nonorthogonality in critical non-Hermitian free fermions

Authors: Zhenyu Xiao, Shinsei Ryu

Abstract: Entanglement carries universal content that labels phases and critical points. We study the entanglement entropy of the steady states of critical non-Hermitian free-fermion chains. It scales logarithmically with subsystem size, but the coefficients vary continuously with the parameters and form a Rényi family that no single central charge can reproduce. We trace this anomaly to an ``imaginary'' Di… ▽ More Entanglement carries universal content that labels phases and critical points. We study the entanglement entropy of the steady states of critical non-Hermitian free-fermion chains. It scales logarithmically with subsystem size, but the coefficients vary continuously with the parameters and form a Rényi family that no single central charge can reproduce. We trace this anomaly to an ``imaginary'' Dirac point, a crossing in the imaginary part of the energy where the occupied state switches between two Bloch states. Their nonorthogonality weakens the occupation discontinuity and lowers the logarithmic coefficient. A low-energy expansion yields closed-form coefficients in excellent agreement with lattice numerics in various one-dimensional critical steady states. Remarkably, weak real onsite disorder leaves this logarithmic scaling intact and enhances the entanglement. Our results provide a generic understanding of entanglement in critical non-Hermitian free-fermion steady states. △ Less

Submitted 27 July, 2026; originally announced July 2026.

Femtoscopy Measurement with S$π$RIT TPC in Radioactive BeamHeavy-ion Collisions

Authors: Y. J. Wang, C. K. Tam, Z. G. Xiao, W. G. Lynch, C. Y. Tsang, J. Barney, G. Jhang, J. Estee, M. B. Tsang, R. S. Wang, M. Kaneko, J. W. Lee, J. Park, Z. Chajęcki, G. Verde, T. Isobe, M. Kurata-Nishimura, T. Murakami, D. S. Ahn, L. Atar, T. Aumann, H. Baba, K. Boretzky, J. Brzychczyk, G. Cerizza , et al. (42 additional authors not shown)

Abstract: Femtoscopy is a powerful tool for exploring the dynamic emitting structure in heavy-ion collisions, while radioactive beam heavy-ion collisions enable the investigation of nuclear matter under extreme isospin conditions. Here, we successfully perform femtoscopy measurements using the S$π$RIT Time Projection Chamber (TPC). A dedicated correction scheme for track merging and splitting is proposed, w… ▽ More Femtoscopy is a powerful tool for exploring the dynamic emitting structure in heavy-ion collisions, while radioactive beam heavy-ion collisions enable the investigation of nuclear matter under extreme isospin conditions. Here, we successfully perform femtoscopy measurements using the S$π$RIT Time Projection Chamber (TPC). A dedicated correction scheme for track merging and splitting is proposed, which is well applicable to rectangular TPCs housed inside dipole magnets and effectively improves the reconstructed correlation functions at small relative momenta. Focusing on the proton-proton (p-p) correlation function in the 270 MeV/u $^{132}\text{Sn}+^{124}\text{Sn}$ system, we successfully apply the track merging and splitting correction; additionally, the TPC angular acceptance exhibits a negligible impact on the correlation function. A systematic uncertainty quantification framework is established. The experimental results of the p-p correlation function confirm the feasibility of the S$π$RIT TPC for femtoscopy measurements and provide technical support for high-precision femtoscopy studies using rectangular TPCs in radioactive beam heavy-ion collisions. △ Less

Submitted 15 July, 2026; originally announced July 2026.

Comments: 9 pages, 10 figures

ACRL: Adaptive Control of Training-Inference Discrepancy for Stable Reinforcement Learning

Authors: Wenwu Fan, Qihong Lin, Zhijie Xia, Zhuo Zheng, Sihao Wang, Qiang Chen, Liangsheng Zhu

Abstract: Reinforcement Learning (RL) training for Large Language Models (LLMs) often suffers from instability due to the discrepancy between training and inference. This training-inference discrepancy stems from two primary factors: an architectural separation between training and inference engines, and the use of low-precision quantization in inference versus higher-precision computation in training. To a… ▽ More Reinforcement Learning (RL) training for Large Language Models (LLMs) often suffers from instability due to the discrepancy between training and inference. This training-inference discrepancy stems from two primary factors: an architectural separation between training and inference engines, and the use of low-precision quantization in inference versus higher-precision computation in training. To address training instability issues caused by high training-inference discrepancy, we present the principles and methods for its adaptive control. We propose Adaptive Control Reinforcement Learning (ACRL), which adaptively maintains the training-inference discrepancy within a reasonable range to ensure stable RL training. Beyond stabilization, ACRL inherently increases policy entropy, thereby enhancing exploration and improving accuracy. The experimental results show that when the inference engine utilizes FP8 quantization, ACRL consistently maintains the training-inference discrepancy within a reasonable range and stabilizes RL training. Furthermore, ACRL not only matches the accuracy of the BF16 baseline but also outperforms importance sampling (IS) fixes. △ Less

Submitted 27 July, 2026; originally announced July 2026.

Precision Measurement of Decay Dynamics in $D^{0(+)}\to π^{-(0)}\ell^+ν_\ell$

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, M. S. Anderson, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone , et al. (752 additional authors not shown)

Abstract: The branching fractions of $D^0\to π^-e^+ν_e$, $D^0\to π^-μ^+ν_μ$, $D^+\to π^0e^+ν_e$, and $D^+\to π^0μ^+ν_μ$ are precisely measured, using 20.3 fb$^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The ratios of the decay widths between muon and positron channels are examined in full, across several four-momentum transfer ranges of… ▽ More The branching fractions of $D^0\to π^-e^+ν_e$, $D^0\to π^-μ^+ν_μ$, $D^+\to π^0e^+ν_e$, and $D^+\to π^0μ^+ν_μ$ are precisely measured, using 20.3 fb$^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The ratios of the decay widths between muon and positron channels are examined in full, across several four-momentum transfer ranges of $\ell^+ν_{\ell}$. No lepton flavor universality violation is found in the current data. From a simultaneous fit to the precisely measured partial decay rates and the first measured forward-backward asymmetries of these four decays, the product of the hadronic transition form factor, $f^{D\toπ}_+(0)$, and the modulus of the $c\to d$ quark mixing element, $|V_{cd}|$, is measured with unprecedented precision to be $f^{D\toπ}_+(0)|V_{cd}|=0.1425\pm0.0005_{\rm stat.}\pm0.0003_{\rm syst.}$. Taking the value of $|V_{cd}|$ from the standard model global fit and $f^{D\toπ}_+(0)$ derived by the lattice quantum chromodynamics calculation as input, we obtain $f^{D\toπ}_+(0)=0.1425\pm0.0005_{\rm stat.}\pm0.0003_{\rm syst.}$ and $|V_{cd}|=0.2262\pm0.0008_{\rm stat.}\pm0.0005_{\rm syst.}\pm0.0018_{\rm LQCD.}$, respectively. The precision of each result is a factor of 2-3 better than the previous best measurements. Additionally, the real and imaginary parts of the scalar current contribution in the $c\to d \ell^+ν_{\ell}$ transition are measured for the first time to be Re $(C_S^μ)=$ $0.022 \pm 0.023_{\rm stat.}\pm 0.003_{\rm syst.}$ and $|\mathrm{Im} (C_S^μ)|=0.000 \pm 0.038_{\rm stat.}\pm 0.012_{\rm syst.}$. △ Less

Submitted 26 July, 2026; originally announced July 2026.

Comments: 11 pages, 3 figures

Precision measurements of semleptonic decays $D^0 \to π^-\ell^+ν_\ell$ and $D^+ \to π^0\ell^+ν_\ell$ ($\ell =e,μ$)

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, M. S. Anderson, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone , et al. (752 additional authors not shown)

Abstract: The branching fractions of $D^0\to π^-e^+ν_e$, $D^0\to π^-μ^+ν_μ$, $D^+\to π^0e^+ν_e$, and $D^+\to π^0μ^+ν_μ$ are measured to be $(2.950\pm0.017_{\rm stat.}\pm 0.017_{\rm syst.})\times10^{-3}$, $(2.817\pm0.037_{\rm stat.}\pm 0.019_{\rm syst.})\times10^{-3}$, $(3.622\pm0.034_{\rm stat.}\pm 0.018_{\rm syst.})\times10^{-3}$, and $(3.507\pm0.043_{\rm stat.}\pm 0.026_{\rm syst.})\times10^{-3}$ using… ▽ More The branching fractions of $D^0\to π^-e^+ν_e$, $D^0\to π^-μ^+ν_μ$, $D^+\to π^0e^+ν_e$, and $D^+\to π^0μ^+ν_μ$ are measured to be $(2.950\pm0.017_{\rm stat.}\pm 0.017_{\rm syst.})\times10^{-3}$, $(2.817\pm0.037_{\rm stat.}\pm 0.019_{\rm syst.})\times10^{-3}$, $(3.622\pm0.034_{\rm stat.}\pm 0.018_{\rm syst.})\times10^{-3}$, and $(3.507\pm0.043_{\rm stat.}\pm 0.026_{\rm syst.})\times10^{-3}$ using $e^+e^-$ collision data with an integrated luminosity of 20.3 fb$^{-1}$ collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The partial decay rates of these four decays are measured with the best precision to date and their forward-backward asymmetries are determined for the first time. By performing a simultaneous fit to these results, the product of the hadronic transition form factor $f^{D\toπ}_+(0)$ and the modulus of the $c\to d$ Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ is given by $f^{D\toπ}_+(0)|V_{cd}|=0.1425\pm0.0005_{\rm stat.}\pm0.0003_{\rm syst.}$. Taking the $|V_{cd}|$ provided by the standard model global fit and the $f^{D\toπ}_+(0)$ calculated from the lattice quantum chromodynamics as input, we obtain $f^{D\toπ}_+(0)=0.6339\pm0.0024_{\rm stat.}\pm0.0014_{\rm syst.}$ and $|V_{cd}|=0.2262\pm0.0008_{\rm stat.}\pm0.0005_{\rm syst.}\pm0.0018_{\rm LQCD.}$, respectively. The reported results have the best precision to date. We also search for the scalar current contribution in the $c\to d \ell^+ν_{\ell}$ transition and determine Re$(C_S^μ)=$ $0.022 \pm 0.023_{\rm stat.}\pm 0.003_{\rm syst.}$ and $|{\rm Im}(C_S^μ)|=0.000 \pm $ $0.038_{\rm stat.} \pm 0.012_{\rm syst.}$. In addition, the lepton flavor universality is tested with the ratios of the decay rates between semimuonic and semielectronic decays in full and several $\ell^+ν_\ell$ four-momentum transfer ranges. △ Less

Submitted 26 July, 2026; originally announced July 2026.

Comments: 34 pages, 17 figures

Proton Collectivity in Au+Au Collisions at $\sqrt{s_{\rm NN}}=2.4-4.5$~GeV from a Unified Purely Hadronic EOS without QCD Phase Transition

Authors: Gao-Feng Wei, Shuang-Jie Liu, Yu-Liang Zhao, Qi-Jun Zhi, Zhigang Xiao

Abstract: The nuclear equation of state (EOS) is generally considered to soften in the density range of $2-5$ times the saturation density $ρ_0$. Using a purely hadronic transport model, we calculate the proton directed, sideward, and elliptic flows and their excitation functions in heavy-ion collisions (HICs) at $\sqrt{s_{\rm NN}}=2.4-4.5$~GeV and compare with the HADES, E895, and STAR data. We find that a… ▽ More The nuclear equation of state (EOS) is generally considered to soften in the density range of $2-5$ times the saturation density $ρ_0$. Using a purely hadronic transport model, we calculate the proton directed, sideward, and elliptic flows and their excitation functions in heavy-ion collisions (HICs) at $\sqrt{s_{\rm NN}}=2.4-4.5$~GeV and compare with the HADES, E895, and STAR data. We find that a momentum-dependent mean field with a unified incompressibility $K_0=230$~MeV quantitatively reproduces the experimental proton flows up to 4.3 GeV, at which the maximum density reaches approximately $5ρ_0$. At 4.5 GeV, however, the pure hadronic model fails to reproduce the proton directed and elliptic flow data, providing circumstantial evidence for the onset of partonic degrees of freedom in HICs. Our results provide a hadronic baseline to characterize the high-density nuclear matter and to map the region of hadron-quark phase transition. △ Less

Submitted 25 July, 2026; originally announced July 2026.

Comments: 5 Pages, 5 Figures and 37 references

Measurement of Born Cross Section for $e^+e^-\to K_S^0\barΞ^+Σ^-+\rm{c.c.}$ at $\sqrt{s} = 3.51-4.95$ GeV

Authors: BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko , et al. (737 additional authors not shown)

Abstract: Using $e^+e^-$ collision data collected with the BESIII detector at the BEPCII collider corresponding to a total integrated luminosity of 44~fb$^{-1}$, we present the first measurement of the Born cross sections for the process $e^+e^-\to K_S^0\barΞ^+Σ^-+\rm{c.c.}$ at 56 center-of-mass energies from 3.510 to 4.951~GeV. By fitting the dressed cross sections of $e^+e^-\to K_S^0\barΞ^+Σ^-+\rm{c.c.}$… ▽ More Using $e^+e^-$ collision data collected with the BESIII detector at the BEPCII collider corresponding to a total integrated luminosity of 44~fb$^{-1}$, we present the first measurement of the Born cross sections for the process $e^+e^-\to K_S^0\barΞ^+Σ^-+\rm{c.c.}$ at 56 center-of-mass energies from 3.510 to 4.951~GeV. By fitting the dressed cross sections of $e^+e^-\to K_S^0\barΞ^+Σ^-+\rm{c.c.}$ with the assumption of a power-law function plus a charmonium(-like) resonance, i.e. $ψ(3770)$, $ψ(4040)$, $ψ(4160)$, $Y(4230)$, $Y(4360)$, $ψ(4415)$, {\it Y}(4500), $Y(4660)$, and {\it Y}(4710), no significant signal of any charmonium(-like) state decaying into the $K_S^0\barΞ^+Σ^-+\rm{c.c.}$ is observed. Upper limits on the product of the electronic width and branching fraction at the 90\% confidence level are given for each resonance. Combining this result with the previous measurement of the isospin-symmetric process $e^+e^-\to K^{-} \barΞ^{+} Σ^{0} + \rm{c.c.}$, the ratio of the Born cross sections, $R=σ^{B}(e^+e^-\to K_S^0\barΞ^+Σ^-+\rm{c.c.})/$$σ^{B}(e^+e^-\to K^-\barΞ^+Σ^0+\rm{c.c.})$, is found to be approximately 1. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: 11 Pages, 3 Figures, 1 Appendix, etc

HEMERA: A Heterogeneous Memory-Centric Accelerator with Recursive Dataflow for Edge-Constrained State-Space-Duality Models Inference

Authors: Hao Ding, Ling Liang, Ruitong Qiao, Dongxue Zhao, Xiantong Qiu, Jinshan Li, Meng Li, Lei Jin, Zhiliang Xia, Zongliang Huo, Zongwei Wang, Yimao Cai

Abstract: Structured State Space Models (SSMs), such as Mamba, enable efficient long-sequence modeling with linear time complexity. Recent implementations realize this capability through Structured State Space Duality (SSD), which transforms recursive state evolution into matrix-form computations. However, SSD introduces substantial system-level overheads, including quadratic intermediate materialization, i… ▽ More Structured State Space Models (SSMs), such as Mamba, enable efficient long-sequence modeling with linear time complexity. Recent implementations realize this capability through Structured State Space Duality (SSD), which transforms recursive state evolution into matrix-form computations. However, SSD introduces substantial system-level overheads, including quadratic intermediate materialization, irregular data movement, and prefix-dependent execution, leading to excessive memory traffic and bandwidth demand on conventional architectures. Although prior accelerators mitigate these overheads through optimized dataflows or compute-in-memory techniques, they largely retain matrix-oriented SSD execution and cannot simultaneously avoid quadratic intermediate storage and efficiently map dependency-bound state propagation. This paper presents HEMERA, a heterogeneous memory-centric accelerator for efficient Mamba-2 inference. Rather than directly executing the matrix-form SSD computation, HEMERA reformulates it into an algebraically equivalent streaming-recursive dataflow that avoids quadratic intermediate storage while preserving the original computation. The resulting heterogeneous execution paradigm maps dense linear operations onto in-memory computing units and recursive state updates onto a dedicated streaming engine. Across Mamba-2 models ranging from 130M to 2.8B, HEMERA achieves average latency speedups of 1.4x-3.6x and energy-efficiency improvements of 12.2x-27.0x over the official optimized fused Mamba-2 kernel on NVIDIA A100. It further reduces the average SSD-related execution-time ratio across model scales to 14.12% during long-sequence inference, demonstrating its potential for efficient deployment under edge constraints. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: Accepted for presentation at ICCAD 2026. 9 pages, 12 figures, and 6 tables

AREX: Towards a Recursively Self-Improving Agent for Deep Research

Authors: Shuqi Lu, Chaofan Li, Kun Luo, Zhang Zhang, Hui Wang, Hongwang Xiao, Lei Xiong, Jiahao Wang, Sen Wang, Xiyan Jiang, Wanli Li, Yuyang Hu, Hongjin Qian, Bingyu Yan, Jianlyu Chen, Ziyi Xia, Yingxia Shao, Kang Liu, Zhicheng Dou, Di He, Chaozhuo Li, Qiwei Ye, Zhongyuan Wang, Zheng Liu

Abstract: Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a research agent should do more than simply search longer: it should recursively improve its current answer by verifying intermed… ▽ More Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a research agent should do more than simply search longer: it should recursively improve its current answer by verifying intermediate results and using the partially verified state to guide subsequent refinement. We introduce AREX, a family of Recursively Self-Improving (RSI) deep research agents. AREX alternates between an inner research loop that gathers evidence and constructs a provisional answer, and an outer self-improvement loop that audits the answer constraint-wise, identifies unresolved claims, and launches targeted follow-up research. To sustain RSI over long horizons, AREX learns an autonomous context-update tool that compresses growing interaction history into a compact improvement state preserving verified evidence and unresolved constraints, without relying on an external model. We train AREX on verified synthetic tasks and high-quality trajectories through agentic mid-training and long-horizon reinforcement learning. To mitigate sparse final rewards during long horizon learning, we emphasize key steps where decisive evidence is acquired or erroneous research directions are corrected. We instantiate a dense 4B model and a 122B-A10B Mixture-of-Experts model. Across BrowseComp, WideSearch, DeepSearchQA, Humanity's Last Exam (HLE), and other reasoning and tool-use benchmarks, AREX substantially outperforms comparable-scale baselines and remains competitive with models using substantially more activated parameters. △ Less

Submitted 23 July, 2026; v1 submitted 23 July, 2026; originally announced July 2026.

PhaseAware: Interpretable Human-in-the-Loop Rehabilitation Scoring with Boundary Monitoring

Authors: Yankai Zheng, Yuhe Liu, Yuxin Ma, Tianci Xue, Jiayuan Tian, Yu Fu, Yuxuan Hu, Jianing Wang, Zichun Xiao, Junya Mu, Shaohui Ma

Abstract: Rehabilitation scoring systems are most useful when their outputs can be reviewed and interpreted within clinical workflows. This study presents PhaseAware, a compact framework for continuous rehabilitation quality assessment that combines a temporal backbone with phase- and body-group descriptors through a backbone-conditioned gated residual pathway. The model was evaluated on the UI-PRMD deep-sq… ▽ More Rehabilitation scoring systems are most useful when their outputs can be reviewed and interpreted within clinical workflows. This study presents PhaseAware, a compact framework for continuous rehabilitation quality assessment that combines a temporal backbone with phase- and body-group descriptors through a backbone-conditioned gated residual pathway. The model was evaluated on the UI-PRMD deep-squat protocol and further tested on the KIMORE squatting subset. On UI-PRMD, PhaseAware achieved an RMSE of 0.0230, corresponding to an 88.9% reduction relative to the accepted baseline. It also maintained favorable performance on KIMORE, suggesting that the phase-aware design transfers across related squatting protocols. In addition to score prediction, PhaseAware generates structured review cues based on phase- and body-level sensitivity, highlighting the movement stages and body regions most relevant to each prediction. The architecture employs a backbone-conditioned gated residual mechanism to stabilize feature representation, supporting use in resource-constrained settings. These cues are intended to support clinician review, boundary-case monitoring, and human-in-the-loop triage rather than autonomous decision-making. Overall, PhaseAware offers a practical and interpretable approach to rehabilitation scoring that may help integrate automated assessment into information systems while preserving clinician oversight. △ Less

Submitted 22 July, 2026; originally announced July 2026.

Comments: 22 pages, 4 main figures, 3 tables, and 17 supplementary figures. Supplementary Information is included in the same PDF