Showing 1–50 of 603 results for author: Xing, H

MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulations

Authors: Sky Ng, Brihi Joshi, Ishan Gupta, Shirley Huang, Zonglin Di, Yun Shen, Qianfeng Wen, Yifan Simon Liu, Ruoqi Gao, Yilan, Fan, Zhiwei Zhang, Muhammad Ahmed Mohsin, Yucheng Lu, Xiaoyi Liu, Heming Liu, Qianyu Zhu, Hanwen Xing, Zhengyang Shan, My Chiffon Nguyen, Guanghui Min, Jianheng, Hou, Yunze, Xiao , et al. (25 additional authors not shown)

Abstract: Long-horizon, multi-agent language model (LM) simulations are widely proposed for studying social behavior, yet instruments to measure whether persona-conditioned agents maintain identity fidelity under sustained pressure are lacking. We present MicroVerse, a behavioral-science instrument that measures identity drift in generative agents. Agents carry an immutable "soul file" (core values, moral b… ▽ More Long-horizon, multi-agent language model (LM) simulations are widely proposed for studying social behavior, yet instruments to measure whether persona-conditioned agents maintain identity fidelity under sustained pressure are lacking. We present MicroVerse, a behavioral-science instrument that measures identity drift in generative agents. Agents carry an immutable "soul file" (core values, moral boundaries, personality, goals) and inhabit a resource-scarce 50 x 50 environment where water is a non-respawning survival constraint. Scarcity is operationalized via a per-tick existence-cost gradient. The eight-verb action space maps directly to moral boundaries (trade, talk, attack, scavenge). Using a three-layer memory architecture, agents periodically revise a mutable current identity against their immutable original soul via importance-triggered reflection. To mitigate survivor bias, MicroVerse decouples measurement from behavior using uniform longitudinal engine snapshots every N ticks alongside a forced-end snapshot of all living and dead agents. Identity drift is scored offline using a paraphrase-aware, value-anchored, multi-register diff rather than raw cosine similarity. We evaluate the instrument via a controlled seed run (n = 25) and a reflection-threshold sweep (thresholds {40, 80, 150}) to determine if drift dynamics are gate artifacts or threshold-robust properties. We report two primary findings: (1) Anti-self-deception emerges unprompted as the single largest semantic category of identity modification (27 of 111 added boundaries, 24%). (2) The system is threshold-robust; lower gates accelerate and increase revision frequency but preserve drift direction. All empirical results are strictly preliminary existence proofs and effect shapes (one model, one seed per arm, n = 25) rather than statistical significance claims. △ Less

Submitted 16 August, 2026; originally announced August 2026.

PersonaEval: Persona-Based User Simulation for Evaluating Interactive Applications

Authors: Yifan Simon Liu, Qianfeng Wen, Yilan Fan, Shirley Huang, Ruoqi Gao, Jianheng Hou, Muhammad Ahmed Mohsin, Zonglin Di, Brihi Joshi, Xincheng Tan, Yucheng Lu, Xiaoyi Liu, Heming Liu, Hanwen Xing, Guanghui Min, Zhengyang Shan, My Chiffon Nguyen, Ishan Gupta, Yunze Xiao, Hannah Collison, Jintao Huang, Jiatong Li, Sankalp Jajee, Yunhan Zhao, Bing Hu , et al. (18 additional authors not shown)

Abstract: Real user studies are important for understanding how people interact with systems under test or already deployed. In practice, however, they are often costly, time-consuming, and difficult to scale. To address these challenges, we introduce PersonaEval, a persona-based user simulation framework that approximates real-user behavior across diverse interactive settings. PersonaEval connects simulate… ▽ More Real user studies are important for understanding how people interact with systems under test or already deployed. In practice, however, they are often costly, time-consuming, and difficult to scale. To address these challenges, we introduce PersonaEval, a persona-based user simulation framework that approximates real-user behavior across diverse interactive settings. PersonaEval connects simulated users drawn from existing persona datasets to task-specific application interfaces and collects the interaction trajectories and outcomes. PersonaEval provides a plug-and-play evaluation workflow in which the application being evaluated can be easily changed. In this demo, we present PersonaEval on three forms of interactive applications: surveys, chatbots, and web applications. Together, these examples show that PersonaEval can support repeatable, parallelizable, and scalable evaluation across different interaction settings, while producing user-oriented feedback and task-specific behavior. △ Less

Submitted 16 August, 2026; originally announced August 2026.

Demystifying Agent Skills: Why They Work-Until They Don't

Authors: Zhiyuan Jiang, Fangrui Huang, Hanwen Xing, Xander Wu, Yipeng Gao, Rui Cao, Mengdi Wang, Shilong Liu, Yijiang Li

Abstract: Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However, existing evaluations largely measure whether skills improve aggregated task success, leaving a more fundamental question underexplored: \emph{\textbf{When do skills help, why do they work, and where do they fail?}} Through controlled experiments ac… ▽ More Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However, existing evaluations largely measure whether skills improve aggregated task success, leaving a more fundamental question underexplored: \emph{\textbf{When do skills help, why do they work, and where do they fail?}} Through controlled experiments across various benchmarks, agent harnesses and LLMs, we isolate the effects of representation, outcome annotation, retrieval difficulty, and cross-framework robustness of skills. To further answer this question, we design a contrastive study that combines controlled quantitative experiments with paired trajectory analysis. We normalize 8,135 trial records from controlled experiments and retain 238 valid unique labels from 240 open-coded records. We consolidate these observations into a taxonomy of three high-level categories and twelve skill-use modes: skills work when noisy trajectories become procedural anchors that stabilize execution. Skills improve over Workflow Memory by 6.06 points in matched comparisons. Procedural anchoring accounts for 65.7\% of skill cases, versus 4.5\% for explicit knowledge injection, showing that skills stabilize action rather than inject missing facts. Retrieval is a separate bottleneck: as pools grow from 5 to 100, actual-use precision falls from 29.6\% to 3.3\%. Confusable distractors impair offline identification, yet downstream success remains stable; exact ground-truth invocation is neither sufficient nor necessary. Skills fail under brittle assumptions, incompatible contexts, or insufficient adaptation. These findings move evaluation beyond aggregate success rates and guide reliable self-evolving agents. △ Less

Submitted 14 August, 2026; originally announced August 2026.

TEMPER: Tensorized Efficient Manifold-constrained Parameterization for Expressive Residual Routing

Authors: Yuxuan Gu, Wuyang Zhou, Huijun Xing, Danilo Mandic

Abstract: Residual connections rely on a static residual pathway, and are essential for training deep neural networks. Hyper-connections (HC) increase the expressivity of residual routing by incorporating multiple residual streams and learning dynamic information flow, while manifold-constrained (mHC) variants stabilize training through doubly stochastic residual mixing. However, a generator-level bottlenec… ▽ More Residual connections rely on a static residual pathway, and are essential for training deep neural networks. Hyper-connections (HC) increase the expressivity of residual routing by incorporating multiple residual streams and learning dynamic information flow, while manifold-constrained (mHC) variants stabilize training through doubly stochastic residual mixing. However, a generator-level bottleneck remains in existing methods: they use dense, unstructured generators for pre-branch aggregation, residual mixing, and post-branch redistribution, which results in parameter count growing rapidly with the number of streams. To address this issue, we propose \underline{\textbf{T}}ensorized \underline{\textbf{E}}fficient \underline{\textbf{M}}anifold-constrained \underline{\textbf{P}}arameterization for \underline{\textbf{E}}xpressive Residual \underline{\textbf{R}}outing (\textbf{TEMPER}), which represents these generators as multi-way tensors over the input-stream, feature, and output-stream modes, and parameterizes them using tensor networks. Such a structured low-rank formulation is shown to preserve token-dependent manifold-constrained routing interface while substantially reducing parameter growth. It also promotes interpretability and intuition, as: i) tensor ranks control the dimensionality of the learned routing subspace, with full ranks recovering dense routing; while ii) the generator approximation errors bound differences in routing logits and, consequently, in the routed-block outputs. Comprehensive experiments show that TEMPER matches or outperforms existing methods across language modeling and commonsense reasoning tasks, while requiring substantially fewer additional parameters. At eight residual streams, TEMPER achieves the best CORE score while using about $84\%$ fewer additional parameters than mHC, thus showing a stronger performance-parameter efficiency trade-off. △ Less

Submitted 7 August, 2026; originally announced August 2026.

Anisotropic Particle Transport from a Pulsar Wind Nebula Revealed by Einstein Probe and LHAASO

Authors: Zhen Cao, F. Aharonian, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, W. Bian, J. Blunier, A. V. Bukevich, C. M. Cai, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, E. S. Chen, G. H. Chen, H. K. Chen, L. F. Chen, Liang Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen, S. Chen , et al. (320 additional authors not shown)

Abstract: Pulsar wind nebulae (PWNe) are major cosmic ray accelerators, yet the mechanisms transporting high-energy particles into the interstellar medium remain elusive. Building on the LHAASO discovery of an ultra-high-energy (UHE) $γ$-ray source near the bow-shock PWN powered by the pulsar PSR J1740+1000, we present a joint Einstein Probe (EP) and LHAASO study of this system. EP observations reveal an ex… ▽ More Pulsar wind nebulae (PWNe) are major cosmic ray accelerators, yet the mechanisms transporting high-energy particles into the interstellar medium remain elusive. Building on the LHAASO discovery of an ultra-high-energy (UHE) $γ$-ray source near the bow-shock PWN powered by the pulsar PSR J1740+1000, we present a joint Einstein Probe (EP) and LHAASO study of this system. EP observations reveal an extended X-ray tail far exceeding the structure previously seen by XMM-Newton. Updated LHAASO observations show that the $γ$-ray emission is elongated, with its major axis aligned with the extended X-ray tail revealed by EP. This is the first detection of an X-ray pulsar tail associated with a spatially coincident extended UHE $γ$-ray emission. The X-ray and $γ$-ray spectrum can be well explained with a single population of relativistic electrons via synchrotron and inverse Compton radiation, respectively, removing the need for particle re-acceleration during propagation. The results unambiguously show that electrons/positrons above 100 TeV are escaping from the PWN. Instead of the immediate, isotropic diffusion into ambient interstellar medium that is typically assumed, these particles are transported anisotropically over at least $\sim$10 pc, either guided by the background magnetic field or carried by an advective outflow. △ Less

Submitted 7 August, 2026; originally announced August 2026.

Comments: Accepted by Science China Physics, Mechanics, and Astronomy. Main text: 9 pages, 4 figures, 1 table; Supplementary Materials: 7 pages, 2 figures, 4 tables

Measurement-induced generation of Schrödinger cat states in cavity QED

Authors: Tong Wang, Peng-Fei Wei, Hai-Jun Xing, Zhihai Wang

Abstract: Schrödinger cat states, representing coherent superpositions of macroscopically distinguishable states, are indispensable nonclassical resources for continuous-variable quantum information processing. Existing generation protocols typically rely on strong nonlinear interactions, complicated control techniques, or engineered dissipation, posing challenges for experimental implementation. Here, we p… ▽ More Schrödinger cat states, representing coherent superpositions of macroscopically distinguishable states, are indispensable nonclassical resources for continuous-variable quantum information processing. Existing generation protocols typically rely on strong nonlinear interactions, complicated control techniques, or engineered dissipation, posing challenges for experimental implementation. Here, we propose a simple measurement-based protocol for generating Schrödinger cat states in a cavity-QED system by combining coherent driving, dispersive atom--cavity interactions, and atomic postselection. The atom--cavity interaction establishes coherent correlations between the atomic and photonic degrees of freedom, while the subsequent atomic postselection projects the cavity field onto a non-Gaussian superposition state with pronounced Wigner negativity. Numerical simulations based on the Lindblad master equation show that the generated Schrödinger cat states remain robust against moderate cavity dissipation. Our results demonstrate that conditional atomic measurements provide an effective and experimentally accessible approach for preparing nonclassical cavity states without relying on strong optical nonlinearities or engineered dissipation. △ Less

Submitted 5 August, 2026; originally announced August 2026.

Comments: 7 Pages, 3 Figures, Comments are welcomed

MatrAIx: Simulating the World with 8.3 Billion Persona Agents

Authors: Xiaomin Li, Yuexing Hao, Jianheng Hou, Jintao Huang, Qianfeng Wen, Shirley Huang, Yifan Liu, Xiaoyi Liu, Yilan Fan, Yijun Wang, Koutian Wu, Ruoqi Gao, Muhammad Ahmed Mohsin, Jing Tang, Brihi Joshi, Heming Liu, Zheyuan Deng, Zonglin Di, Sankalp Jajee, Jiuyao Lu, Zhiwei Zhang, Saksham Kapoor, Ishan Gupta, Yunhan Zhao, Chanwoo Park , et al. (68 additional authors not shown)

Abstract: Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and interactive behavior. We therefore introduce MatrAIx, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users. MatrAIx has three core components: First,… ▽ More Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and interactive behavior. We therefore introduce MatrAIx, a population-scale simulated-user evaluation infrastructure for testing AI systems and digital products with heterogeneous users. MatrAIx has three core components: First, Persona 8B contains 8.3 billion persona records represented by 1,290 categorical dimensions. Records are either sampled from a dependency graph that preserves correlated attributes or derived from human-authored profiles. We release a quality-filtered coreset of approximately 1 million personas, comprising 599,847 human-grounded and 400,000 synthetic records. Second, the MatrAIx Playground provides four environments in which diverse users evaluate and interact with digital products: Survey, AI Chatbot, Web, and App. Third, MatrAIx provides 1,010 application tasks spanning more than 25 domains, including Commerce, Software, Finance, and Healthcare. We conducted 18,189 evaluation trials across eight representative tasks. Persona agents were powered by three LLMs: Claude Opus 4.8, GPT 5.5, and Claude Haiku 4.5. The resulting feedback captures how decisions and preferences vary across persona backgrounds, including hesitation after a price increase, willingness to continue after an AI assistant fails, and latency tolerance. We conducted two main validation studies: First, a 400-trial controlled study evaluated persona adherence across ten behavioral attributes and all four environments. The declared behavior was expressed or correctly suppressed in 366 trials (91.5%). Second, human and LLM judges evaluated the extraction quality of human-grounded personas. Overall, MatrAIx provides an end-to-end infrastructure for evaluating AI systems and digital products with diverse simulated human users. △ Less

Submitted 4 August, 2026; originally announced August 2026.

Comments: Project website: https://matraix.ai

CurveShift: Is Agent Progress Scalar? Separating Level from Shape

Authors: Hanwen Xing, Pengyun Wang, BingXu Meng, Kumail Alhamoud, Xiang Li, Jicheng Wang, Xin Yu, Xinyang Han, Xiaomin Li, Philip Torr, Yuexing Hao

Abstract: Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summaries capture the overall performance, but they do not test whether progress is distributed differently across task difficulty. We find that most of the apparent shift in gains toward harder tasks does not reflect a chang… ▽ More Progress in large language models is often summarized using a single scalar measure, such as a time horizon, a latent ability estimate, or an aggregate benchmark score. These summaries capture the overall performance, but they do not test whether progress is distributed differently across task difficulty. We find that most of the apparent shift in gains toward harder tasks does not reflect a change in the shape of the difficulty-response curve. On METR time-horizon data, a single Rasch model with rising ability reproduces this pattern, so it is largely explained by ceiling effects rather than a qualitative change in capability. This echoes how the choice of metric can make claimed emergent abilities look like a property of the models themselves. We then identify a smaller hard-task effect that survives this control. Isolating it is difficult on agentic benchmarks, because newer models are usually run with newer agentic harnesses, so a gain on hard tasks cannot be assigned to the model or its scaffold. We break the confound with LiveCodeBench, a public competitive programming benchmark that runs no agentic scaffold while pairing dated models with an exogenous difficulty ordering. After accounting for the rise in overall ability, models released after September 2024 still gain on the hardest problems beyond what their easy and medium performance predicts, by about +0.40 logits under our most conservative assumption, raising the hard-problem solve rate from roughly 18% to 25%. The effect is led by the strongest reasoning models and holds for hard tasks that need only short reasoning, not autonomy over long horizons. We present this as a result specific to competitive programming, since our clean identification rests on a single coding benchmark. We release the LiveCodeBench Difficulty Panel (66 dated models x 1,055 problems) and our analysis code. △ Less

Submitted 31 July, 2026; originally announced August 2026.

Comments: 25 pages, 4 figures, 7 tables. Data and code: https://github.com/harvenstar/CurveShift

ACM Class: I.2.7; I.2.6; G.3

MVLA-GR: A Phase-Free Multipath-Based Geometry Reconstruction Method via Multi-View Likelihood Accumulation for ISAC

Authors: Bowei Xing, Yuxiang Zhang, Jianhua Zhang, Yifeng Xiong, Hongbo Xing, Li Yu, Guangyi Liu

Abstract: Integrated sensing and communication (ISAC) enables wireless systems to reuse communication signals for environmental sensing, where reconstructing the geometry of surrounding objects is a representative sensing task. However, many conventional methods rely on coherent processing and require accurate phase information, which is often hard to guarantee in practical communication systems, particular… ▽ More Integrated sensing and communication (ISAC) enables wireless systems to reuse communication signals for environmental sensing, where reconstructing the geometry of surrounding objects is a representative sensing task. However, many conventional methods rely on coherent processing and require accurate phase information, which is often hard to guarantee in practical communication systems, particularly at high carrier frequencies. To address this problem, this paper proposes a Multi-View Likelihood Accumulation Geometry Reconstruction (MVLA-GR) method based on channel impulse response (CIR) measurements, which uses only delay and power observations without requiring phase information. The method extracts dominant multipath components from each observation, and for each candidate spatial location, accumulates components across views whose propagation distances match the location as supporting evidence. A soft distance-matching kernel is introduced to tolerate range estimation errors and viewpoint-dependent scattering migration, and the received power of each component is used as a reliability weight. A joint thresholding strategy combining response magnitude and angular support continuity then converts the continuous support map into a binary geometry estimate. Ray-tracing simulations on canonical and complex targets, as well as real-world vehicle measurements at 36 GHz, demonstrate that MVLA-GR can effectively recover target geometry, providing a low-complexity phase-free solution for ISAC. △ Less

Submitted 28 July, 2026; originally announced July 2026.

Comments: 13 pages, 10 figures. Submitted to IEEE Transactions on Wireless Communications

SADe: Sparse-Atom Support Decontamination for Few-Shot Segmentation with Weak Support Annotations

Authors: Hang Xing, Guangjun Liu, Yan Xia, Xueming Ding

Abstract: Few-shot segmentation (FSS) commonly assumes clean pixel-level support masks, yet practical support supervision often uses boxes, scribbles, coarse masks, or pseudo-masks. These weak annotations may include texture-similar distractors and background context alongside the target, contaminating class prototypes or visual prompts before query prediction. We introduce SADe, a predictor-agnostic suppor… ▽ More Few-shot segmentation (FSS) commonly assumes clean pixel-level support masks, yet practical support supervision often uses boxes, scribbles, coarse masks, or pseudo-masks. These weak annotations may include texture-similar distractors and background context alongside the target, contaminating class prototypes or visual prompts before query prediction. We introduce SADe, a predictor-agnostic support decontamination layer that estimates the reliability of selected support patches without query information. Central to SADe is sparse autoencoder (SAE) atom evidence: dense similarity may respond to both target and texture-similar context, whereas contrasting atom activations inside and outside the weak-support region provides factor-level reliability cues. A lightweight router combines atom evidence with dense similarity and episode statistics to predict patch reliability and generate a cleaned support mask. Trained once on synthetic weak-support episodes from FSS-1000, the router is frozen for all target evaluations. The resulting mask supports standalone prediction or can be supplied to heterogeneous FSS models through native support interfaces without altering query-side inference. Under a matched weak-support protocol, SADe achieves the highest query mIoU in six of nine standalone prompt-shot combinations. With the same ProMi query head, it is within 0.03 mIoU of SAM3-derived masks under tight boxes and surpasses them by 11.17 and 19.49 points under box-r2 and box-r4, respectively. As a plug-in, SADe improves over raw support in 70 of 72 matched box-family comparisons across four frozen downstream models and two datasets. On point and scribble prompts, its average performance remains close to the corresponding raw-support baseline. Ablations and atom-removal controls show that atom evidence contributes reliability information beyond dense similarity. △ Less

Submitted 29 July, 2026; v1 submitted 27 July, 2026; originally announced July 2026.

Comments: 19 pages, 8 figures; includes a technical appendix

Latent Fragility and Clustered Withdrawals in Dynamic Banks Runs

Authors: Jodi Dianetti, Giorgio Ferrari, Yunzhi Hu, Hao Xing

Abstract: Using a mean-field game framework, we study a dynamic model of bank runs in which more withdrawals raise the risk of bank failure. Even though depositors receive gradual and idiosyncratic shocks, withdrawals occur in clusters. The main mechanism is latent fragility: run-prone depositors accumulate gradually over time and may prefer to wait individually, but they withdraw together once collective e… ▽ More Using a mean-field game framework, we study a dynamic model of bank runs in which more withdrawals raise the risk of bank failure. Even though depositors receive gradual and idiosyncratic shocks, withdrawals occur in clusters. The main mechanism is latent fragility: run-prone depositors accumulate gradually over time and may prefer to wait individually, but they withdraw together once collective exit becomes self-fulfilling. We establish equilibrium existence and characterize earliest-run and latest-run equilibria. The clustering mechanism arises whether depositor heterogeneity is discrete or continuous. A common aggregate state coordinates withdrawal timing and leads to a unique threshold equilibrium. △ Less

Submitted 24 July, 2026; originally announced July 2026.

Comments: 67 pages, 5 figures

The Extended Ultrahigh-energy Gamma-Ray Emission in the Vicinity of PSR J2238+5903

Authors: Zhen Cao, F. Aharonian, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, W. Bian, J. Blunier, A. V. Bukevich, C. M. Cai, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, E. S. Chen, G. H. Chen, H. K. Chen, L. F. Chen, Liang Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen, S. Chen , et al. (305 additional authors not shown)

Abstract: We present a comprehensive analysis of the recently discovered TeV gamma-ray source, LHAASO J2238+5900. Based on data collected from the LHAASO, our fitting results suggest that the source is significantly extended with an angular extension of 0.54° \pm 0.01° and is spatially coincident with the pulsar PSR J2238+5903. Its spectrum is characterized by a power-law with a cutoff at 41.0\pm 3.5 TeV. A… ▽ More We present a comprehensive analysis of the recently discovered TeV gamma-ray source, LHAASO J2238+5900. Based on data collected from the LHAASO, our fitting results suggest that the source is significantly extended with an angular extension of 0.54° \pm 0.01° and is spatially coincident with the pulsar PSR J2238+5903. Its spectrum is characterized by a power-law with a cutoff at 41.0\pm 3.5 TeV. Additionally, the source exhibits a significant signal of 7.9σabove 100 TeV, implying that it is a PeVatron candidate. While the gamma-ray emission is consistent with a pulsar wind nebula (PWN) scenario, the relatively large extension size also allows for a halo interpretation, potentially caused by electron-positron pairs escaping from the PWN. △ Less

Submitted 23 July, 2026; originally announced July 2026.

High-Q superconducting microwave resonators using MBE titanium nitride

Authors: Anand Ithepalli, Haoran Lu, Eegene Clara Chung, Xiangqin Wang, Amit Rohan Rajapurohita, Keun-Yeol Park, Celesta S. Chang, Peter McMahon, Huili Grace Xing, David Muller, Valla Fatemi, Debdeep Jena

Abstract: Using molecular beam epitaxy, we have realized thin films of titanium nitride (TiN) on c-plane sapphire that exhibit the lowest observed full-width at half maximum X-ray rocking curve width of 18 arcsec. Though the (111) oriented TiN exhibits an abrupt and crystalline interface with sapphire, for the first time we observe sub-surface defects in the sapphire substrate, which nucleate structural def… ▽ More Using molecular beam epitaxy, we have realized thin films of titanium nitride (TiN) on c-plane sapphire that exhibit the lowest observed full-width at half maximum X-ray rocking curve width of 18 arcsec. Though the (111) oriented TiN exhibits an abrupt and crystalline interface with sapphire, for the first time we observe sub-surface defects in the sapphire substrate, which nucleate structural defects in the epitaxial TiN layer. Using quarter-wavelength coplanar waveguide (CPW) resonators in a 3 \textmu m/6 \textmu m/3 \textmu m gap/strip/gap lines in a hanger geometry, we find the internal quality factor of the TiN resonators to be $>10^{6}$ in the single-photon $\langle n \rangle \sim 1$ limit at 5.8 GHz and 10 mK, rising to $>20 \times 10^{6}$ at $\langle n \rangle \sim 10^{6}$. The results are of high interest for applications of superconducting TiN in several areas, and provide a path towards epitaxial Josephson junctions with crystalline barriers in the future for high coherence qubits. △ Less

Submitted 19 July, 2026; v1 submitted 16 July, 2026; originally announced July 2026.

Comments: 8 pages and 5 figures

Intrinsic Spatial Position Resolution of P-type Point-Contact Germanium Detector

Authors: R. M. J. Li, S. K. Liu, S. T. Lin, Q. Y. Li, L. T. Yang, Q. Yue, Q. Wang, H. Y. Li, X. Y. Peng, H. Y. Xing, J. J. Zhu

Abstract: The p-type point-contact germanium detectors have emerged as the ideal detection technology for rare-event experiments such as direct dark matter searches and neutrinoless double beta decay, and have been verified to be capable of single-site spatial position resolution. Accurately characterizing the position-dependent pulse shape responses of the detector is a crucial prerequisite for deepening b… ▽ More The p-type point-contact germanium detectors have emerged as the ideal detection technology for rare-event experiments such as direct dark matter searches and neutrinoless double beta decay, and have been verified to be capable of single-site spatial position resolution. Accurately characterizing the position-dependent pulse shape responses of the detector is a crucial prerequisite for deepening background understanding and achieving background reduction. Relying on an optimized cross-scanning localization method and a full-chain physical framework, this study extracted the pulse shape responses in critical regions of the CDEX detector, quantitatively evaluated its intrinsic spatial position resolution for the first time, and ultimately achieved the position tracing of real environmental backgrounds using the constructed pulse shape database. This study completely establishes a physical analysis closed-loop for spatial position resolution, providing critical theoretical and technical support for background analysis in future ton-scale arrays. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Comments: 13 pages, 15 figures, submitted to Chinese Physics C

Second-Order Optical Nonlinearity of AlScN Films Grown By Molecular Beam Epitaxy

Authors: Joongwon Lee, Thai-Son Nguyen, Len van Deurzen, Debaditya Bhattacharya, Chandrashekhar Savant, Siddhartha Ghosh, Patrick Shea, Carl Bernard, Huili Grace Xing, Debdeep Jena, Farhan Rana

Abstract: Alloys of AlN have rapidly emerged as a material platform for nonlinear optics. In this paper, we measure the second-order optical nonlinearity of AlScN films grown directly on nitrided c-plane sapphire by molecular beam epitaxy. This direct growth approach, which bypasses a thick AlN buffer layer, allows us to isolate the true nonlinear response of the AlScN film. Our results show a large enhance… ▽ More Alloys of AlN have rapidly emerged as a material platform for nonlinear optics. In this paper, we measure the second-order optical nonlinearity of AlScN films grown directly on nitrided c-plane sapphire by molecular beam epitaxy. This direct growth approach, which bypasses a thick AlN buffer layer, allows us to isolate the true nonlinear response of the AlScN film. Our results show a large enhancement of d31, but a suppression of d33 in AlScN films compared to AlN. We observe that d31 can be as high as 4.92 pm/V , which is 60 times larger than that of AlN. The development of AlScN-based photonic devices can enable energy-efficient nonlinear optical operations that can be epitaxially integrated with electronic and photonic devices based on Si, GaN and AlN. △ Less

Submitted 16 July, 2026; originally announced July 2026.

Comments: 7 pages, 4 figures

Local well-posedness for nonlinear Dirac equation on $N$-star metric graphs

Authors: Huichao Xing, Zhipeng Yang

Abstract: We consider the Cauchy problem for the nonlinear Dirac equation on a noncompact $N$-star metric graph $G$, \[ \mathrm{i}\partial_t ψ= Dψ- |ψ|^{p-2}ψ, \qquad ψ(0)=ψ_0, \] where $p\ge3$, $ψ:\mathbb{R}\times G\to\mathbb{C}^2$ and $D$ denotes the self-adjoint Dirac-Kirchhoff operator on $G$. Using Bourgain-type spaces defined through the spectral resolution of $D$, together with elementary $L^\infty$… ▽ More We consider the Cauchy problem for the nonlinear Dirac equation on a noncompact $N$-star metric graph $G$, \[ \mathrm{i}\partial_t ψ= Dψ- |ψ|^{p-2}ψ, \qquad ψ(0)=ψ_0, \] where $p\ge3$, $ψ:\mathbb{R}\times G\to\mathbb{C}^2$ and $D$ denotes the self-adjoint Dirac-Kirchhoff operator on $G$. Using Bourgain-type spaces defined through the spectral resolution of $D$, together with elementary $L^\infty$ bounds for the Dirac flow and fractional Nemytskii estimates below the trace threshold, we prove local well-posedness for initial data \[ ψ_0\in H_D^s(G)\cap L^\infty(G;\mathbb C^2), \qquad 0\le s<\frac12 . \] The corresponding solution belongs to \[ C([0,T];H_D^s(G))\cap X_T^{s,b}\cap L^\infty([0,T]\times G). \] Moreover, $\|ψ(t)\|_{L^2(G;\mathbb{C}^2)}$ is conserved along the solution on the existence interval. We also establish a blow-up alternative in the combined $H_D^s$ and space-time $L^\infty$ control norm. △ Less

Submitted 10 July, 2026; originally announced July 2026.

Comments: 20 pages, comments are welcome

MSC Class: 35Q41; 35A01; 81Q35

Analysis of Nuclear Fragmentation Functions for Pions with $A$ and $ν$ Dependence

Authors: Mengyang Li, Zijian Ye, Jun Gao, Xiaomin Shen, Hongxi Xing, Yuxiang Zhao

Abstract: We present a QCD analysis of pion nuclear fragmentation functions (nFFs), which encode nuclear modifications to hadronization in high-energy nuclear collisions. Within this framework, vacuum fragmentation functions and their nuclear modifications are extracted simultaneously. The nuclear effects are parameterized as functions of the mass number $A$, the energy of the fragmenting parton in the targ… ▽ More We present a QCD analysis of pion nuclear fragmentation functions (nFFs), which encode nuclear modifications to hadronization in high-energy nuclear collisions. Within this framework, vacuum fragmentation functions and their nuclear modifications are extracted simultaneously. The nuclear effects are parameterized as functions of the mass number $A$, the energy of the fragmenting parton in the target rest frame $ν$, and the hadron energy fraction $z$, allowing their dependence on these variables to be quantified. Our analysis includes semi-inclusive deep-inelastic scattering data on nuclear targets, with kinematic cuts chosen to ensure the applicability of perturbative QCD and collinear factorization. The resulting fit provides a good description of most datasets, with the nFFs well constrained in the energy fraction range $z \in [0.2, 0.7]$. Additionally, with our new nFFs, we present next-to-leading order predictions in $pp$ and $pA$ collisions, which show reasonable agreement with ALICE data within the current experimental uncertainties. △ Less

Submitted 9 July, 2026; originally announced July 2026.

Comments: 22 pages, 14 figures

DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification

Authors: Shuang Wang, Chenxu Wang, Hantong Xing, Hanlin Mo, Lirong Han, Licheng Jiao

Abstract: The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models. While existing UDA methods alleviate this problem by aligning source and target features, they give limited consideration to modulation-specific structures that remain informative across domain cond… ▽ More The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models. While existing UDA methods alleviate this problem by aligning source and target features, they give limited consideration to modulation-specific structures that remain informative across domain conditions. In this paper, we consider signal prior knowledge, grounded in communication protocols and physical principles, as a potential way to enhance cross-domain representation learning. Given that different priors may vary in modulation discriminability, domain stability, and complementarity, this paper first analyzes five commonly adopted signal representations that instantiate different signal priors. From them, in-phase/quadrature (IQ), amplitude--phase (AP), and autocorrelation function (ACF) are selected as compact prior-guided inputs. Based on that, a dual knowledge and data-driven network (DKDNet) is proposed for cross-domain AMC. The multi-representation feature encoder (MRFE) and dynamic lightweight fusion unit (DLFU) are designed to achieve unified representation learning and adaptive feature fusion, and the resulting fused features are optimized with modulation classification and adversarial domain alignment objectives. Experiments on both simulated and public datasets validate the rationality of the prior selection and demonstrate the superiority of the proposed method. △ Less

Submitted 8 July, 2026; originally announced July 2026.

Comments: 13 pages, 6 figures, 9 tables

EvoRec: Self Evolving Agentic Recommender Systems

Authors: Lingyu Mu, Hao Deng, Haibo Xing, Jinxin Hu, Yu Zhang, Xiaoyi Zeng

Abstract: Optimizing modern recommender systems still relies heavily on engineers iterating by hand, which is slow and bounded by individual expertise. LLM-based agents open a path toward automating this loop, yet two issues remain. First, the agent is used only as a code translator and accumulates no methodology across iterations. Second, the optimization space is confined to a predefined range and rarely… ▽ More Optimizing modern recommender systems still relies heavily on engineers iterating by hand, which is slow and bounded by individual expertise. LLM-based agents open a path toward automating this loop, yet two issues remain. First, the agent is used only as a code translator and accumulates no methodology across iterations. Second, the optimization space is confined to a predefined range and rarely introduces structurally new ideas. To address these problems, we propose EvoRec, a multi-agent framework that co-evolves the recommendation model and the optimization methodology driving it. Four collaborating agents carry out a dual-track loop: the Research Agent and Code Agent iterate the model each round, while the Skill Evolver periodically distills reusable methodology from a persistent Memory of past experiments. Experiments on two public benchmarks and one large-scale industrial dataset show that EvoRec improves offline metrics by up to 5.54% over the strongest baseline, and an online A/B test delivers a 1.85% revenue lift and a 1.02% CTR gain. △ Less

Submitted 15 June, 2026; originally announced June 2026.

Extreme PeV accelerator associated with GRS 1915+105

Authors: Zhen Cao, F. Aharonian, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, W. Bian, J. Blunier, A. V. Bukevich, C. M. Cai, Y. Y. Cai, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, E. S. Chen, G. H. Chen, H. K. Chen, L. F. Chen, Liang Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen , et al. (304 additional authors not shown)

Abstract: Microquasars, binary systems featuring relativistic jets, have emerged as sources for particle acceleration beyond PeV energies. We present a study of the broadband $γ$-ray emission from one of the most prominent Galactic microquasars GRS 1915+105 based on data accumulated by LHAASO and Fermi-LAT over 4 and 17 years, respectively. A joint analysis of LHAASO-WCDA and LHAASO-KM2A data reveals extend… ▽ More Microquasars, binary systems featuring relativistic jets, have emerged as sources for particle acceleration beyond PeV energies. We present a study of the broadband $γ$-ray emission from one of the most prominent Galactic microquasars GRS 1915+105 based on data accumulated by LHAASO and Fermi-LAT over 4 and 17 years, respectively. A joint analysis of LHAASO-WCDA and LHAASO-KM2A data reveals extended $γ$-ray emission whose centroid appears significantly shifted, by ~ 0.13°, from the binary system and its jets. The spectral energy distribution is well described by a curved spectrum with progressive steepening that can be described by a log-parabola function with no evidence for a sharp cutoff, consistent with parent particles reaching multi-PeV energies and an extreme acceleration efficiency approaching the limit set by the available potential drop across the source. Several features, most notably the shift of the emission and single-power-law spectrum down to GeV band, favor radiation by cosmic rays accelerated in the source interacting with the dense ambient medium. Our spectral modeling implies that at least a few percent of the jet mechanical power is transferred to protons, whose maximum energy reaches beyond 5 PeV. These results strengthen the case for microquasars as exceptionally efficient accelerators in our Galaxy. △ Less

Submitted 25 June, 2026; v1 submitted 23 June, 2026; originally announced June 2026.

Comments: 10 pages, 4 figures, with supplementary material. Corrected a typo in the y-axis units of Fig. 2

OpenThoughts-Agent: Data Recipes for Agentic Models

Authors: Negin Raoof, Richard Zhuang, Marianna Nezhurina, Etash Guha, Atula Tejaswi, Ryan Marten, Charlie F. Ruan, Tyler Griggs, Alexander Glenn Shaw, Hritik Bansal, E. Kelly Buchanan, Artem Gazizov, Reinhard Heckel, Chinmay Hegde, Sankalp Jajee, Daanish Khazi, Emmanouil Koukoumidis, Xiangyi Li, Hange Liu, Shlok Natarajan, Harsh Raj, Nicholas Roberts, Ethan Shen, Nishad Singhi, Michael Siu , et al. (25 additional authors not shown)

Abstract: Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typically target a single benchmark, leaving open the question of how to train models that generalize across diverse agentic tasks. The OpenThoughts-Agent (OT-Agent) project… ▽ More Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typically target a single benchmark, leaving open the question of how to train models that generalize across diverse agentic tasks. The OpenThoughts-Agent (OT-Agent) project addresses this gap with a fully open data curation pipeline for training agentic models. We conduct more than 100 controlled ablation experiments to systematically investigate each stage of the pipeline, yielding insights on the importance of task sources and diversity. We then assemble a training set of 100K examples from our pipeline and fine-tune Qwen3-32B on this dataset, which yields an average accuracy of 44.8% across seven agentic benchmarks and a 3.9 percentage point improvement over the strongest existing open data agentic model (Nemotron-Terminal-32B, 40.9%). Moreover, our training data exhibits strong scaling properties, outperforming alternative open datasets at every training set size in compute-controlled comparisons. We publicly release our training sets, data pipeline, experimental data, and models at openthoughts.ai to support future open research on agentic model training. △ Less

Submitted 23 June, 2026; originally announced June 2026.

Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity

Authors: Yuanhe Zhao, Tianyu Zhang, Huafei Xing, Derek F. Wong, Jianbin Li, Tao Fang

Abstract: Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts. To address this, we propose a multi-agent framework that sanitizes retrieved content through semantic rewriting. By employing three specialized agents for privacy extraction, semantic analysis, and reconstruction, ou… ▽ More Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious prompts. To address this, we propose a multi-agent framework that sanitizes retrieved content through semantic rewriting. By employing three specialized agents for privacy extraction, semantic analysis, and reconstruction, our approach collaboratively removes sensitive identifiers while preserving the semantic core. We evaluate the framework on the ChatDoctor and Wiki-PII datasets across six large language models. Experimental results demonstrate a significant reduction in privacy leakage under targeted attacks. For instance, we reduced targeted information exposure in LLaMA-3-8B from 144 instances in the baseline to just 1. Furthermore, we maintain strong contextual fidelity with a BLEU-1 score of 0.122, outperforming the existing SAGE method's 0.117. Finally, the framework operates as an asynchronous preprocessing module, introducing no additional latency to online inference, as all rewriting is executed as a one-time offline preprocessing step. To promote reproducibility, the source code of this work is publicly available at https://github.com/foursoils/Privacy-Preserving-RAG. △ Less

Submitted 23 June, 2026; originally announced June 2026.

Comments: This full manuscript contains 23 pages and has been formally accepted for publication in Information Processing & Management (Elsevier IPM). Tao Fang is the corresponding author

Quantum Simulation of Generalized Parton Distributions in the Schwinger Model

Authors: Tianyin Li, Hongxi Xing

Abstract: We present a quantum algorithm for simulating Generalized Parton Distributions (GPDs) in the Schwinger model. Unlike the staggered fermions widely utilized in current quantum simulations, we employ Wilson fermions for lattice discretization. This choice is critical for the quantum computation of GPDs due to their strict preservation of charge conjugation symmetry. We construct a comprehensive algo… ▽ More We present a quantum algorithm for simulating Generalized Parton Distributions (GPDs) in the Schwinger model. Unlike the staggered fermions widely utilized in current quantum simulations, we employ Wilson fermions for lattice discretization. This choice is critical for the quantum computation of GPDs due to their strict preservation of charge conjugation symmetry. We construct a comprehensive algorithmic framework that includes the preparation of hadronic states with non-zero momentum and the measurement of light-cone correlation functions incorporating Wilson lines. We provide a complexity analysis, demonstrating that the resources required for our algorithm scale polynomially with both the number of qubits and the desired precision $\varepsilon$. Finally, we benchmark our approach using exact diagonalization, extracting mass spectra and GPDs (also parton distribution functions) that are consistent with theoretical expectations and fundamental physical constraints. △ Less

Submitted 21 June, 2026; originally announced June 2026.

Comments: 9 pages, 7 figures

Report number: RIKEN-iTHEMS-Report-26

How Should Agents Read Demonstrations? Hierarchical Structure Beats Flat Action Logs

Authors: Honjar Xing, Jefferson Lin, Henry Lieberman

Abstract: Programming by Demonstration (PbD) offers a human-centered way to author procedural knowledge for LLM agents: users communicate what they want by showing rather than by writing prompts or code, making agent authoring accessible to non-programmers. The natural output of a PbD recording is a flat action log, but how this log is organized before being passed to the agent is an open design question wi… ▽ More Programming by Demonstration (PbD) offers a human-centered way to author procedural knowledge for LLM agents: users communicate what they want by showing rather than by writing prompts or code, making agent authoring accessible to non-programmers. The natural output of a PbD recording is a flat action log, but how this log is organized before being passed to the agent is an open design question with significant consequences for plan quality. We propose grouping recorded actions into labeled, hierarchical subgoals and evaluate the effect of this organizational structure in a controlled experiment. Across 85 web automation tasks, we compare a zero-shot baseline against four demonstration formats that share identical action sequences but differ in structure. On 43 natural-language tasks with vague descriptions, hierarchically grouped demonstrations improve pass rates from 76.7\% to 90.7\% (paired permutation test $p{=}0.034$; win-loss 6:0), while flat demonstrations show a smaller, non-significant improvement. On 42 tasks with precise descriptions, no format provides any benefit, confirming that the hierarchical advantage arises specifically when descriptions leave procedural details ambiguous. Ablation shows that subgoal grouping alone drives the effect: preconditions, postconditions, and parameter annotations add no measurable benefit. These results offer a concrete design recommendation for PbD pipelines and, more broadly, for any system that feeds procedural context to an LLM agent: segment action sequences into named subgoal groups rather than presenting flat step lists. △ Less

Submitted 18 June, 2026; originally announced June 2026.

Comments: Accepted at the 5th Deep Learning for Code (DL4C) Workshop, ICML 2026. 8 pages, 2 figures, 4 tables

Hadronic tensor in lattice gauge theories by quantum computing

Authors: Dairui Zou, Tianyin Li, Jian Liang, Enke Wang, Hongxi Xing

Abstract: The hadronic tensor encodes crucial information regarding the internal structure of hadrons, reflecting the non-perturbative features of quantum chromodynamics (QCD). In this work, we directly compute the hadronic tensor within (1+1)-dimensional $\rm U(1)$ and $\rm SU(2)$ gauge theories by evaluating real-time current-current correlation functions. Utilizing quantum algorithms executed on classica… ▽ More The hadronic tensor encodes crucial information regarding the internal structure of hadrons, reflecting the non-perturbative features of quantum chromodynamics (QCD). In this work, we directly compute the hadronic tensor within (1+1)-dimensional $\rm U(1)$ and $\rm SU(2)$ gauge theories by evaluating real-time current-current correlation functions. Utilizing quantum algorithms executed on classical hardware, we demonstrate that the hadron form factors for both meson and baryon states can be reliably extracted from the hadronic tensor. Our methodology is validated by strong agreement with both direct calculation and exact diagonalization of the form factors. △ Less

Submitted 15 June, 2026; originally announced June 2026.

Comments: 9 pages, 7 figures

Report number: RIKEN-iTHEMS-Report-26

Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance

Authors: Honjar Xing, Yehong Jiang, Xianbang Wang, Zehua Wang, Zhicheng Jiang

Abstract: Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approximation parameters -- such as bond dimension thresholds -- remains a costly trial-and-error process. We present a family-aware neural architecture that predicts both the minimum approximation threshold required to achieve target fidelity and the expec… ▽ More Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approximation parameters -- such as bond dimension thresholds -- remains a costly trial-and-error process. We present a family-aware neural architecture that predicts both the minimum approximation threshold required to achieve target fidelity and the expected wall-clock runtime for quantum circuit simulation, given only the circuit's OpenQASM description and execution context. Our key insight is that quantum circuits from different algorithmic families (e.g., QFT, Grover, VQE) exhibit fundamentally distinct simulation cost profiles due to their differing entanglement structures. We employ family-conditioned residual corrections -- additive, family-specific adjustments atop a shared backbone, drawing on established conditional computation techniques -- enabling the model to capture both universal circuit properties and algorithmic nuances. The architecture incorporates a pretrained family classifier (97.5% accuracy) and domain-informed algorithm fingerprint features derived from gate-composition heuristics. Evaluated on circuits spanning 7--130 qubits across 10 algorithm families, our system achieves 79.5% exact threshold accuracy (91.2% within one rung) and $R^2 = 0.82$ runtime correlation, with inference completing in approximately 50 ms -- replacing trial-and-error simulation runs that may take minutes to hours. Ablation studies confirm that family-aware modeling provides the single largest performance improvement (+3.2 percentage points), validating the hypothesis that algorithm family is a first-class feature for simulation cost prediction. △ Less

Submitted 9 June, 2026; originally announced June 2026.

Comments: Accepted as a full paper at IEEE ISVLSI 2026 (QC-CSAA Workshop). To appear in IEEE Xplore. 6 pages, 1 figure, 2 tables

ACM Class: I.2.6; I.6.3; J.2

Low-regularity well-posedness for a mixed-sign quadratic Dirac equation on $N$-star metric graphs

Authors: Huichao Xing, Zhipeng Yang

Abstract: We study the Cauchy problem for a mixed-sign quadratic Dirac equation on a noncompact $N$-star metric graph $G$, \[ \mathrm{i}\partial_t ψ= Dψ- \mathcal N(ψ), \qquad ψ(0)=ψ_0, \] where $ψ=(ψ_1,ψ_2)^{\mathsf T}:\mathbb{R}\times G\to\mathbb{C}^2$ and $D$ denotes the self-adjoint Dirac-Kirchhoff operator on $G$. The nonlinearity acts edgewise and is given by a bilinear interaction between the positiv… ▽ More We study the Cauchy problem for a mixed-sign quadratic Dirac equation on a noncompact $N$-star metric graph $G$, \[ \mathrm{i}\partial_t ψ= Dψ- \mathcal N(ψ), \qquad ψ(0)=ψ_0, \] where $ψ=(ψ_1,ψ_2)^{\mathsf T}:\mathbb{R}\times G\to\mathbb{C}^2$ and $D$ denotes the self-adjoint Dirac-Kirchhoff operator on $G$. The nonlinearity acts edgewise and is given by a bilinear interaction between the positive and negative spectral parts, \[ \mathcal N(ψ)=\mathcal B\bigl(Π_+ψ,Π_-ψ\bigr), \] where $Π_\pm$ are the spectral projections of $D$ and $\mathcal B$ is a fixed bilinear map on $\mathbb{C}^2$ applied componentwise on each edge. This is a model quadratic interaction tailored to the mixed-sign Bourgain-space mechanism, rather than a general nonlinear Dirac equation on graphs. Using Bourgain-type spaces associated with the spectral resolution of $D$ and a mixed-sign bilinear estimate on $N$-star graphs, we prove local well-posedness in the operator Sobolev space $H_D^s(G)$ for \(s>-\frac18\). We also establish a blow-up alternative in $H_D^s(G)$ for the maximal forward lifespan. △ Less

Submitted 8 June, 2026; originally announced June 2026.

Comments: 19 pages, comments are welcome

MSC Class: 35Q41; 35A01; 81Q35

Journal ref: Z. Angew. Math. Phys. 77 (2026), no. 162, 1-19

Hyperon-Nucleon Spectrometer

Authors: Xiaozhi Bai, Xu Cao, Zhe Cao, Jinhui Chen, Kai Chen, Qibo Chen, Shi Chen, Xin Chen, Yuquan Chen, Zhenyu Chen, Jianping Dai, Heng-Tong Ding, Dongshuo Du, Shuxian Du, Limin Duan, Zhe Duan, Anhui Feng, Jie Feng, Yicheng Feng, Jinlin Fu, Xiaofeng Fu, Chaosong Gao, Liang Ge, Wenwen Ge, Lisheng Geng , et al. (215 additional authors not shown)

Abstract: Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $Λ$ polarization puzzle, in which $Λ$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse pola… ▽ More Chirality lies at the heart of low-energy QCD, governing the symmetry structure that shapes hadron masses and strong interaction dynamics. Among the most compelling open questions tied to chiral dynamics and spontaneous chiral symmetry breaking is the longstanding $Λ$ polarization puzzle, in which $Λ$ hyperons produced in unpolarized hadronic collisions exhibit a surprisingly large transverse polarization that remains theoretically unexplained. This whitepaper presents the proposal for the Hyperon-Nucleon Spectrometer (H-NS) at the High-Intensity heavy-ion Accelerator Facility (HIAF). Leveraging the high energy and high intensity of HIAF's proton and heavy-ion beams, the H-NS experiment will perform systematic studies of hyperon polarization phenomena and their underlying mechanisms in proton-proton ($pp$), proton-nucleus ($pA$), and nucleus-nucleus ($AA$) collisions in the fixed target mode. A wide-range beam energy scan, including proton beams from 3 GeV up to 9.3 GeV (HIAF) and up to 32 GeV (upgraded HIAF), will be conducted to examine the dependence of polarization on collision energy. The spectrometer is designed with specialized detectors capable of high-precision reconstruction of final-state baryon polarizations. Among its many interesting and important measurements, H-NS will simultaneously measure hyperon and proton spin observables to explore the polarization mechanism in hadronic interactions and the spin structure of baryons. Furthermore, the use of $pA$ and $AA$ collisions will enable detailed investigations of cold and hot nuclear matter effects on spin polarization. Its physics program and detector development will significantly benefit the future Electron-ion Collider in China. △ Less

Submitted 4 June, 2026; originally announced June 2026.

Comments: 69 pages, Hyperon-Nucleon Spectrometer (H-NS) white paper

Agents' Last Exam

Authors: Yiyou Sun, Xinyang Han, Weichen Zhang, Yuanbo Pang, Tianyu Wang, Yuhan Cao, Yixiao Huang, Chris Duroiu, Haoyun Zhang, Jeffrey Lin, Weishu Zhang, Tyler Zeng, Ying Yan, Bo Liu, Hanson Wen, Mingyang Xu, Xiaoyuan Liu, Zimeng Chen, Weiyan Shi, Amanda Dsouza, Vincent Sunn Chen, Patrick Bryant, Carl Boettiger, Yamini Rangan, Bradley Rothenberg , et al. (285 additional authors not shown)

Abstract: Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a… ▽ More Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional domains. We argue that this gap is largely an evaluation problem: widely used benchmarks lack sustained performance measurement on real and economically valuable workflows. This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long horizon, economically valuable, real world tasks with verifiable outcomes. Developed in collaboration with 250+ industry experts, ALE covers non-physical industries defined with reference to O*NET / SOC 2018 (the U.S. federal occupational taxonomy). It is organized around a task taxonomy with 55 sub fields grouped into 13 industry clusters covering 1K+ tasks. Current results show that the hardest tier remains far from saturated: across mainstream harness and backbone configurations, the average full pass rate is below 1%. ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded. More broadly, ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP relevant impact. △ Less

Submitted 11 June, 2026; v1 submitted 3 June, 2026; originally announced June 2026.

Comments: Project website: https://agents-last-exam.org Code: https://github.com/rdi-berkeley/agents-last-exam

MPDocBench-Parse: Benchmarking Practical Multi-page Document Parsing

Authors: Bangbang Zhou, Hangdi Xing, Yifan Chen, Jianjun Xu, Qi Zheng, Feiyu Gao, Zhibo Yang, Shuai Bai, Ming Yan, Jieping Ye, Hongtao Xie

Abstract: Document parsing converts visually rich documents into machine-readable structured representations, forming a crucial foundation for information systems. Although many benchmarks have been proposed for document parsing, they remain inadequate for realistic scenarios. Existing benchmarks either focus on specific tasks or assess only single-page, text-centric settings, making them insufficient for p… ▽ More Document parsing converts visually rich documents into machine-readable structured representations, forming a crucial foundation for information systems. Although many benchmarks have been proposed for document parsing, they remain inadequate for realistic scenarios. Existing benchmarks either focus on specific tasks or assess only single-page, text-centric settings, making them insufficient for practical multi-page parsing. Moreover, they lack fine-grained evaluation of semantic continuity, hierarchical structure recovery, and visual content preservation. To address these gaps, we propose MPDocBench-Parse, a benchmark for multi-page document parsing in real-world applications. It contains 433 manually annotated documents with 3,246 pages, covering 15 document types in English and Chinese, with diverse layout styles, and supports document-level end-to-end evaluation. We further design a comprehensive protocol for content fidelity and logical structure, covering text, table, and formula recognition, truncated text and table merging, figure extraction, reading order, and heading hierarchy recovery. Experiments show that, while existing models perform well on basic text extraction, they still suffer clear limitations in semantic continuity integration, visual content parsing, and hierarchical structure recovery. MPDocBench-Parse provides a unified foundation for advancing document parsing toward more realistic scenarios. △ Less

Submitted 28 May, 2026; v1 submitted 21 May, 2026; originally announced May 2026.

LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation

Authors: Lingyu Mu, Hao Deng, Haibo Xing, Kaican Lin, Zhitong Zhu, Yu Zhang, Xiaoyi Zeng, Zhengxiao Liu, Zheng Lin, Jinxin Hu

Abstract: Recent progress in large language model (LLM) based generative recommendation (GR) shows that leveraging LLM world knowledge can substantially improve performance. However, existing methods rely on fixed, manually designed instructions to generate semantic knowledge and directly incorporate it into GR, which has two limitations. First, fixed instructions cannot capture the multidimensional heterog… ▽ More Recent progress in large language model (LLM) based generative recommendation (GR) shows that leveraging LLM world knowledge can substantially improve performance. However, existing methods rely on fixed, manually designed instructions to generate semantic knowledge and directly incorporate it into GR, which has two limitations. First, fixed instructions cannot capture the multidimensional heterogeneity of user interests. Second, uncontrollable knowledge fusion may conflict with behavioral signals and harm recommendations. To address these limitations, we propose LWGR, a framework that leverages Lagrangian constraints to transfer users' personalized world knowledge from LLMs into generative recommendation. LWGR enhances GR along two axes: knowledge extraction and fusion. It builds personalized soft instructions to extract behavior-relevant LLM world knowledge, and formulates knowledge fusion as an optimization problem with explicitly bounded performance degradation, which is solved by a Lagrangian primal-dual method to selectively incorporate beneficial knowledge. We further design two training strategies for different LLM scales and a deployment scheme that combines nearline precomputation with lightweight online serving. Experiments on multiple public datasets and one industrial dataset show that LWGR outperforms eight state-of-the-art baselines by up to 11.23% and brings a 1.35% revenue lift on a large-scale advertising platform, demonstrating its effectiveness and practicality. △ Less

Submitted 16 April, 2026; originally announced May 2026.

ContractBench: Can LLM Agents Preserve Observation Contracts?

Authors: Jicheng Wang, Yifeng He, Zili Wang, Hanwen Xing, Arkaprava De, Hao Chen

Abstract: Tool-augmented LLM agents call APIs whose intermediate outputs, such as presigned URLs, session tokens, and OAuth state parameters, are observation contracts: artifacts whose later use is constrained by the external system that produced them. We show that observation contract compliance (preserving the temporal validity and byte-level integrity) is an emergent, regression-prone capability: it is n… ▽ More Tool-augmented LLM agents call APIs whose intermediate outputs, such as presigned URLs, session tokens, and OAuth state parameters, are observation contracts: artifacts whose later use is constrained by the external system that produced them. We show that observation contract compliance (preserving the temporal validity and byte-level integrity) is an emergent, regression-prone capability: it is neither guaranteed by general tool-use ability nor consistently improved by larger or newer models. To measure this, we introduce ContractBench, a benchmark of 33 dual-axis tasks that probe two orthogonal failure modes no existing benchmark evaluates: validity failures (using an artifact after expiry) and integrity failures (corrupting an artifact's bytes through the observation-to-action pipeline). Our evaluation is deterministic and programmatic, with a virtual clock controlling time and SHA-256 hashes verifying byte integrity. We assign each outcome a failure label drawn from real-world API specifications. We evaluate 38 models and report four findings: (i) no evaluated model clears 80%, with Claude-Opus-4.6 leading at 77.8%, revealing that current frontier models still fail to comply with observation contracts; (ii) a sharp within-family capability cliff in Qwen 3.5 between 4B (0%) and 9B (56.6%), smoothing to 70.7% at 397B-A17B: what emerges across the cliff is mid-trajectory restraint, not tool-call competence; (iii) non-monotonic scaling across the GPT-5 family: agentic post-training can erode compliance through sycophancy-driven regression; (iv) our failure taxonomy works as an actionable in-context reward signal, yielding +7.1 pp on 42 paired GPT-5.1 failures. △ Less

Submitted 17 May, 2026; originally announced May 2026.

ACM Class: I.2.7; I.2.6

AgentTrap: Measuring Runtime Trust Failures in Third-Party Agent Skills

Authors: Haomin Zhuang, Hanwen Xing, Yujun Zhou, Yuchen Ma, Yue Huang, Yili Shen, Yufei Han, Xiangliang Zhang

Abstract: Third-party skills are becoming the package ecosystem for LLM agents. They package natural-language instructions, helper scripts, templates, documents, and service configuration into reusable workflows. This makes skills useful, but it also introduces a new security problem: a malicious skill does not need to ask the model to perform an obviously harmful action. Instead, it can disguise the harmfu… ▽ More Third-party skills are becoming the package ecosystem for LLM agents. They package natural-language instructions, helper scripts, templates, documents, and service configuration into reusable workflows. This makes skills useful, but it also introduces a new security problem: a malicious skill does not need to ask the model to perform an obviously harmful action. Instead, it can disguise the harmful behavior as part of a routine workflow, relying on the agent to execute that workflow with high-value permissions and limited human supervision. We introduce AgentTrap, a dynamic benchmark for evaluating whether LLM agents can use third-party skills while resisting malicious runtime behavior. AgentTrap contains 141 tasks: 91 malicious tasks and 50 benign utility tasks, covering 16 security-impact dimensions grounded in agent-skill supply-chain threats. In each task, the agent receives an ordinary user request, runs with installed skills that may contain malicious workflow elements, and is executed in a sandboxed environment. AgentTrap then judges complete trajectories for attack success, blocked or refused behavior, attack-not-triggered cases, and no-attack-evidence outcomes. Our central finding is that the most informative failures are not simple jailbreaks. Models often complete the visible user task while treating unsafe side effects introduced by the skill as part of the normal workflow. This motivates runtime evaluation of the concrete model--framework--workspace environment in which users actually delegate work. Code and data are available at https://github.com/zhmzm/AgentTrap and https://huggingface.co/datasets/zhmzm/AgentTrap. △ Less

Submitted 13 May, 2026; originally announced May 2026.

Do Skill Descriptions Tell the Truth? Detecting Undisclosed Security Behaviors in Code-Backed LLM Skills

Authors: Wenhui He, Yue Li, Bang Fu, Huan Xing, Xing Fan, ZeHua Zhang, Baoning Niu

Abstract: Programmatic skills in LLM ecosystems consist of a natural-language description and executable implementation files. Users and LLMs rely on the description to understand the skill's scope. However, the implementation may perform security-relevant operations, such as credential access, network communication, or command execution, that the description does not state. We study this description--imple… ▽ More Programmatic skills in LLM ecosystems consist of a natural-language description and executable implementation files. Users and LLMs rely on the description to understand the skill's scope. However, the implementation may perform security-relevant operations, such as credential access, network communication, or command execution, that the description does not state. We study this description--implementation inconsistency by asking whether the implementation stays within the security-relevant scope declared in the description. We manually analyze 920 real-world programmatic skills and construct an 11-category security property taxonomy. Based on this taxonomy, we build SKILLSCOPE, which constructs source-level security property graphs (SPGs) from implementations and performs LLM-assisted consistency checking. SPG nodes retain source-level code patterns rather than abstract taxonomy labels, preserving fine-grained evidence for checking. On 4,556 programmatic skills with double-blind human review, SKILLSCOPE achieves a precision of 84.8\% and a recall of 96.5\% for identifying inconsistency. Confirmed inconsistency affects 9.4\% of skills, while cases of coarser description, in which implementation details remain within the declared scope, account for 24.3\%. Ablation experiments confirm that both the SPG and the taxonomy contribute: removing the taxonomy reduces precision from 87.8\% to 72.3\%, while removing the SPG reduces recall from 94.7\% to 79.0\%. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Comments: 11 pages, 3 figures, 9 tables

Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification

Authors: Chenxu Wang, Shuang Wang, Lirong Han, Xinyu Hu, Hanlin Mo, Hantong Xing, Licheng Jiao

Abstract: Deep learning-based AMC methods have achieved remarkable performance, but their practical deployment remains constrained by the high cost of labeled data. Although self-supervised learning (SSL) reduces the reliance on labels, existing SSL-based AMC methods often rely on task-agnostic pretext objectives misaligned with modulation classification, leading to representations entangled with nuisance f… ▽ More Deep learning-based AMC methods have achieved remarkable performance, but their practical deployment remains constrained by the high cost of labeled data. Although self-supervised learning (SSL) reduces the reliance on labels, existing SSL-based AMC methods often rely on task-agnostic pretext objectives misaligned with modulation classification, leading to representations entangled with nuisance factors such as symbol, channel, and noise. In this paper, we identify intra-instance modulation consistency as a task-aware structural prior, whereby different temporal segments of the same signal may differ in waveform while preserving the same modulation type, thus providing a principled cue for task-aligned self-supervision. Based on this prior, we propose Mod-CL, a Modulation consistency-based Contrastive Learning framework that constructs positive pairs from different temporal segments of the same signal instance, to encourage the model to learn shared modulation information while suppressing nuisance variations. We further develop a contrastive objective tailored to Mod-CL, which jointly exploits temporal segmentation and data augmentation to pull together views sharing the same modulation semantics while avoiding supervisory conflicts within each signal instance. Extensive experiments on RadioML datasets show that Mod-CL consistently outperforms strong baselines, especially in low-label regimes, achieving substantial improvements in linear probing accuracy. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Martingale-Consistent Self-Supervised Learning

Authors: Moritz Gögl, Hanwen Xing, Christopher Yau

Abstract: Self-supervised learning (SSL) is often deployed under changing information, such as shorter histories, missing features, or partially observed images. In these settings, predictions from coarse and refined views should be coherent: before refinement, the coarse-view prediction should match the average prediction expected after refinement. Martingales formalize this coherence principle, but standa… ▽ More Self-supervised learning (SSL) is often deployed under changing information, such as shorter histories, missing features, or partially observed images. In these settings, predictions from coarse and refined views should be coherent: before refinement, the coarse-view prediction should match the average prediction expected after refinement. Martingales formalize this coherence principle, but standard SSL objectives do not enforce it. Unlike invariance objectives that pull views together, martingale consistency constrains only the expected refined prediction, allowing predictions to update as information is revealed while preventing systematic drift. We introduce a martingale-consistent SSL framework that closes this gap, with practical prediction- and latent-space variants and an unbiased two-sample Monte Carlo estimator based on stochastic refinement. We evaluate the approach on synthetic and real time-series, tabular, and image benchmarks under partial-observation regimes, in both semi-self-supervised and fully label-free settings. Across these experiments, our framework improves robustness and calibration under partial observation, yielding more stable representations as information is revealed. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Sparsity-Aware Event-Driven Impulse Radio Transceivers for Reliable Neuromorphic Inference

Authors: Zhengzhong Guan, Jiaying Li, Kanghua Li, Bojun Cheng, Hong Xing

Abstract: The growing number of Internet-of-Things (IoT) based artificial intelligence (AI) applications deployed at resource-constrained network edge call for ultra-reliable and low-latency data processing pipelines from distributed front-end sensors to remote inference units. Meanwhile, brain-inspired neuromorphic computing featuring spiking neural networks (SNNs) have arisen as a new paradigm for energy-… ▽ More The growing number of Internet-of-Things (IoT) based artificial intelligence (AI) applications deployed at resource-constrained network edge call for ultra-reliable and low-latency data processing pipelines from distributed front-end sensors to remote inference units. Meanwhile, brain-inspired neuromorphic computing featuring spiking neural networks (SNNs) have arisen as a new paradigm for energy-efficient AI inference. However, significant energy and time expenses incurred in high-complexity transceivers that combat fading and multi-user interference hinder implementations of multi-user neuromorphic inference for edge intelligence. To address this challenge, we consider in this paper a broadband multi-user remote inference system that integrates event-based sensing and time-hopping (TH) on-off keying (OOK) based ultra-wideband (UWB) communications for reliable neuromorphic inference. Specifically, we propose a novel two-timescale repetition coding that leverages intra-frame pulse sparsity for low-latency repetition. We also develop two neuromorphic inference schemes based on: (i) digital spike encoding that recovers each pixel of the event-frame by threshold-adaptive detection via an SNN based sparsity estimator; and (ii) analog spike encoding that converts noisy correlator outputs at the receiver into analog-valued inputs for end-to-end (E2E) classification. Finally, numerical results validate the effectiveness of the proposed coding schemes, and reveal a signal-to-noise ratio (SNR)-dependent performance crossover between the two inference schemes, indicating that analog spike encoding based schemes are preferable with mild or high SNR while digital spike encoding based schemes remain robust in low SNR regime. △ Less

Submitted 26 April, 2026; originally announced April 2026.

Ultra-high-energy $γ$-ray imprints from PeV particles accelerated by supernova remnants

Authors: Zhen Cao, F. Aharonian, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, W. Bian, J. Blunier, A. V. Bukevich, C. M. Cai, Y. Y. Cai, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, E. S. Chen, G. H. Chen, H. K. Chen, L. F. Chen, Liang Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen , et al. (303 additional authors not shown)

Abstract: The quest for the origin of cosmic ray (CRs) is a fundamental issue in astrophysics. Shocks of supernova remnants (SNRs) have been considered as the dominant contributors to Galactic CRs below the spectral knee near $\sim 3$ petaelectronvolt (PeV). Whether SNRs are efficient accelerators of particles beyond PeV energies has long been debated. Here we report observations of very-high-energy $γ$-ray… ▽ More The quest for the origin of cosmic ray (CRs) is a fundamental issue in astrophysics. Shocks of supernova remnants (SNRs) have been considered as the dominant contributors to Galactic CRs below the spectral knee near $\sim 3$ petaelectronvolt (PeV). Whether SNRs are efficient accelerators of particles beyond PeV energies has long been debated. Here we report observations of very-high-energy $γ$-ray emission up to hundreds of TeV from two middle age shell-type SNRs, G150.3$+$4.5 and $γ$-Cygni, with the Large High Altitude Air Shower Observatory (LHAASO). Two (or three) distinct morphological/spectral components with convex spectral shapes are observed in both sources, with the low-energy one being more extended than the high-energy one. %Although it is possible that these high-energy components may be driven by powerful pulsars, The likely association of the high-energy component with molecular clouds at similar distances, and the weakness/absence of pulsar wind nebulae (PWNe) inside these SNRs clearly indicate for the first time that the highest energy emission is produced by collision of hadronic CRs up to PeV energies with the clouds. These results are compatible with the classic model prediction that PeV particles accelerated near the end of the free expansion phase of SNR evolution can illuminate nearby molecular clouds (MCs) to produce strong $γ$-ray emission. △ Less

Submitted 24 April, 2026; originally announced April 2026.

Comments: 31 pages, 8 figures, 3 Tables

StyleVAR: Controllable Image Style Transfer via Visual Autoregressive Modeling

Authors: Liqi Jing, Dingming Zhang, Peinian Li, Lichen Zhu, Yang Xu, Hanyu Xing

Abstract: We build on the Visual Autoregressive Modeling (VAR) framework and formulate style transfer as conditional discrete sequence modeling in a learned latent space. Images are decomposed into multi-scale representations and tokenized into discrete codes by a VQ-VAE; a transformer then autoregressively models the distribution of target tokens conditioned on style and content tokens. To inject style and… ▽ More We build on the Visual Autoregressive Modeling (VAR) framework and formulate style transfer as conditional discrete sequence modeling in a learned latent space. Images are decomposed into multi-scale representations and tokenized into discrete codes by a VQ-VAE; a transformer then autoregressively models the distribution of target tokens conditioned on style and content tokens. To inject style and content information, we introduce a blended cross-attention mechanism in which the evolving target representation attends to its own history, while style and content features act as queries that decide which aspects of this history to emphasize. A scale-dependent blending coefficient controls the relative influence of style and content at each stage, encouraging the synthesized representation to align with both the content structure and the style texture without breaking the autoregressive continuity of VAR. We train StyleVAR in two stages from a pretrained VAR checkpoint: supervised fine-tuning on a large triplet dataset of content--style--target images, followed by reinforcement fine-tuning with Group Relative Policy Optimization (GRPO) against a DreamSim-based perceptual reward, with per-action normalization weighting to rebalance credit across VAR's multi-scale hierarchy. Across three benchmarks spanning in-, near-, and out-of-distribution regimes, StyleVAR consistently outperforms an AdaIN baseline on Style Loss, Content Loss, LPIPS, SSIM, DreamSim, and CLIP similarity, and the GRPO stage yields further gains over the SFT checkpoint, most notably on the reward-aligned perceptual metrics. Qualitatively, the method transfers texture while maintaining semantic structure, especially for landscapes and architectural scenes, while a generalization gap on internet images and difficulty with human faces highlight the need for better content diversity and stronger structural priors. △ Less

Submitted 11 May, 2026; v1 submitted 22 April, 2026; originally announced April 2026.

Movable-Antenna Enabled Robust Vehicular Consumer Networks Under Imperfect CSI

Authors: Xuhui Zhang, Chunjie Wang, Wenchao Liu, Huijun Xing, Jinke Ren, Zheng Xing, Yanyan Shen

Abstract: The accelerating advancement of intelligent transportation systems has established consumer-oriented vehicular networks (CVNs) as a critical infrastructure for next-generation connected mobility. However, the high mobility of vehicular users (VUs) introduces significant channel state information (CSI) uncertainty, which severely undermines the performance of conventional fixed-position antenna sys… ▽ More The accelerating advancement of intelligent transportation systems has established consumer-oriented vehicular networks (CVNs) as a critical infrastructure for next-generation connected mobility. However, the high mobility of vehicular users (VUs) introduces significant channel state information (CSI) uncertainty, which severely undermines the performance of conventional fixed-position antenna systems. To address this, this paper explores the deployment of movable-antennas (MAs) to enhance communication robustness in CVNs under imperfect CSI conditions. We develop a joint optimization framework that dynamically coordinates the spatial positioning of MAs and transmit beamforming at the base station, with the objective of maximizing the worst-case sum rate across all VUs. The problem is formulated as a non-convex max-min optimization problem, subject to bounded CSI estimation errors, transmit power limits, and physical constraints on antenna displacement. By adopting an alternating optimization strategy, the original problem is decomposed into tractable subproblems, solved via techniques including the S-Procedure, Schur complement, and successive convex approximation. Numerical evaluations confirm that the proposed approach achieves substantial gains over existing benchmarks in terms of worst-case throughput. △ Less

Submitted 20 April, 2026; originally announced April 2026.

Comments: This manuscript is accepted by IEEE

AgentClick: A Skill-Based Human-in-the-Loop Review Layer for Terminal AI Agents

Authors: Haomin Zhuang, Hanwen Xing, Xiangliang Zhang

Abstract: Recent autonomous AI agents such as Codex, and Claude Code have made it increasingly practical for users to delegate complex tasks, including writing emails, executing code, issuing shell commands, and carrying out multi-step plans. However, despite these capabilities, human-agent interaction still largely happens through terminal interfaces or remote text-based channels such as Discord. These int… ▽ More Recent autonomous AI agents such as Codex, and Claude Code have made it increasingly practical for users to delegate complex tasks, including writing emails, executing code, issuing shell commands, and carrying out multi-step plans. However, despite these capabilities, human-agent interaction still largely happens through terminal interfaces or remote text-based channels such as Discord. These interaction modes are often inefficient and unfriendly: long text outputs are difficult to read and review, proposed actions lack clear structure and visual context, and users must express feedback by typing detailed corrections, which is cumbersome and often discourages effective collaboration. As a result, non-expert users in particular face a high barrier to working productively with agents. To address this gap, we present AgentClick, an interactive review layer for terminal-based agents. AgentClick is implemented as a localhost npm server paired with a skill-based plugin that connects the running agent to a browser interface, allowing users to supervise and collaborate with agents through a structured web UI rather than raw terminal text alone. The system supports a range of human-in-the-loop workflows, including email drafting and revision, plan review and modification, memory management, trajectory inspection and visualization, and error localization during agent execution. It also turns code generation and execution into a reviewable process, enabling users to inspect and intervene before consequential actions are taken. In addition, AgentClick supports persistent preference capture through editable memory and remote access over HTTP, allowing users to review agents running on servers from their personal devices. Our goal is to lower the barrier for non-expert users and improve the efficiency and quality of human-agent co-work. △ Less

Submitted 15 April, 2026; originally announced April 2026.

Comments: Accepted to ACM CAIS 2026 System Demonstrations. Conference paper

Cryogenic Loss Limits in Microwave Epitaxial AlN Acoustic Resonators

Authors: Hemant Gulupalli, Navnil Choudhury, Jiacheng Xie, Yufeng Wu, Huili Grace Xing, Hong X. Tang, Debdeep Jena, Kanad Basu, Wenwen Zhao

Abstract: Aluminum nitride (AlN)-based thin-film bulk acoustic wave resonators (FBARs) are promising compact platforms for 6G communications and quantum memory hardware, enabled by their integrable acoustic modes with high quality factors. However, temperature-dependent acoustic dissipation ultimately limits device performance. In this work, we fabricated a 16 GHz epitaxial AlN FBAR as a test platform, perf… ▽ More Aluminum nitride (AlN)-based thin-film bulk acoustic wave resonators (FBARs) are promising compact platforms for 6G communications and quantum memory hardware, enabled by their integrable acoustic modes with high quality factors. However, temperature-dependent acoustic dissipation ultimately limits device performance. In this work, we fabricated a 16 GHz epitaxial AlN FBAR as a test platform, performed small-signal RF measurements from 6.5 K to 300 K, and developed a physics-based model to estimate the fundamental quality-factor limits of FBARs to cryogenic temperatures. The proposed model incorporates both intrinsic and extrinsic loss mechanisms, including an analytical anchor-radiation loss model for bulk acoustic wave resonators, rather than relying solely on finite-element simulations. Measured loaded quality factor (Q) decreases monotonically with temperature, from Qmax of approximately 1589 (Qf=24.79 THz) at 6.5 K to 363 at 294K (Qf=5.66 THz). This trend is consistent with the theoretical limit based on the resonator geometry and the chosen Metal-Insulator-Metal (MIM) stack. To demonstrate the generality of the physics-based framework, we further validate it by benchmarking against a 23 GHz high-overtone bulk acoustic resonator (HBAR) using previously reported data. The validated model provides a practical, transferable framework to interpret Q(T) limits in low-loss resonators by quantifying the temperature-dependent mechanisms that constrain Q, enabling the design of cryogenic microwave filter elements for superconducting quantum hardware. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Comments: 10 pages, 4 figures

Tomonaga-Luttinger liquid and charge-density wave in a quasi-one-dimensional material

Authors: Jing Li, Guo-Wei Yang, Bai-Zhuo Li, Yi Liu, Si-Qi Wu, Ji-Yong Liu, Jin-Ke Bao, Xiaoxian Yan, Hua-Xun Li, Jia-Xin Li, Jia-Lu Wang, Yun-Lei Sun, Yi-Ming Lu, Jia-Yi Lu, Yi-Qiang Lin, Hui Xing, Chao Cao, Hao Jiang, Yang Liu, Guang-Han Cao, Hai-Qing Lin

Abstract: In one-dimensional (1D) electron systems, the Fermi liquid state breaks down due either to electron interactions, which results in a Tomonaga-Luttinger liquid (TLL) state, or to Peierls instability, which leads to an insulating charge-density-wave (CDW) phase. In general, these two phenomena are mutually exclusive, and their coexistence remains elusive in real materials. Here, we report the discov… ▽ More In one-dimensional (1D) electron systems, the Fermi liquid state breaks down due either to electron interactions, which results in a Tomonaga-Luttinger liquid (TLL) state, or to Peierls instability, which leads to an insulating charge-density-wave (CDW) phase. In general, these two phenomena are mutually exclusive, and their coexistence remains elusive in real materials. Here, we report the discovery of a new quasi-1D material, Cs$_{1-δ}$Cr$_3$S$_3$, which unexpectedly exhibits coexistence of the antithetical CDW and TLL states. The CDW state is evidenced by the intra-unit-cell dimerization, and the opening of an optical band gap of $\sim$250 meV. Meanwhile, TLL behaviour is unambiguously demonstrated by the measurements of electrical transport and angle-resolved photoemission spectroscopy, which reveal a power-law scaling with temperature, bias voltage and electron energy. Band structure calculations reveal isolated, linearly dispersive, 1D bands around the Fermi level. For the dimerized CDW phase, the 1D Fermi-surface sheets located at the boundary of the Brillouin zone are gapped from intra-unit-cell bond symmetry breaking. Experimentally, subtle Cs vacancies shift the Fermi level into the linearly dispersive valence band, enabling the observation of TLL behaviour without interrupting the CDW order. This work establishes Cs$_{1-δ}$Cr$_3$S$_3$ as a rare material platform in which the antagonistic Fermi-liquid instabilities coexist and intertwine, opening new avenues for studying emergent quantum phenomena in 1D systems. △ Less

Submitted 30 March, 2026; originally announced March 2026.

Comments: 32 pages, 13 figures, 5 tables

RCLRec: Reverse Curriculum Learning for Modeling Sparse Conversions in Generative Recommendation

Authors: Yulei Huang, Hao Deng, Haibo Xing, Jinxin Hu, Chuanfei Xu, Zulong Chen, Yu Zhang, Xiaoyi Zeng

Abstract: Conversion objectives in large-scale recommender systems are sparse, making them difficult to optimize. Generative recommendation (GR) partially alleviates data sparsity by organizing multi-type behaviors into a unified token sequence with shared representations, but conversion signals remain insufficiently modeled. While recent behavior-aware GR models encode behavior types and employ behavior-aw… ▽ More Conversion objectives in large-scale recommender systems are sparse, making them difficult to optimize. Generative recommendation (GR) partially alleviates data sparsity by organizing multi-type behaviors into a unified token sequence with shared representations, but conversion signals remain insufficiently modeled. While recent behavior-aware GR models encode behavior types and employ behavior-aware attention to highlight decision-related intermediate behaviors, they still rely on standard attention over the full history and provide no additional supervision for conversions, leaving conversion sparsity largely unresolved. To address these challenges, we propose RCLRec, a reverse curriculum learning-based GR framework for sparse conversion supervision. For each conversion target, RCLRec constructs a short curriculum by selecting a subsequence of conversion-related items from the history in reverse. Their semantic tokens are fed to the decoder as a prefix, together with the target conversion tokens, under a joint generation objective. This design provides additional instance-specific intermediate supervision, alleviating conversion sparsity and focusing the model on the user's critical decision process. We further introduce a curriculum quality-aware loss to ensure that the selected curricula are informative for conversion prediction. Experiments on offline datasets and an online A/B test show that RCLRec achieves superior performance, with +2.09% advertising revenue and +1.86% orders in online deployment. △ Less

Submitted 30 March, 2026; originally announced March 2026.

Pion and Kaon Fragmentation Functions from Continuum Schwinger Function Methods

Authors: Hui-Yu Xing

Abstract: Using the Drell-Levy-Yan relation, the pion and kaon elementary fragmentation functions (EFFs) are obtained from their hadron-scale parton distribution functions (DFs). These EFFs serve as driving terms in the hadron cascade equations, whose solution yields the complete array of hadron-scale fragmentation functions (FFs) for pion and kaon production in high energy reactions. Evolved to experimenta… ▽ More Using the Drell-Levy-Yan relation, the pion and kaon elementary fragmentation functions (EFFs) are obtained from their hadron-scale parton distribution functions (DFs). These EFFs serve as driving terms in the hadron cascade equations, whose solution yields the complete array of hadron-scale fragmentation functions (FFs) for pion and kaon production in high energy reactions. Evolved to experimental scales, the continuum Schwinger function methods (CSMs) predictions satisfy QCD endpoint behavior: nonsinglet FFs vanish at $z=0$, singlet FFs diverge faster than $1/z$. Jet multiplicity predictions reveal SU(3) symmetry breaking in the charged/neutral kaon ratio, decreasing with energy, and show the pion/kaon ratio in $e^+e^-$ collisions asymptotes to a mass-independent value. △ Less

Submitted 24 March, 2026; originally announced March 2026.

Flexible Information Acquisition in the Kyle Model

Authors: S. Viswanathan, Hao Xing

Abstract: We study an information acquisition problem in which an informed trader acquires costly information prior to trading in the Kyle equilibrium. The cost of information acquisition is represented by an entropy cost. Regardless of the prior distribution of the asset payoff, continuous signals are optimal. Moreover, any continuously distributed signal, together with an associated logit type posterior d… ▽ More We study an information acquisition problem in which an informed trader acquires costly information prior to trading in the Kyle equilibrium. The cost of information acquisition is represented by an entropy cost. Regardless of the prior distribution of the asset payoff, continuous signals are optimal. Moreover, any continuously distributed signal, together with an associated logit type posterior distribution of the payoff, yields the same ex-ante value for the informed trader, the same distribution of posterior expected payoff, and the same unconditional distribution of the informed trader's trading strategy. Consequently, a normally distributed signal can be adopted without loss of generality. We further show that when the information acquisition cost increases or the volatility of noise trades decreases, the variance of the posterior expected payoff declines, the profit potential from trading diminishes, meanwhile the posterior expected payoff increasingly resembles a normal distribution, and the information leakage cost from trading decreases. △ Less

Submitted 23 March, 2026; originally announced March 2026.

Comments: 60 pages, 8 figures

Data-knowledge dual-driven intelligent framework for full-chain, experiment-efficient synthesis of 2D dendrites

Authors: Wenqiang Huang, Xuhang Gu, Susu Fang, Shen'ao Xue, Huanhuan Xing, Junjie Jiang, Junying Zhang, Shen Zhou, Zheng Luo, Jin Zhang, Fangping Ouyang, Shanshan Wang

Abstract: Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and c… ▽ More Exemplified by the chemical vapor deposition growth of two-dimensional dendrites, which has potential applications in catalysis and presents a parameter-intensive, data-scarce and reaction process-complex model problem, we devise a machine intelligence-empowered framework for the full chain support of material synthesis, encompassing rapid process optimization, accurate customized synthesis, and comprehensive mechanism deciphering.First, active learning is integrated into the experimental workflow, identifying an optimal recipe for the growth of highly-branched, electrocatalytically-active ReSe2 dendrites through 60 experiments (4 iterations), which account for less than 1.3% of the numerous possible parameter combinations.Then, a prediction accuracy-guided data augmentation strategy is developed combined with a tree-based machine learning (ML) algorithm, unveiling a non-linear correlation between 5 process variables and fractal dimension (DF) of ReSe2 dendrites with only 9 experiment additions, which guides the synthesis of various user-defined DF. Finally, we construct a data-knowledge dual-driven mechanism model by integration of cross-scale characterizations, interpretable ML models, and domain knowledge in thermodynamics and kinetics, unraveling synergistic contributions of multiple process parameters to the product morphology. This work demonstrates the ML potential to transform the research paradigm and is adaptable to broader material synthesis. △ Less

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

Comments: 57 pages, 30 figures

Journal ref: Science Bulletin (2026)

Efficient Federated Conformal Prediction with Group-Conditional Guarantee

Authors: Haifeng Wen, Osvaldo Simeone, Hong Xing

Abstract: Deploying trustworthy AI systems requires principled uncertainty quantification. Conformal prediction (CP) is a widely used framework for constructing prediction sets with distribution-free coverage guarantees. In many practical settings, including healthcare, finance, and mobile sensing, the calibration data required for CP are distributed across multiple clients, each with its own local data dis… ▽ More Deploying trustworthy AI systems requires principled uncertainty quantification. Conformal prediction (CP) is a widely used framework for constructing prediction sets with distribution-free coverage guarantees. In many practical settings, including healthcare, finance, and mobile sensing, the calibration data required for CP are distributed across multiple clients, each with its own local data distribution. In this federated setting, data can often be partitioned into, potentially overlapping, groups, which may reflect client-specific strata or cross-cutting attributes such as demographic or semantic categories. We propose group-conditional federated conformal prediction (GC-FCP), a federated extension of conditional conformal calibration for a target mixture over prespecified groups. GC-FCP constructs mergeable, atom-stratified coresets from local calibration scores, enabling compact aggregation at the server when the number of active atoms is moderate. Experiments on synthetic and real-world datasets validate the performance of GC-FCP compared to centralized calibration baselines. The code of our work can be found at https://github.com/HaifengWen/GC-FCP. △ Less

Submitted 2 July, 2026; v1 submitted 14 March, 2026; originally announced March 2026.

Comments: 24 pages, 8 figures

QUSR: Quality-Aware and Uncertainty-Guided Image Super-Resolution Diffusion Model

Authors: Junjie Yin, Jiaju Li, Hanfa Xing

Abstract: Diffusion-based image super-resolution (ISR) has shown strong potential, but it still struggles in real-world scenarios where degradations are unknown and spatially non-uniform, often resulting in lost details or visual artifacts. To address this challenge, we propose a novel super-resolution diffusion model, QUSR, which integrates a Quality-Aware Prior (QAP) with an Uncertainty-Guided Noise Gener… ▽ More Diffusion-based image super-resolution (ISR) has shown strong potential, but it still struggles in real-world scenarios where degradations are unknown and spatially non-uniform, often resulting in lost details or visual artifacts. To address this challenge, we propose a novel super-resolution diffusion model, QUSR, which integrates a Quality-Aware Prior (QAP) with an Uncertainty-Guided Noise Generation (UNG) module. The UNG module adaptively adjusts the noise injection intensity, applying stronger perturbations to high-uncertainty regions (e.g., edges and textures) to reconstruct complex details, while minimizing noise in low-uncertainty regions (e.g., flat areas) to preserve original information. Concurrently, the QAP leverages an advanced Multimodal Large Language Model (MLLM) to generate reliable quality descriptions, providing an effective and interpretable quality prior for the restoration process. Experimental results confirm that QUSR can produce high-fidelity and high-realism images in real-world scenarios. The source code is available at https://github.com/oTvTog/QUSR. △ Less

Submitted 9 March, 2026; originally announced March 2026.

Comments: This paper has been accepted by ICASSP 2026

Learning to Reflect and Correct: Towards Better Decoding Trajectories for Large-Scale Generative Recommendation

Authors: Haibo Xing, Hao Deng, Lingyu Mu, Jinxin Hu, Yu Zhang, Xiaoyi Zeng, Jing Zhang

Abstract: Generative Recommendation (GR) has become a promising paradigm for large-scale recommendation systems. However, existing GR models typically perform single-pass decoding without explicit refinement, causing early deviations to accumulate and ultimately degrade recommendation quality. To tackle this problem, we propose GRC, which is, to our knowledge, the first structured reflection-correction fram… ▽ More Generative Recommendation (GR) has become a promising paradigm for large-scale recommendation systems. However, existing GR models typically perform single-pass decoding without explicit refinement, causing early deviations to accumulate and ultimately degrade recommendation quality. To tackle this problem, we propose GRC, which is, to our knowledge, the first structured reflection-correction framework for GR that extends standard decoding into a Generation-Reflection-Correction (GRC) process. Concretely, GRC introduces a supervised reflection-correction template that decomposes the decoding process into initial draft generation, multi-granular reflection, and reflection-guided correction, thereby enabling structured reflection and correction in the semantic token space. To further explore the enlarged refinement space introduced by the GRC process, we optimize the entire GRC trajectory with GRPO-based reinforcement learning, under a carefully designed reward function with token-level and trajectory-level signals. For efficient online serving, we propose an Entropy-Guided Reflection Scheduling (EGRS) strategy that dynamically allocates more correction budget to high-uncertainty decoding trajectories during beam search. Extensive experiments on real-world datasets show that GRC consistently outperforms six state-of-the-art baselines by up to 15.74%, and online A/B tests demonstrate its substantial practical value in large-scale industrial recommendation, delivering a 1.79% lift in advertising revenue with only modest latency overhead. △ Less

Submitted 26 February, 2026; originally announced February 2026.