Showing 1–50 of 393 results for author: Yuan, D

Intern-S2-Preview: Scientific Agentic Foundation Model

Authors: Lei Bai, Jiaqi Cao, Chiyu Chen, Guanzhou Chen, Kai Chen, Guangran Cheng, Erfei Cui, Xuanlang Dai, Shengyuan Ding, Shangheng Du, Yanhui Duan, Yue Fan, Youqing Fang, Quan Gan, Yuanyuan Gao, Jiaye Ge, Lixin Gu, Yuzhe Gu, Qipeng Guo, Junjun He, Xin Hong, Ming Hu, Zhouqi Hua, Haian Huang, Junhao Huang , et al. (100 additional authors not shown)

Abstract: Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sustain progress across long task horizons. We present Intern-S2-Preview, a series of scientific agentic foundation models designed to support multimodal scientific understanding, reasoning, generation, and long-horizon tas… ▽ More Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sustain progress across long task horizons. We present Intern-S2-Preview, a series of scientific agentic foundation models designed to support multimodal scientific understanding, reasoning, generation, and long-horizon tasks. The training pipeline begins with scientific multimodal pre-training over rendered scientific documents, interleaved image-text data, and diverse scientific corpora. Starting from the pretrained checkpoint, we apply a unified post-training pipeline consisting of supervised fine-tuning, scalable multi-task reinforcement learning (RL), black- and white-box agentic RL, and on-policy distillation. This pipeline is supported by practical techniques that improve rollout and training stability and efficiency, including partial rollout with off-policy correction, adaptive length regularization, online speculative decoding, robust multi-task optimization, and trace-aware experience assembly for agentic tasks. At the architecture level, Intern-S2-Preview-397B extends time series modelling from efficient long-sequence understanding to numerical forecasting, while Memory Decoder is studied as a separate memory-augmented path for rapid scientific specialization without modifying the frozen 397B backbone. Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings. The time series modules improve scientific signal understanding and forecasting on SciTS, while the separate Intern-MemDec-4B extension improves the Biology-Instructions average score from 56.92 to 60.32 without modifying the frozen 397B backbone. △ Less

Submitted 13 August, 2026; originally announced August 2026.

Comments: 35 pages, 12 figures

SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning

Authors: David D. Yuan, Tony Z. Zhao, Kaylee Burns, Chelsea Finn

Abstract: While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds. Imitation learning policies are inherently limited by hardware constraints and the speed of the operator during data collection. In addition, there are no established methods for accelerating policies learned via imitation, and the empir… ▽ More While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds. Imitation learning policies are inherently limited by hardware constraints and the speed of the operator during data collection. In addition, there are no established methods for accelerating policies learned via imitation, and the empirical relationship between execution speed and task success remains underexplored. To address these issues, we introduce SpeedTuning, a reinforcement learning framework specifically designed to enhance the speed of manipulation policies. SpeedTuning learns to predict the optimal execution speed for actions, thereby complementing a base policy without necessitating additional data collection. We provide empirical evidence that SpeedTuning achieves substantial improvements in execution speed, exceeding 2.4x speed-up, while preserving an adequate success rate compared to both the original task policy and straightforward speed-up methods such as linear interpolation at a fixed speed. We evaluate our approach across a diverse set of dynamic and precise tasks, including pouring, throwing, and picking, demonstrating its effectiveness and robustness in enhancing real-world robotic manipulation. Videos and code are available at https://daivdyuan.github.io/speed-tuning/ △ Less

Submitted 11 August, 2026; v1 submitted 10 August, 2026; originally announced August 2026.

Comments: 10 pages, 12 figures. This arXiv version includes an appendix with qualitative simulation rollouts and additional ablations. Published at ICRA 2025

Journal ref: 2025 IEEE International Conference on Robotics and Automation (ICRA), pp. 1184-1192, 2025

WA-SpecDec: World-Aware Speculative Decoding for Vision-Language-Action Models

Authors: Zikang Wen, Yuning Zhang, Dong Yuan

Abstract: Vision-language-action (VLA) policies generate robot controls autoregressively, making closed-loop latency dominated by repeated target-model forward passes. Speculative decoding reduces this cost by verifying blocks of draft action tokens in parallel, and recent VLA methods further relax token-level acceptance because small differences in action-token space often map to similar continuous control… ▽ More Vision-language-action (VLA) policies generate robot controls autoregressively, making closed-loop latency dominated by repeated target-model forward passes. Speculative decoding reduces this cost by verifying blocks of draft action tokens in parallel, and recent VLA methods further relax token-level acceptance because small differences in action-token space often map to similar continuous controls. However, this relaxation remains scene-agnostic. A fixed token-distance tolerance treats the same action-token deviation as equally safe across states, although deviations that are harmless in free space can cause collisions or grasp failures near contact. We propose WA-SpecDec, a world-aware speculative decoding framework that injects world-model-derived physical scene awareness during the VLA prefill stage, producing shared world-aware prefill states for draft proposal and target verification without changing the relaxed acceptance rule. Across three state-of-the-art relaxed acceptance schemes, WA-SpecDec preserves higher task success under looser relaxation and enables longer accepted prefixes. At comparable-success operating points, WA-SpecDec achieves a 1.5x matched-success speedup over VLA speculative decoding alone and reduces near-contact failure (NCF) by 18.6% on average relative to the corresponding speculative baselines. △ Less

Submitted 9 August, 2026; originally announced August 2026.

Comments: Preprint

Extracting the pairing gap from van Hove singularities in rf spectra of the Fermi Hubbard model

Authors: Chuping Li, Kaichao Zhang, Junru Wu, Yuxuan Wu, Dingli Yuan, Pengyi Chen, Lin Sun, Qijin Chen

Abstract: We show that van Hove singularities in rf spectra of the 3D attractive Fermi Hubbard model provide a robust route to extracting the pairing gap. Four types of singularities are classified, and their spectral positions are shown to depend solely on the pairing gap $Δ$ and chemical potential $μ$ through simple algebraic relations. Measuring two well-resolved singularities therefore determines both p… ▽ More We show that van Hove singularities in rf spectra of the 3D attractive Fermi Hubbard model provide a robust route to extracting the pairing gap. Four types of singularities are classified, and their spectral positions are shown to depend solely on the pairing gap $Δ$ and chemical potential $μ$ through simple algebraic relations. Measuring two well-resolved singularities therefore determines both parameters without requiring full spectral fitting. Numerical simulations incorporating phenomenological lifetime and scattering broadenings confirm that these features remain visible in both momentum-integrated and $k_z$-integrated spectra, and become more pronounced at stronger coupling where conventional back-bending methods lose sensitivity. At half filling, particle-hole symmetry fixes $μ$, reducing the extraction to a single singularity measurement. These results establish vHS analysis as a practical spectroscopic diagnostic for pairing in quantum-simulated 3D Fermi Hubbard systems. △ Less

Submitted 7 August, 2026; originally announced August 2026.

Comments: 9 pages, 8 figures

HorizonServe: Coordinating Request Scheduling with GPU Sharing for Omni-Model Serving

Authors: Yuning Zhang, Dong Yuan

Abstract: Omni models unify text, speech, image, and multimodal reasoning in a single serving backend, but this unified deployment exposes a new scheduling problem. Requests with different output modalities may share an initial multimodal backbone and then diverge into downstream generation stages, creating heterogeneous first-response metrics and service-level objective (SLO) targets on the same GPU. Exist… ▽ More Omni models unify text, speech, image, and multimodal reasoning in a single serving backend, but this unified deployment exposes a new scheduling problem. Requests with different output modalities may share an initial multimodal backbone and then diverge into downstream generation stages, creating heterogeneous first-response metrics and service-level objective (SLO) targets on the same GPU. Existing large language model (LLM) and multimodal serving systems mainly optimize token progress or input-side processing, and they do not jointly control temporal sharing in the shared stage and spatial sharing among co-running stages. This paper presents HorizonServe, a single-GPU omni-model serving system that coordinates request admission and GPU allocation under heterogeneous SLOs. HorizonServe profiles per-class first-response latency, protects requests with limited slack, rotates shared-stage opportunities across execution paths, and throttles the shared-stage streaming multiprocessor (SM) allocation when downstream stages are active. Across three omni-model workloads and two GPU platforms, HorizonServe improves SLO attainment by up to 4.9$\times$ in arrival-rate sweeps and 7.0$\times$ under downstream-heavy traffic, and reduces per-class first-response latency by 38.4--63.7\%. △ Less

Submitted 3 August, 2026; originally announced August 2026.

"Anomalous Solid Solution" in Ultra-High Melting Point Oxides: A New Strategy for Developing Ultra-High Temperature Thermal Protection Coatings

Authors: Yubo Wang, Hong Meng, Pengfei He, Shujun Hu, Chuan Sun, Ximing Duan, Xiaopeng Lu, Dingwang Yuan, Wangyu Hu, Xiubing Liang

Abstract: The high-temperature performance of ultra-high temperature ceramics (UHTCs) in atmospheric environment is fundamentally governed by their melting points of oxidation products. Typical high-melting-point oxides, such as ZrO2, undergo phase transformations at elevated temperatures, leading to structural instability. Although doping with rare-earth or transition-metal cations can suppress these trans… ▽ More The high-temperature performance of ultra-high temperature ceramics (UHTCs) in atmospheric environment is fundamentally governed by their melting points of oxidation products. Typical high-melting-point oxides, such as ZrO2, undergo phase transformations at elevated temperatures, leading to structural instability. Although doping with rare-earth or transition-metal cations can suppress these transformations, it often results in a reduction in melting point, thereby limiting practical service temperature. Here, ytterbia-stabilized zirconia (YbSZ) coatings are prepared via atmospheric plasma spraying, achieving a remarkable increase in the melting point of ZrO2 to approximately 2850 $^\circ\mathrm{C}$ and raising the ultimate plasma and oxyacetylene ablation temperature up to nearly 2780 $^\circ\mathrm{C}$ and 3200 $^\circ\mathrm{C}$, which is the highest temperature resistance property as reported. Notably, this performance enhancement originates from a synergistic mechanism of strengthened ionic-covalent mixed bonding and improved oxygen vacancy stability. Based on these findings, the concept of "anomalous solid solution" is firstly proposed to be used in the area of ultra-high temperature protection, which provides new insights into the compositional design of UHTC systems. △ Less

Submitted 30 July, 2026; originally announced July 2026.

mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar

Authors: Jianyi Zhou, Chenghao Zhang, Yanli Li, Dong Yuan

Abstract: Indoor mmWave radar perception is difficult to reproduce because measured range-angle responses depend on scene geometry, material response, multipath, hardware conventions, and signal processing. Existing ray-tracing and digital-twin tools often expose rendering, channel, or path-level quantities, while radar sensing requires complex signal products that can be processed and compared in the same… ▽ More Indoor mmWave radar perception is difficult to reproduce because measured range-angle responses depend on scene geometry, material response, multipath, hardware conventions, and signal processing. Existing ray-tracing and digital-twin tools often expose rendering, channel, or path-level quantities, while radar sensing requires complex signal products that can be processed and compared in the same domain as real FMCW measurements. We present mmRadarTwin, a signal-level and path-attributed digital-twin platform for indoor mmWave radar. mmRadarTwin links a real radar measurement branch with an Unreal Engine scene-simulation branch through a shared receive-channel and range-angle processing interface. The simulator writes complex multi-channel receive grids and exports per-path contribution records that identify the actor, material tag, propagation event, and output-bin support of each simulated return. We evaluate mmRadarTwin in an office deployment using a commodity monostatic mmWave radar and mobile scene-capture hardware. Across 154 measured poses spanning 22 radar locations, the current physics-only path-basis simulator recalls 70.8% of measurement-active geometry-supported response regions in the central usable field of view while exposing residuals caused by weak or missing path support, shifted responses, unsupported anchors, and missing physical mechanisms. Rather than claiming complete radar-map reconstruction or cross-room generalization, mmRadarTwin establishes a practical systems workflow for constructing, comparing, and diagnosing indoor radar digital twins. △ Less

Submitted 30 July, 2026; originally announced July 2026.

Comments: 7 figures, 4 tables

Harness-G: A Graph-Structured Harness for Search Agents

Authors: Yanning Hou, Haoyuan Chen, Sihang Zhou, Xiaoshu Chen, Xirui Liu, Duanyang Yuan, Lingyuan Meng, Siwei Wang, Quan Liu, Jian Huang

Abstract: Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. Current studies mainly improve training with denser or more structured credit signals, but rarely examine whether retrieval is properly formulated at the policy-environment interface. We observe pronounced retrieval alias… ▽ More Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. Current studies mainly improve training with denser or more structured credit signals, but rarely examine whether retrieval is properly formulated at the policy-environment interface. We observe pronounced retrieval aliasing during Search-R1 training: rollouts for the same question continue to generate distinct query strings, yet their accumulated evidence sets increasingly overlap. We call this phenomenon retrieval-equivalence collapse; in this regime, trajectories approach utility equivalence with respect to retrieval decisions, leaving within-group returns with little effective retrieval contrast. To address this problem, we propose Harness-G, a graph-structured retrieval framework that redesigns this interface. It reformulates free-form query generation as finite action selection: the policy selects an evidence sentence or entity, or chooses to answer, while the environment constructs the menu, tracks retrieval state, and validates and executes each choice. This interface reduces linguistic aliasing and makes same-state alternatives directly comparable. Building on this interface, we introduce Structured Non-myopic Credit (SNC), which uses a frozen answer scorer to compare the selected action with its alternatives and assigns downstream gains to the earlier actions that enabled them. Across six QA benchmarks, Harness-G achieves the highest average F1 at both evaluated model scales, outperforming the strongest baseline, Graph-R1, by 10.74 points at 1.5B and 3.98 points at 3B. △ Less

Submitted 12 August, 2026; v1 submitted 30 July, 2026; originally announced July 2026.

Comments: Code:https://github.com/7HHHHH/Harness-G

ZMIS-SAM: Segment Anything Model Enhanced with Wavelet Transform for Zooplankton Microscopy Image Instance Segmentation

Authors: Dekun Yuan, Zhongwei Li, Zheng Qiao, Jie Zhang

Abstract: As primary consumers in the marine food chain, zooplankton play a crucial role in maintaining marine ecological balance. However, the Segment Anything Model (SAM) exhibits limited performance in microscopic image instance segmentation due to its lack of zooplankton-specific domain knowledge. To address these challenges, we propose a novel instance segmentation model based on SAM and wavelet transf… ▽ More As primary consumers in the marine food chain, zooplankton play a crucial role in maintaining marine ecological balance. However, the Segment Anything Model (SAM) exhibits limited performance in microscopic image instance segmentation due to its lack of zooplankton-specific domain knowledge. To address these challenges, we propose a novel instance segmentation model based on SAM and wavelet transform (ZMIS-SAM), effectively tackling issues such as inaccurate classification, discontinuous segmentation of slender appendages, and incomplete boundary segmentation. Our framework incorporates three core innovations: ZM-ViT enhances SAM's capability to model zooplankton morphology and image intensity distributions through two lightweight adapters, the Neighboring Feature Aggregation Module (NFAM) improves continuous segmentation of semi-transparent slender appendages by integrating general-purpose and domain-specific features, and the Wavelet-based Multi-scale Multi-directional Feature Enhancement (WM2FE) module effectively recovers high-frequency details to refine boundary segmentation completeness. Extensive experiments demonstrate that ZMIS-SAM achieves state-of-the-art instance segmentation performance on the zooplankton dataset and exhibits strong generalization capability across multiple public cross-domain datasets. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Nonlinear Stability and Instability of Finite-Energy Solutions of the Compressible Euler-Riesz Equations with General Pressure Laws

Authors: Jose A. Carrillo, Samuel R. Charles, Gui-Qiang G. Chen, Difan Yuan

Abstract: The compressible Euler-Riesz equations arise in the modeling of a wide range of physical phenomena, including stellar dynamics, plasma physics, and mathematical biology. In this paper, we investigate the nonlinear stability and instability of steady states for the multidimensional compressible Euler-Riesz equations under general pressure laws. In the polytropic case, we establish the nonlinear ins… ▽ More The compressible Euler-Riesz equations arise in the modeling of a wide range of physical phenomena, including stellar dynamics, plasma physics, and mathematical biology. In this paper, we investigate the nonlinear stability and instability of steady states for the multidimensional compressible Euler-Riesz equations under general pressure laws. In the polytropic case, we establish the nonlinear instability of steady states in the mass-supercritical regime for attractive potentials; this is achieved by analyzing the concavity of the free energy along mass-preserving dilations. At the mass-critical exponent, we show that, for any steady state, there exist solutions that start arbitrarily close to it, but develop growing support. For general pressure laws, we employ a concentration-compactness approach to prove the existence of energy minimizers and establish the nonlinear stability of steady states. Moreover, we quantify the finite-time stability by deriving a relative entropy bound for finite-energy solutions, without requiring uniform pointwise upper and lower bounds on the density. We further exploit the convexity of the second moment to obtain quantitative growth estimates for solutions with positive energy, thereby proving the local nature of the stability result. Finally, we prove the global existence of finite-energy weak solutions to the compressible Euler-Riesz equations with spherical symmetry for general pressure laws via the compensated compactness method, thereby yielding unconditional stability around steady states within the class of weak solutions. The approach developed in this paper should be useful for solving other nonlinear partial differential equations involving similar difficulties. △ Less

Submitted 29 July, 2026; originally announced July 2026.

Comments: 72 pages

MSC Class: 35Q31; 35Q35; 35B35; 35L65; 35L67; 35D30; 35R09; 35B44; 76N10; 35B25; 35A15; 35R35; 76N17

AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities

Authors: Kai Chen, Zichen Ding, Jiaye Ge, Shufan Jiang, Mo Li, Qingqiu Li, Zehao Li, Zonglin Li, Tianhao Liang, Shudong Liu, Zerun Ma, Zixin Shang, Wenhui Tian, Zun Wang, Liwei Wu, Zhenyu Wu, Jun Xu, Bowen Yang, Dingbo Yuan, Qi Zhang, Songyang Zhang, Peiheng Zhou, Dongsheng Zhu

Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To address this, we introduce AgentCompass, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based… ▽ More As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical. However, current evaluation pipelines remain highly fragmented and tightly coupled, hindering reproducibility and causing redundant engineering. To address this, we introduce AgentCompass, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based agents. AgentCompass organizes the evaluation process around three independent components, namely Benchmark, Harness, and Environment, thereby enabling flexible configurations without requiring the reimplementation of complex execution logic. Furthermore, it features a fault-tolerant asynchronous runtime and comprehensive trajectory analysis tools to transparently diagnose nuanced failure modes like reward-hacking. Natively supporting over 20 benchmarks across five capability dimensions, AgentCompass provides the community with a scalable and reproducible infrastructure for advancing agent research. △ Less

Submitted 20 July, 2026; v1 submitted 15 July, 2026; originally announced July 2026.

Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data

Authors: Xuanyu Chen, Nan Yang, Shuai Wang, Dong Yuan

Abstract: Recent research has introduced distributed self-supervised learning (D-SSL) approaches to leverage vast amounts of unlabeled decentralized data. However, D-SSL faces the critical challenge of data heterogeneity, and there is limited theoretical understanding of how different D-SSL frameworks respond to this challenge. To fill this gap, we present a rigorous theoretical analysis of the robustness o… ▽ More Recent research has introduced distributed self-supervised learning (D-SSL) approaches to leverage vast amounts of unlabeled decentralized data. However, D-SSL faces the critical challenge of data heterogeneity, and there is limited theoretical understanding of how different D-SSL frameworks respond to this challenge. To fill this gap, we present a rigorous theoretical analysis of the robustness of D-SSL frameworks under non-IID (non-independent and identically distributed) settings. Our results show that pre-training with Masked Image Modeling (MIM) is inherently more robust to heterogeneous data than Contrastive Learning (CL), and that the robustness of decentralized SSL increases with average network connectivity, implying that federated learning (FL) is no less robust than decentralized learning (DecL). These findings provide a solid theoretical foundation for guiding the design of future D-SSL algorithms. To further illustrate the practical implications of our theory, we introduce MAR loss, a refinement of the MIM objective with local-to-global alignment regularization. Extensive experiments across model architectures and distributed settings validate our theoretical insights, and additionally confirm the effectiveness of MAR loss as an application of our analysis. △ Less

Submitted 2 July, 2026; originally announced July 2026.

Comments: Accepted at ICLR2026

WaterGen: Decoupling Scene and Medium in Underwater Image Generation

Authors: Jiayi Wu, Tianfu Wang, Tianyi Xiong, Dehao Yuan, Xiaomin Lin, Md Jahidul Islam, Cornelia Fermuller, Christopher Metzler, Yiannis Aloimonos

Abstract: Underwater computer vision tasks, such as detection, restoration, and segmentation, are limited by the scarcity of large-scale and diverse training data. We introduce WaterGen, a method for generating large-scale, realistic, and diverse underwater images that provides independent control of the scene and water medium conditions. Our approach treats underwater image generation as the decoupled cont… ▽ More Underwater computer vision tasks, such as detection, restoration, and segmentation, are limited by the scarcity of large-scale and diverse training data. We introduce WaterGen, a method for generating large-scale, realistic, and diverse underwater images that provides independent control of the scene and water medium conditions. Our approach treats underwater image generation as the decoupled control of two factors: realistic and diverse scene content (what is in the image), and accurate and controllable water medium effects (what the water does to the image). Existing methods generally achieve only part of this objective: they either provide controllability with limited realism or diversity, or generate realistic scenes without accurately and independently modeling water-medium effects. Our key insight, that allows us to avoid this compromise, is that scene generation and medium modeling can be decoupled within a latent diffusion framework, enabling diverse scene generation together with accurate and controllable underwater appearance. To do this, we decompose underwater image synthesis into two stages. First, we fine-tune the latent diffusion U-Net using degradation-free underwater images so that it learns to generate diverse and realistic latent embeddings of underwater scene content without medium-induced degradation. Second, we formulate the physically accurate medium degradation synthesis as a conditional decoding process applied to these latent embeddings. This decoupled design allows our model to generate diverse scenes with full control of underwater appearance. We leverage WaterGen to build large-scale synthetic underwater datasets that are diverse in scene structures and accurate in water effects and pseudo-labels. We demonstrate that our synthetic data consistently improve downstream performance in underwater restoration and semantic segmentation. △ Less

Submitted 30 June, 2026; originally announced June 2026.

Controlled chemical vapor deposition for synthesis of emerging Mo(W)Te2 systems

Authors: Ya Deng, Zi-Yi Han, Yao Wu, Kongyang Yi, Ya-Ning Ren, Dundong Yuan, Chao Zhu, Lin He, Zheng Liu

Abstract: The Group-VI transition metal ditellurides offer a rich platform for correlated and topological phenomena, yet their structural polymorphism and instability complicate the creation of single crystals and heterointerfaces. Here, we introduce a confined-space chemical vapor deposition (CVD) strategy that lowers the growth temperature window and, when combined with tailored precursor configurations a… ▽ More The Group-VI transition metal ditellurides offer a rich platform for correlated and topological phenomena, yet their structural polymorphism and instability complicate the creation of single crystals and heterointerfaces. Here, we introduce a confined-space chemical vapor deposition (CVD) strategy that lowers the growth temperature window and, when combined with tailored precursor configurations and stepwise thermal ramps, enables the deterministic synthesis of high-quality single crystals, alloys, and lateral/vertical heterostructures. High-resolution aberration-corrected STEM provides atomic characterization of lattice-matched Mo(W)Te2 lateral heterostructure, revealing nearly atomically sharp, compositionally well-defined seamless boundaries. This approach avoids the thickness nonuniformity and structural limitations commonly associated with exfoliated samples, enabling reproducible fabrication of clean heterointerfaces and establishing a nearly ideal in-situ experimental system. Furthermore, scanning tunneling microscopy and spectroscopy (STM and STS) enable direct imaging of the seamless boundaries in Mo(W)Te2 lateral heterostructures, while uncovering their distinct real-space distributions of the local density of states. Our results establish a scalable pathway for engineering crystalline Te-based structures with controlled geometry and stacking, providing an essential step toward quantum and topological device platforms based on the transition metal ditellurides family. △ Less

Submitted 25 June, 2026; originally announced June 2026.

Journal ref: Materials Today,2026,98,103390

Phase-drifting with emitting plasma temperature in the quasi-periodic pulsations of an X-class solar flare

Authors: Libo Fu, Valery M. Nakariakov, Ding Yuan, Suraj Sahu, Song Feng, Ehsan Tavabi

Abstract: Recent multi-wavelength observations of solar flares have provided new constraints on the physical origin of quasi-periodic pulsations (QPPs). In an X-class flare, we detect a short-lived $\sim$5-minute QPP simultaneously in hard X-rays, extreme-ultraviolet (EUV), and soft X-ray emissions, exhibiting a clear phase-drifting behavior with emitting plasma temperature. Based on phase-resolved timing a… ▽ More Recent multi-wavelength observations of solar flares have provided new constraints on the physical origin of quasi-periodic pulsations (QPPs). In an X-class flare, we detect a short-lived $\sim$5-minute QPP simultaneously in hard X-rays, extreme-ultraviolet (EUV), and soft X-ray emissions, exhibiting a clear phase-drifting behavior with emitting plasma temperature. Based on phase-resolved timing analysis, it is found that (i) the QPPs in all diagnostics share nearly identical oscillation periods, (ii) a systematic temperature-dependent phase drifting is present, with the phase delay relative to the hard X-ray emission increases systematically from the hottest to cooler EUV channels, and (iii) the QPP persists for only a few cycles during the impulsive phase. These properties imply that periodic magnetic reconnection, possibly triggered by the leakage of 5-minute oscillations from the lower atmosphere, modulates the non-thermal electrons responsible for the leading Hard X-ray QPPs. Subsequently, plasma heating and cooling processes manifest sequentially across passbands with different temperature responses, resulting in the observed temperature-dependent phase drifting. These results provide novel observational evidence supporting the use of multi-temperature, multi-wavelength phase relationships to constrain the temporal evolution of flare energy release and the origins of QPPs. △ Less

Submitted 9 June, 2026; originally announced June 2026.

Interaction of Fast Magnetoacoustic Wave with the Localized Coronal Null and Generation of the Energetic Alfvén Wave Packet

Authors: Akash Bairagi, Abhishekh K. Srivastava, Sripan Mondal, T. V. Zaqarashvili, Astrid Veronig, P. Bourdin, Ding Yuan, Ryun-Young Kwon

Abstract: In the present paper, we have performed 2.5D resistive magnetohydrodynamic simulations of the interaction of a fast magnetoacoustic wave with a localized coronal magnetic null point. As a result, an Alfvén wave packet is generated by the mode conversion when a fast magnetoacoustic perturbation interacts with the null point. The field-aligned plasma flows are also generated due to the non-linear ef… ▽ More In the present paper, we have performed 2.5D resistive magnetohydrodynamic simulations of the interaction of a fast magnetoacoustic wave with a localized coronal magnetic null point. As a result, an Alfvén wave packet is generated by the mode conversion when a fast magnetoacoustic perturbation interacts with the null point. The field-aligned plasma flows are also generated due to the non-linear effects. When the fast mode wavefront interacts with the null, some parts of this wavefront get refracted around it, while some other part is trapped at the null region. Subsequently, the velocity fluctuation out of the plane and in-phase magnetic field fluctuations have evolved and propagated with the local Alfvén speed along the separatrixes at one side of the coronal null region. The resulting disturbance behaves as an incompressible and energetic Alfvén wave packet. A secondary fast magnetoacoustic wave is also produced and propagates. In the synthetic SDO/AIA observations, no intensity fluctuations are evident in the region where the Alfvén wave packet propagates, while the fast magnetoacoustic wave fronts are clearly evident. Our results suggest that given the appropriate physical conditions at the null, when the fast mode wave is incident, Alfvén packets can be excited due to the mode conversion, further carrying substantial momentum and energy flux in the solar corona. △ Less

Submitted 4 June, 2026; originally announced June 2026.

Comments: 19 pages, 8 figures, Accepted for publication in The Astrophysical Journal

Recurrent Coronal Jets and QPPs: Periodic Reconnection and Localized Heating Across Quiet-Sun to Active Regions

Authors: Sudheer K. Mishra, Kartika Sangal, Balveer Singh, Ayumi Asai, A. K. Srivastava, Ding Yuan

Abstract: We analyze quasi-periodic pulsations (QPPs) in recurrent coronal jets driven by periodic magnetic reconnection associated with successive flux emergence in the fan-spine magnetic topologies. Using the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), we investigate three long-lived recurring jets spanning quiet Sun to moderate-field-strength regions, each exhibiting… ▽ More We analyze quasi-periodic pulsations (QPPs) in recurrent coronal jets driven by periodic magnetic reconnection associated with successive flux emergence in the fan-spine magnetic topologies. Using the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), we investigate three long-lived recurring jets spanning quiet Sun to moderate-field-strength regions, each exhibiting recurrent eruptions linked to episodic reconnection. Wavelet analysis of multithermal AIA EUV jet-base light curves detects QPPs with periods of 6-13 min, exceeding typical p-mode oscillation periods. Distance-time analysis reveals quasi-periodic propagating ridges, interpreted as recurrent field-aligned plasma ejections, and morphological similarities to slow magnetoacoustic waves, which cannot be entirely excluded. However, the dominant photospheric unsigned flux periodicities of 10-32 min at the jet source regions favor the reconnection-driven interpretation. DEM analysis confirms multithermal plasma with the hottest emission concentrated near the jet base, and the QPP periods fall well below both radiative and conductive cooling timescales, implying persistent localized heating within the fan-spine configuration. These results demonstrate that periodic reconnection in fan-spine topologies drives recurrent jet eruptions and contributes to localized coronal heating across the quiet Sun, moderate-field strength regions, and active regions. △ Less

Submitted 3 June, 2026; originally announced June 2026.

Comments: 17 pages, 8 figures, 1 table; Accepted for publication in the Philosophical Transactions of the Royal Society of London Series A

Scheduling Mechanisms in Wireless Sensor-Actuator Networks for Multi-rate Periodic Control in Industry 4.0

Authors: Dingwen Yuan, Luis F. Abanto-Leon, Matthias Hollick

Abstract: This paper investigates scheduling strategies for wireless sensor-actuator networks (WSANs) in Industry 4.0 scenarios. In particular, we address the problem of real-time scheduling for multi-rate control systems by proposing a novel framework. Our framework features four strategies that improve reliability, schedulability and execution time, and reduce communication and storage costs. Two-phase sc… ▽ More This paper investigates scheduling strategies for wireless sensor-actuator networks (WSANs) in Industry 4.0 scenarios. In particular, we address the problem of real-time scheduling for multi-rate control systems by proposing a novel framework. Our framework features four strategies that improve reliability, schedulability and execution time, and reduce communication and storage costs. Two-phase scheduling is our first strategy, devised to improve communication reliability. Our second strategy is the least-laxity-first with remaining conflicts (LLF-RC) scheduling algorithm, which has high schedulability and affordable execution time. LLF-RC also keeps the maximum queue length at a moderate level, making it suitable for storage-constrained devices. Our third and fourth strategies are opportunistic aggregation and repetitive scheduling. Opportunistic aggregation performs simple and effective packet aggregation, enhancing schedulability by up to 97% and reducing execution time by up to 29%, in our simulation. Repetitive scheduling has negligible execution time, and contributes to minimize communication and storage costs. It reduces the maximum execution time by 92% and the maximum communication and storage cost by 99%, in our simulation. We compare our proposed framework against existing approaches, and evaluate the advantages of our strategies in realistic scenarios. △ Less

Submitted 28 May, 2026; originally announced May 2026.

Comments: IEEE Sensors Journal

A Markov-Chain-Monte-Carlo-based Hybrid Noise Inference for Continuous Wavelet Power Spectra: with Applications to Solar and Stellar Oscillatory Signals

Authors: Song Feng, Lin Li, Ding Yuan

Abstract: Detecting oscillations in solar and stellar time series is complicated by non-stationary red noise and evolving background emission. Methods based on detrending and AR(1)-based wavelet analysis can introduce spurious periodicities and do not adequately describe time-dependent backgrounds. We develop a Bayesian approach that combines the continuous wavelet transform with MCMC sampling to infer a ti… ▽ More Detecting oscillations in solar and stellar time series is complicated by non-stationary red noise and evolving background emission. Methods based on detrending and AR(1)-based wavelet analysis can introduce spurious periodicities and do not adequately describe time-dependent backgrounds. We develop a Bayesian approach that combines the continuous wavelet transform with MCMC sampling to infer a time-dependent background spectrum. The background is represented by a power-law plus white-noise component, with parameters allowed to vary smoothly in time, so that significance levels can be evaluated locally without explicit detrending. Tests with synthetic data show that injected oscillations are recovered reliably, while false detections are suppressed in pure-noise cases. Using a frequency-domain signal-to-noise ratio (S/N), we find that oscillations can be identified robustly when the S/N is greater than or equal to 2 under mixed noise conditions. The detectable period range is limited by wavelet resolution, from about 3-4 sampling intervals up to roughly one-quarter of the total duration. Application to GOES soft X-ray flare observations shows that the method isolates quasi-periodic oscillations with improved temporal localization compared to standard wavelet and Fourier-based approaches. Meanwhile, this behavior is consistent across a range of noise conditions and signal morphologies. △ Less

Submitted 22 May, 2026; originally announced May 2026.

Comments: 8 figures

Journal ref: The Astrophysical Journal Supplement Series, 2026

Chromospheric resonator model for sunspot revealed by multi-height observation of umbral wave

Authors: Kartika Sangal, A. K. Srivastava, Libo Fu, Ding Yuan, Song Feng, Yuandeng Shen

Abstract: Sunspots are transient, magnetically intense features that host oscillations linked to magnetohydrodynamic (MHD) waves. These waves may contribute to plasma heating and drive mass flows in the solar wind. Beyond their energetic role, they serve as diagnostic tools for probing sunspot structure. In this study, we investigated chromospheric wave propagation in a sunspot using high-resolution, multi-… ▽ More Sunspots are transient, magnetically intense features that host oscillations linked to magnetohydrodynamic (MHD) waves. These waves may contribute to plasma heating and drive mass flows in the solar wind. Beyond their energetic role, they serve as diagnostic tools for probing sunspot structure. In this study, we investigated chromospheric wave propagation in a sunspot using high-resolution, multi-wavelength observations from the Goode Solar Telescope at Big Bear Solar Observatory. Spectral analysis shows that the intensity at H$α$ line core and its wings exhibited oscillatory signal at about 3 min. We performed a cross-wavelet analysis to examine the phase relationship between the wing-integrated and line-core intensity oscillations of the H$α$ line and the centroid-derived H$α$ Doppler velocity. We also analyze the phase relationships between intensity pairs from different passband combinations of the H$α$ line. The results indicate the presence of slow magnetoacoustic modes manifesting standing waves along with upward propagating waves. The observed phase patterns suggest that umbral waves are confined within a non-ideal acoustic resonator, providing measurable wave properties that could serve as input for sunspot seismology and refine models of sunspot atmospheric structure. △ Less

Submitted 19 May, 2026; originally announced May 2026.

Comments: Accepted for publication in Monthly Notices of the Royal Astronomical Society

Journal ref: Mon Not R Astron Soc (2026)

PoseCompass: Intelligent Synthetic Pose Selection for Visual Localization

Authors: Yanan Zhou, Zhaoyan Qian, Yanli Li, Nan Yang, Zhongliang Guo, Dong Yuan

Abstract: In visual localization, Absolute Pose Regression (APR) enables real-time 6-DoF camera pose inference from single images, yet critically depends on fine-tuning data quality and coverage. While recent methods leverage 3D Gaussian Splatting (3DGS) for novel view synthesis-based data augmentation, random sampling generates redundant views and noisy samples from poorly reconstructed regions. To mitigat… ▽ More In visual localization, Absolute Pose Regression (APR) enables real-time 6-DoF camera pose inference from single images, yet critically depends on fine-tuning data quality and coverage. While recent methods leverage 3D Gaussian Splatting (3DGS) for novel view synthesis-based data augmentation, random sampling generates redundant views and noisy samples from poorly reconstructed regions. To mitigate this research gap, we propose PoseCompass, an intelligent pose selection pipeline for 3DGS-based APR. PoseCompass formulates synthetic pose selection and derives a value-based pose ranking mechanism to identify informative poses. The ranking integrates three dimensions: Localization Difficulty, favoring challenging regions; Coverage Novelty, exploring under-sampled areas; and Rendering Observability, filtering artifacts and noise. PoseCompass then generates trajectory-constrained candidates, selects the top-K ranked poses, and synthesizes views using 3DGS with lightweight diffusion-based alignment. Finally, the pose regressor is fine-tuned on mixed real and synthetic data. We evaluate PoseCompass on 7-Scenes, where it reduces adaptation time from 15.2 to 5.1 minutes, a 3x speedup, while cutting median pose errors by 53.8 percent and significantly outperforming random baselines. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Effect of Solar Flares on Decayless Kink Oscillations in Nearby Coronal Loops

Authors: Zhiyi Li, Valery M. Nakariakov, Ding Yuan, Song Feng

Abstract: We present a statistical study of 130 solar flares (B to X class) that lack soft X-ray quasi-periodic pulsations and show no kink oscillations of nearby coronal loops visible in SDO/AIA 171~Å~images. The aim is to investigate whether decayless kink oscillations of coronal loops respond to nearby flaring activity. Using the Fractional Anisotropy-based Video Motion Magnification technique, we detect… ▽ More We present a statistical study of 130 solar flares (B to X class) that lack soft X-ray quasi-periodic pulsations and show no kink oscillations of nearby coronal loops visible in SDO/AIA 171~Å~images. The aim is to investigate whether decayless kink oscillations of coronal loops respond to nearby flaring activity. Using the Fractional Anisotropy-based Video Motion Magnification technique, we detected low-amplitude decayless oscillations in all 130 loops before, during, and after each flare, confirming their ubiquitous nature. Oscillation periods are found to range from 122~s to 268~s, and the projected displacement amplitudes are 0.023--0.111~Mm. No amplitude--period correlation is found. For each event, we estimated the amplitude before, during, and after the flare. Across all flare classes, the average amplitude remains unchanged. However, in some specific cases, the oscillation amplitude may exhibit minor changes. For B-, C-, and M-class flares, the fraction of events with an amplitude change exceeding 10% is approximately 23%, 41%, and 36%, respectively. In M-class flares, such minor amplitude increases occur four times more often than decreases; in X-class flares (only six events), decreases dominate by a factor of three. The fraction of events that exhibit an increase in the amplitude of more than 20% appears to be highest when the loop centre is located at a distance of 100--120~Mm from the flare site, reaching 33% (6 out of 18 events). Overall, the amplitude of decayless kink oscillations does not undergo a major change in response to nearby flares, especially for less powerful classes, suggesting that flare-related processes such as blast waves and reconnection inflows have little effect on the energy supply to oscillating loops. △ Less

Submitted 12 May, 2026; originally announced May 2026.

What is The Probability That A Random Graph With A Given Degree Sequence is Connected?

Authors: Louigi Addario-Berry, Bruce Reed, Dao Chen Yuan

Abstract: An $n$-tuple $D=(d(1),\dots,d(n))$ is a \emph{feasible degree sequence} if there is a graph on $\{1,\dots,n\}$ such that $i$ has degree $d(i)$. Any such graph will have $m=\sum_{i=1}^n d(i)/2$ edges. Letting $G(D)$ be a graph chosen uniformly from those with the given degree sequence, we upper-bound the probability that $G(D)$ is disconnected based on the number of vertices of degree $d$ for small… ▽ More An $n$-tuple $D=(d(1),\dots,d(n))$ is a \emph{feasible degree sequence} if there is a graph on $\{1,\dots,n\}$ such that $i$ has degree $d(i)$. Any such graph will have $m=\sum_{i=1}^n d(i)/2$ edges. Letting $G(D)$ be a graph chosen uniformly from those with the given degree sequence, we upper-bound the probability that $G(D)$ is disconnected based on the number of vertices of degree $d$ for small $d$, and develop a powerful tool for proving such bounds. If there are any vertices of degree zero the probability $G$ is disconnected is $1$, so we assume there are no such vertices. Our results then imply that if there are $o(\sqrt{m})$ vertices of degree $1$ and $o(m)$ vertices of degree 2 then with high probability $G$ is connected, while if there are no vertices of degree 1 or 2 then the probability $G$ is disconnected is $O(\frac{n^4}{m^6})$. △ Less

Submitted 28 April, 2026; originally announced April 2026.

Comments: 28 pages, 10 figures

MSC Class: 60C05; 05C80

Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning

Authors: Chaoran Chen, Dayu Yuan, Peter Kairouz

Abstract: In agentic workflows, LLMs frequently process retrieved contexts that are legally protected from further training. However, auditors currently lack a reliable way to verify if a provider has violated the terms of service by incorporating these data into post-training, especially through Reinforcement Learning (RL). While standard auditing relies on verbatim memorization and membership inference, t… ▽ More In agentic workflows, LLMs frequently process retrieved contexts that are legally protected from further training. However, auditors currently lack a reliable way to verify if a provider has violated the terms of service by incorporating these data into post-training, especially through Reinforcement Learning (RL). While standard auditing relies on verbatim memorization and membership inference, these methods are ineffective for RL-trained models, as RL primarily influences a model's behavioral style rather than the retention of specific facts. To bridge this gap, we introduce Behavioral Canaries, a new auditing mechanism for RLFT pipelines. The framework instruments preference data by pairing document triggers with feedback that rewards a distinctive stylistic response, inducing a latent trigger-conditioned preference if such data are used in training. Empirical results show that these behavioral signals enable detection of unauthorized document-conditioned training, achieving a 67% detection rate at a 10% false-positive rate (AUROC = 0.756) at a 1% canary injection rate. More broadly, our results establish behavioral canaries as a new auditing mechanism for RLFT pipelines, enabling auditors to test for training-time influence even when such influence manifests as distributional behavioral change rather than memorization. We release our code at: https://github.com/CRChenCode/behavioral_canary. △ Less

Submitted 6 August, 2026; v1 submitted 23 April, 2026; originally announced April 2026.

Decoupled Travel Planning with Behavior Forest

Authors: Duanyang Yuan, Sihang Zhou, Yanning Hou, Xiaoshu Chen, Haoyuan Chen, Ke Liang, Jiyuan Liu, Chuan Ma, Xinwang Liu, Jian Huang

Abstract: Behavior sequences, composed of executable steps, serve as the operational foundation for multi-constraint planning problems such as travel planning. In such tasks, each planning step is not only constrained locally but also influenced by global constraints spanning multiple subtasks, leading to a tightly coupled and complex decision process. Existing travel planning methods typically rely on a si… ▽ More Behavior sequences, composed of executable steps, serve as the operational foundation for multi-constraint planning problems such as travel planning. In such tasks, each planning step is not only constrained locally but also influenced by global constraints spanning multiple subtasks, leading to a tightly coupled and complex decision process. Existing travel planning methods typically rely on a single decision space that entangles all subtasks and constraints, failing to distinguish between locally acting constraints within a subtask and global constraints that span multiple subtasks. Consequently, the model is forced to jointly reason over local and global constraints at each decision step, increasing the reasoning burden and reducing planning efficiency. To address this problem, we propose the Behavior Forest method. Specifically, our approach structures the decision-making process into a forest of parallel behavior trees, where each behavior tree is responsible for a subtask. A global coordination mechanism is introduced to orchestrate the interactions among these trees, enabling modular and coherent travel planning. Within this framework, large language models are embedded as decision engines within behavior tree nodes, performing localized reasoning conditioned on task-specific constraints to generate candidate subplans and adapt decisions based on coordination feedback. The behavior trees, in turn, provide an explicit control structure that guides LLM generation. This design decouples complex tasks and constraints into manageable subspaces, enabling task-specific reasoning and reducing the cognitive load of LLM. Experimental results show that our method outperforms state-of-the-art methods by 6.67% on the TravelPlanner and by 11.82% on the ChinaTravel benchmarks, demonstrating its effectiveness in increasing LLM performance for complex multi-constraint travel planning. △ Less

Submitted 23 April, 2026; originally announced April 2026.

AtomicRAG: Atom-Entity Graphs for Retrieval-Augmented Generation

Authors: Yanning Hou, Duanyang Yuan, Sihang Zhou, Xiaoshu Chen, Ke Liang, Siwei Wang, Xinwang Liu, Jian Huang

Abstract: Recent GraphRAG methods integrate graph structures into text indexing and retrieval, using knowledge graph triples to connect text chunks, thereby improving retrieval coverage and precision. However, we observe that treating text chunks as the basic unit of knowledge representation rigidly groups multiple atomic facts together, limiting the flexibility and adaptability needed to support diverse re… ▽ More Recent GraphRAG methods integrate graph structures into text indexing and retrieval, using knowledge graph triples to connect text chunks, thereby improving retrieval coverage and precision. However, we observe that treating text chunks as the basic unit of knowledge representation rigidly groups multiple atomic facts together, limiting the flexibility and adaptability needed to support diverse retrieval scenarios. Additionally, triple-based entity linking is sensitive to relation-extraction errors, which can lead to missing or incorrect reasoning paths and ultimately hurt retrieval accuracy. To address these issues, we propose the Atom-Entity Graph, a more precise and reliable architecture for knowledge representation and indexing. In our approach, knowledge is stored as knowledge atoms, namely individual, self-contained units of factual information, rather than coarse-grained text chunks. This allows knowledge elements to be flexibly reassembled without mutual interference, thereby enabling seamless alignment with diverse query perspectives. Edges between entities simply indicate whether a relationship exists. By combining personalized PageRank with relevance-based filtering, we maintain accurate entity connections and improve the reliability of reasoning. Theoretical analysis and experiments on five public benchmarks show that the proposed AtomicRAG algorithm outperforms strong RAG baselines in retrieval accuracy and reasoning robustness. Code: https://github.com/7HHHHH/AtomicRAG. △ Less

Submitted 10 February, 2026; originally announced April 2026.

RefAerial: A Benchmark and Approach for Referring Detection in Aerial Images

Authors: Guyue Hu, Hao Song, Yuxing Tong, Duzhi Yuan, Dengdi Sun, Aihua Zheng, Chenglong Li, Jin Tang

Abstract: Referring detection refers to locate the target referred by natural languages, which has recently attracted growing research interests. However, existing datasets are limited to ground images with large object centered in relative small scenes. This paper introduces a large-scale challenging dataset for referring detection in aerial images, termed as RefAerial. It distinguishes from conventional g… ▽ More Referring detection refers to locate the target referred by natural languages, which has recently attracted growing research interests. However, existing datasets are limited to ground images with large object centered in relative small scenes. This paper introduces a large-scale challenging dataset for referring detection in aerial images, termed as RefAerial. It distinguishes from conventional ground referring detection datasets by 4 characteristics: (1) low but diverse object-to-scene ratios, (2) numerous targets and distractors, (3)complex and fine-grained referring descriptions, (4) diverse and broad scenes in the aerial view. We also develop a human-in-the-loop referring expansion and annotation engine (REA-Engine) for efficient semi-automated referring pair annotation. Besides, we observe that existing ground referring detection approaches exhibiting serious performance degradation on our aerial dataset since the intrinsic scale variety issue within or across aerial images. Therefore, we further propose a novel scale-comprehensive and sensitive (SCS) framework for referring detection in aerial images. It consists of a mixture-of-granularity (MoG) attention and a two-stage comprehensive-to-sensitive (CtS) decoding strategy. Specifically, the mixture-of-granularity attention is developed for scale-comprehensive target understanding. In addition, the two-stage comprehensive-to-sensitive decoding strategy is designed for coarse-to-fine referring target decoding. Eventually, the proposed SCS framework achieves remarkable performance on our aerial referring detection dataset and even promising performance boost on conventional ground referring detection datasets. △ Less

Submitted 23 April, 2026; v1 submitted 22 April, 2026; originally announced April 2026.

Astrophysically Realistic Secondary Spins Trigger Chaos in Schwarzschild Spacetime and Discernible Gravitational Wave Signatures

Authors: Dan-Dan Yuan, Jia-Geng Jiao, Yu-Qi Lei, Jun-Xi Shi, Jing-Qi Lai, Caiying Shao, Yu Tian

Abstract: Chaos in extreme-mass-ratio inspirals is often thought to require unrealistically large secondary spins, making its astrophysical relevance uncertain. However, we find that chaos persists across the astrophysically realistic spin range for a spinning secondary orbiting a Schwarzschild black hole. This nonintegrable dynamics leaves clear signatures in the emitted gravitational waves. Nearby regular… ▽ More Chaos in extreme-mass-ratio inspirals is often thought to require unrealistically large secondary spins, making its astrophysical relevance uncertain. However, we find that chaos persists across the astrophysically realistic spin range for a spinning secondary orbiting a Schwarzschild black hole. This nonintegrable dynamics leaves clear signatures in the emitted gravitational waves. Nearby regular and chaotic trajectories can remain similar in the time domain and retain broadly aligned dominant spectral peaks, yet chaotic signals develop a much less discrete frequency-domain structure with dense inter-peak power. Furthermore, we introduce a local spectral-flatness measure and find it to be several hundred times larger for the chaotic signal than for the neighboring regular signals. Finally, a change in the secondary spin by as little as \(1\%\) of its maximal physically allowed value can drive the system from regular to chaotic motion and produce distinctive detector-level waveforms. △ Less

Submitted 22 April, 2026; originally announced April 2026.

Comments: 19 pages, 11 figures

Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?

Authors: Yi Zhao, Di Yuan, Tao Deng, Suzhi Cao, Ying Dong

Abstract: Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources. Communication in each FL round typically consists of two phases: (i) distributing the global model from a server to clients, and (ii) collecting updated local models from clients to the server for aggregation. This paper focuses on a type of FL where communication between a client and the se… ▽ More Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources. Communication in each FL round typically consists of two phases: (i) distributing the global model from a server to clients, and (ii) collecting updated local models from clients to the server for aggregation. This paper focuses on a type of FL where communication between a client and the server is relay-based over dynamic networks, making routing optimization essential. A typical scenario is in-orbit FL, where satellites act as clients and communicate with a server (which can be a satellite, ground station, or aerial platform) via multi-hop inter-satellite links. This paper presents a comprehensive tractability analysis of routing optimization for in-orbit FL under different settings. For global model distribution, these include the number of models, the objective function, and routing schemes (unicast versus multicast, and splittable versus unsplittable flow). For local model collection, the settings consider the number of models, client selection, and flow splittability. For each case, we rigorously prove whether the global optimum is obtainable in polynomial time or the problem is NP-hard. Together, our analysis draws clear boundaries between tractable and intractable regimes for a broad spectrum of routing problems for in-orbit FL. For tractable cases, the derived efficient algorithms are directly applicable in practice. For intractable cases, we provide fundamental insights into their inherent complexity. These contributions fill a critical yet unexplored research gap, laying a foundation for principled routing design, evaluation, and deployment in satellite-based FL or similar distributed learning systems. △ Less

Submitted 21 April, 2026; originally announced April 2026.

Evaluating LLM Simulators as Differentially Private Data Generators

Authors: Nassima M. Bouzid, Dehao Yuan, Nam H. Nguyen, Mayana Pereira

Abstract: LLM-based simulators offer a promising path for generating complex synthetic data where traditional differentially private (DP) methods struggle with high-dimensional user profiles. But can LLMs faithfully reproduce statistical distributions from DP-protected inputs? We evaluate this using PersonaLedger, an agentic financial simulator, seeded with DP synthetic personas derived from real user stati… ▽ More LLM-based simulators offer a promising path for generating complex synthetic data where traditional differentially private (DP) methods struggle with high-dimensional user profiles. But can LLMs faithfully reproduce statistical distributions from DP-protected inputs? We evaluate this using PersonaLedger, an agentic financial simulator, seeded with DP synthetic personas derived from real user statistics. We find that PersonaLedger achieves promising fraud detection utility (AUC 0.70 at epsilon=1) but exhibits significant distribution drift due to systematic LLM biases--learned priors overriding input statistics for temporal and demographic features. These failure modes must be addressed before LLM-based methods can handle the richer user representations where they might otherwise excel. △ Less

Submitted 16 April, 2026; originally announced April 2026.

Comments: Submitted to ICLR 2026. 6 pages + appendix

MSC Class: 68T07; 68P27 ACM Class: I.2; G.3; K.4; I.6; J.1

Rf spectra and pseudogap in ultracold Fermi gases across the BCS-BEC crossover from pairing fluctuation theory

Authors: Chuping Li, Lin Sun, Kaichao Zhang, Junru Wu, Yuxuan Wu, Dingli Yuan, Pengyi Chen, Qijin Chen

Abstract: The pseudogap phenomenon is a hallmark of strongly interacting Fermi systems, from high-temperature superconductors to ultracold atomic gases, yet its precise origin remains debated. Here we calculate the spectral function and rf spectra of ultracold atomic gases across the BCS-BEC crossover to quantitatively investigate the pairing mechanism of the pseudogap. We advance our pairing fluctuation th… ▽ More The pseudogap phenomenon is a hallmark of strongly interacting Fermi systems, from high-temperature superconductors to ultracold atomic gases, yet its precise origin remains debated. Here we calculate the spectral function and rf spectra of ultracold atomic gases across the BCS-BEC crossover to quantitatively investigate the pairing mechanism of the pseudogap. We advance our pairing fluctuation theory by incorporating particle-hole fluctuations, which renormalize the effective interaction in the particle-particle channel. To achieve quantitative accuracy, we employ a full numerical convolution for the pair susceptibility and self-energy, moving beyond previous analytic pseudogap approximations. This convolution approach automatically captures two critical effects: (i) the full spectral broadening of fermions due to finite pair lifetime, and (ii) the previously neglected pair-hole scattering effect, which manifests as a substantial Hartree energy. We calculate the spectral function, and use rf spectral intensity maps and energy distribution curves to determine the quasiparticle dispersion. From these, we extract the pseudogap $Δ$, Hartree energy, and chemical potential, mapping their evolution across the crossover. Our results show that the pseudogap emerges continuously as the system moves from the BCS regime toward BEC. Furthermore, the pair spectral function reveals that pairs become diffusive at energies above 2$Δ$, indicating that the pair lifetime is governed by virtual binding and unbinding processes. Our calculations achieve quantitative agreement with recent experiments across the BCS-BEC crossover, including at unitarity, providing strong support for a pairing-based origin of the pseudogap as described by our pairing fluctuation theory. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: 12 pages, 15 figures

Beyond Message Passing: A Semantic View of Agent Communication Protocols

Authors: Dun Yuan, Fuyuan Lyu, Ye Yuan, Weixu Zhang, Bowei He, Jiayi Geng, Linfeng Du, Zipeng Sun, Yankai Chen, Changjiang Han, Jikun Kang, Xi Chen, Haolun Wu, Xue Liu

Abstract: Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with other agents, and operate across heterogeneous environments. This work presents a human-inspired perspective on this emerging landscape by organizing agent communication into three layers: communication, syntactic, and semantic. Under this framework, we sys… ▽ More Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with other agents, and operate across heterogeneous environments. This work presents a human-inspired perspective on this emerging landscape by organizing agent communication into three layers: communication, syntactic, and semantic. Under this framework, we systematically analyze 18 representative protocols and compare how they support reliable transport, structured interaction, and meaning-level coordination. Our analysis shows a clear imbalance in current protocol design. Most protocols provide increasingly mature support for transport, streaming, schema definition, and lifecycle management, but offer limited protocol-level mechanisms for clarification, context alignment, and verification. As a result, semantic responsibilities are often pushed into prompts, wrappers, or application-specific orchestration logic, creating hidden interoperability and maintenance costs. To make this gap actionable, we further identify major forms of technical debt in today's protocol ecosystem and distill practical guidance for selecting protocols under different deployment settings. We conclude by outlining a research agenda for interoperable, secure, and semantically robust agent ecosystems that move beyond message passing toward shared understanding. △ Less

Submitted 13 April, 2026; v1 submitted 29 March, 2026; originally announced April 2026.

Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design

Authors: Latent Labs Team, Sebastian M. Schmon, Daniella Pretorius, Simon Mathis, Rebecca Bartke-Croughan, Aishaini Puvanendran, James Vuckovic, Henry Kenlay, Mária Vlachynská, Alex Bridgland, Ivan Grishin, Sven Over, David Li, Bridget Li, Jonathan Crabbé, Agrin Hilmkil, Alexander W. R. Nelson, David Yuan, Annette Obika, Simon A. A. Kohl

Abstract: Drug discovery relies on iterative expert workflows that are slow to parallelize and difficult to scale. Here we introduce Latent-Y, an AI agent that autonomously executes complete antibody design campaigns from text prompts, covering literature review, target analysis, epitope identification, candidate design, computational validation, and selection of lab-ready sequences. Latent-Y is integrated… ▽ More Drug discovery relies on iterative expert workflows that are slow to parallelize and difficult to scale. Here we introduce Latent-Y, an AI agent that autonomously executes complete antibody design campaigns from text prompts, covering literature review, target analysis, epitope identification, candidate design, computational validation, and selection of lab-ready sequences. Latent-Y is integrated into the Latent Labs Platform, where it operates in the same environment as drug-discovery experts with access to bioinformatics tools, biological databases, and scientific literature. The agent can run fully autonomously end-to-end, or collaboratively, where researchers review progress, provide feedback, and direct subsequent steps. Candidate antibodies are generated using Latent-X2, our frontier generative model for drug-like antibody design. We demonstrate the agent's capability across three distinct campaign types: epitope discovery guided by therapeutic specifications, cross-species binder design, and autonomous design from a scientific publication targeting human transferrin receptor for blood-brain barrier crossing. Across nine targets, Latent-Y produced lab-confirmed nanobody binders against six, achieving a 67% target-level success rate with binding affinities reaching the single-digit nanomolar range, without human filtering or intervention. In user studies, experts working with Latent-Y completed design campaigns 56 times faster than independent expert time estimates, compressing weeks of work into hours. Because Latent-X2 is a general-purpose atomic-level model for biologics design, the same agent architecture naturally extends to macrocyclic peptide and mini-binder design campaigns, broadening autonomous discovery across therapeutic modalities. Latent-Y is available to selected partners at https://platform.latentlabs.com. △ Less

Submitted 1 April, 2026; v1 submitted 31 March, 2026; originally announced March 2026.

Comments: A.N. performed work as an advisor to Latent Labs. A.H. and D.Y. performed work while at Latent Labs

Magnetic-field-tunable commensurate multi-q charge orders on UTe2 (011) surface

Authors: Yuanji Li, Ruotong Yin, Jiashuo Gong, Dengpeng Yuan, Yuguang Wang, Shiyuan Wang, Mingzhe Li, Jiakang Zhang, Ziwei Xue, Zengyi Du, Shiyong Tan, Dong-Lai Feng, Ya-Jun Yan

Abstract: The heavy-fermion superconductor UTe2 has attracted intense interest as a candidate for spin-triplet pairing. Recent scanning tunneling microscopy (STM) studies have reported complex charge orders (COs) on its (011) surface, but their origin and relationship with superconductivity remain controversial. Here, by performing temperature-, magnetic field-, and sample-dependent STM measurements, we ide… ▽ More The heavy-fermion superconductor UTe2 has attracted intense interest as a candidate for spin-triplet pairing. Recent scanning tunneling microscopy (STM) studies have reported complex charge orders (COs) on its (011) surface, but their origin and relationship with superconductivity remain controversial. Here, by performing temperature-, magnetic field-, and sample-dependent STM measurements, we identify multiple new CO wave vectors beyond those previously reported. All these CO wave vectors are strictly locked to integer multiples of 1/14 and 1/4 of the reciprocal lattice vectors of the UTe2 (011) surface, and multiple of them coexist in real space, collectively revealing a family of field-tunable, commensurate multi-q COs. These COs exist within an energy range much larger than the superconducting energy scale, their emergence suppresses the density of states near EF, yet show negligible coupling to bulk superconductivity and magnetic vortices. Our findings strongly disfavor the Fermi surface nesting or primary pair-density-wave pictures, but are consistent with a surface parent spin order. △ Less

Submitted 28 March, 2026; originally announced March 2026.

Comments: 25 pages, 15 figures

Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer

Authors: Yulun Wu, Sravan Kumar Ankireddy, Samuel Sharpe, Nikita Seleznev, Dehao Yuan, Hyeji Kim, Nam H. Nguyen

Abstract: Efficiently aggregating spatial or temporal horizons to acquire compact representations has become a unifying principle in modern deep learning models, yet learning data-adaptive representations for long-horizon sequence data, especially continuous sequences like time series, remains an open challenge. While fixed-size patching has improved scalability and performance, discovering variable-sized,… ▽ More Efficiently aggregating spatial or temporal horizons to acquire compact representations has become a unifying principle in modern deep learning models, yet learning data-adaptive representations for long-horizon sequence data, especially continuous sequences like time series, remains an open challenge. While fixed-size patching has improved scalability and performance, discovering variable-sized, data-driven patches end-to-end often forces models to rely on soft discretization, specific backbones, or heuristic rules. In this work, we propose Reinforcement Patching (ReinPatch), the first framework to jointly optimize a sequence patching policy and its downstream sequence backbone model using reinforcement learning. By formulating patch boundary placement as a discrete decision process optimized via Group Relative Policy Gradient (GRPG), ReinPatch bypasses the need for continuous relaxations and performs dynamic patching policy optimization in a natural manner. Moreover, our method allows strict enforcement of a desired compression rate, freeing the downstream backbone to scale efficiently, and naturally supports multi-level hierarchical modeling. We evaluate ReinPatch on time-series forecasting datasets, where it demonstrates compelling performance compared to state-of-the-art data-driven patching strategies. Furthermore, our detached design allows the patching module to be extracted as a standalone foundation patcher, providing the community with visual and empirical insights into the segmentation behaviors preferred by a purely performance-driven neural patching strategy. △ Less

Submitted 27 March, 2026; originally announced March 2026.

SIMD-PAC-DB: Pretty Performant PAC Privacy

Authors: Ilaria Battiston, Dandan Yuan, Xiaochen Zhu, Peter Boncz

Abstract: This work presents a highly optimized implementation of PAC-DB, a recent and promising database privacy model. We prove that our SIMD-PAC-DB can compute the same privatized answer with just a single query, instead of the 128 stochastic executions against different 50% database sub-samples needed by the original PAC-DB. Our key insight is that every bit of a hashed primary key can be seen to repres… ▽ More This work presents a highly optimized implementation of PAC-DB, a recent and promising database privacy model. We prove that our SIMD-PAC-DB can compute the same privatized answer with just a single query, instead of the 128 stochastic executions against different 50% database sub-samples needed by the original PAC-DB. Our key insight is that every bit of a hashed primary key can be seen to represent membership of such a sub-sample. We present new algorithms for approximate computation of stochastic aggregates based on these hashes, which, thanks to their SIMD-friendliness, run up to 40x faster than scalar equivalents. We release an open-source DuckDB community extension which includes a rewriter that PAC-privatizes arbitrary SQL queries. Our experiments on TPC-H, Clickbench, and SQLStorm evaluate thousands of queries in terms of performance and utility, significantly advancing the ease of use and functionality of privacy-aware data systems in practice. △ Less

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

Machine learning the arrow of time in solid-state spins

Authors: Xiang-Qian Meng, Zhide Lu, Ya-Nan Lu, Xiu-Ying Chang, Yan-Qing Liu, Dong Yuan, Weikang Li, Zheng-Zhi Sun, Pei-Xin Shen, Lu-Ming Duan, Dong-Ling Deng, Pan-Yu Hou

Abstract: Understanding the emergence of the thermodynamic arrow of time in microscopic systems is of fundamental importance, particularly given that unitary evolution preserves time-reversal symmetry. While projective measurements introduce temporal irreversibility, identifying this asymmetry from single evolution trajectories in the presence of stochastic fluctuations presents a considerable challenge. He… ▽ More Understanding the emergence of the thermodynamic arrow of time in microscopic systems is of fundamental importance, particularly given that unitary evolution preserves time-reversal symmetry. While projective measurements introduce temporal irreversibility, identifying this asymmetry from single evolution trajectories in the presence of stochastic fluctuations presents a considerable challenge. Here, we harness machine learning to identify the arrow of time from individual trajectories generated by a programmable ten-qubit quantum processor based on a nitrogen-vacancy center in diamond. We implement quantum circuits that realize unitary evolutions where heat flows from hotter to colder subsystems and their time-reversed counterparts. Projective measurements inserted in these processes induce entropy production, and their outcomes constitute the evolution trajectory. We demonstrate that an unsupervised clustering algorithm autonomously divides the experimental trajectories into two distinct groups without prior knowledge, while a convolutional neural network identifies the temporal direction of these trajectories with approximately 92% accuracy. In addition, we show that a diffusion-based generative model reproduces essential signatures of directional energy flow and entropy production. Our results establish machine learning as a powerful tool for uncovering underlying physical processes from complex experimental data, advancing the interface between quantum thermodynamics and artificial intelligence. △ Less

Submitted 10 March, 2026; originally announced March 2026.

AgentServe: Algorithm-System Co-Design for Efficient Agentic AI Serving on a Consumer-Grade GPU

Authors: Yuning Zhang, Yan Yan, Nan Yang, Dong Yuan

Abstract: Large language models (LLMs) are increasingly deployed as AI agents that operate in short reasoning-action loops, interleaving model computation with external calls. Unlike traditional chat applications, these agentic workloads require inference serving systems to balance low latency, stable token emission, and throughput under multiple request arrivals from different AI agents. Recent deployments… ▽ More Large language models (LLMs) are increasingly deployed as AI agents that operate in short reasoning-action loops, interleaving model computation with external calls. Unlike traditional chat applications, these agentic workloads require inference serving systems to balance low latency, stable token emission, and throughput under multiple request arrivals from different AI agents. Recent deployments highlight a shift toward running small language models (SLMs) locally on consumer-grade GPUs, driven by privacy, compliance, and cost constraints. When heterogeneous requests overlap on a single GPU, long prefills and short decodes contend for resources, creating head-of-line blocking that destabilizes interactive performance. By analyzing agent workloads, we observe that their execution naturally separates into cold prefills, which process long system prompts, resume prefills, which append tool outputs to cached contexts, and short decodes, which are latency-critical. This mix intensifies contention compared to conventional chatbot serving. We present AgentServe, a single-GPU serving system that ensures stable multi-agent execution under such conditions by isolating prefills from decodes, applying dynamic budgeting to resume prefills, and allocating GPU resources through pre-established CUDA Green Context slots with adaptive control. Evaluation results show that AgentServe significantly improves latency stability while sustaining competitive throughput, achieving up to 2.8x TTFT improvement and 2.7x TPOT improvement over state-of-the-art baselines across different settings. △ Less

Submitted 10 March, 2026; originally announced March 2026.

Polarization transfer in $ψ'\toψππ$: a complete spin density matrix analysis framework

Authors: Jiabao Gong, Guanyu Wang, Dongyu Yuan, Libo Liao, Yilun Wang, Jiarong Li, Xiaoshen Kang, Lei Zhang, Jin Zhang, Gang Li

Abstract: A theoretical framework based on the Spin Density Matrix (SDM) formalism is developed to describe polarization transfer in the decay chain $e^+e^- \rightarrow ψ^\prime \rightarrow ψππ$. Explicit relations connecting the SDMs of $ψ^\prime$ and $ψ$ are derived, generalizing Cahn's analysis into a complete SDM treatment. For the dominant $S$-wave $ππ$ emission, the SDM is shown to be perfectly preser… ▽ More A theoretical framework based on the Spin Density Matrix (SDM) formalism is developed to describe polarization transfer in the decay chain $e^+e^- \rightarrow ψ^\prime \rightarrow ψππ$. Explicit relations connecting the SDMs of $ψ^\prime$ and $ψ$ are derived, generalizing Cahn's analysis into a complete SDM treatment. For the dominant $S$-wave $ππ$ emission, the SDM is shown to be perfectly preserved, $ρ_ψ= ρ_{ψ^\prime}$, rendering the $ψ$ an ideal probe of the initial polarization state. Deviations arising from $D$-wave contributions are quantified, and a self-consistency experimental test is proposed that simultaneously validates the framework and constrains partial wave amplitudes. This formalism provides a consistent basis for extracting $ψ$ polarization and for amplitude analyses of subsequent $ψ$ decays in a continuum-background-free environment. The framework extends to other hadronic transitions, including $ψ' \to h_cπ^0$ in charmonium and $Υ(nS) \to Υ(mS)ππ$ in bottomonium, as well as to electroweak processes such as $e^+e^- \to Z^\ast \to ZH$, where the same angular-momentum structure governs polarization transfer -- offering a unified probe of dynamics from charmonium to the Higgs sector. △ Less

Submitted 10 March, 2026; originally announced March 2026.

Comments: 13 pages, 5 figures

Spectral study of the pseudogap in unitary Fermi gases

Authors: Chuping Li, Lin Sun, Kaichao Zhang, Junru Wu, Yuxuan Wu, Dingli Yuan, Pengyi Chen, Qijin Chen

Abstract: The existence of a pseudogap in unitary Fermi gases has recently been established and measured experimentally [Li et al., Nature 626, 288 (2024)]. This lends strong support for the pairing origin as the mechanism of the pseudogap in Fermi superfluids. Here we present a spectral study of unitary Fermi gases, and show how the data can be understood quantitatively, when compared with theoretically ca… ▽ More The existence of a pseudogap in unitary Fermi gases has recently been established and measured experimentally [Li et al., Nature 626, 288 (2024)]. This lends strong support for the pairing origin as the mechanism of the pseudogap in Fermi superfluids. Here we present a spectral study of unitary Fermi gases, and show how the data can be understood quantitatively, when compared with theoretically calculated momentum-resolved rf or microwave spectra, and the pseudogap extracted from the spectra. We use an iterative treatment of the fermion self energy and hence the spectral function, beyond previous pseudogap approximation, based on a pairing fluctuation theory that incorporates both particle-particle and particle-hole T matrices, with self-consistent self energy feedback. Our results not only provide a microscopic explanation of the experimental data but also strengthen the support for both the pairing-induced pseudogap physics and the pairing fluctuation theory of Fermi superfluidity. △ Less

Submitted 7 April, 2026; v1 submitted 6 March, 2026; originally announced March 2026.

Comments: 6 pages, 5 figures

Real-time Motion Segmentation with Event-based Normal Flow

Authors: Sheng Zhong, Zhongyang Ren, Xiya Zhu, Dehao Yuan, Cornelia Fermuller, Yi Zhou

Abstract: Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle visual tasks in challenging scenarios. However, due to the sparse information content in individual events, directly processing the raw event data to solve vision tasks is highly inefficient, which severely limits t… ▽ More Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle visual tasks in challenging scenarios. However, due to the sparse information content in individual events, directly processing the raw event data to solve vision tasks is highly inefficient, which severely limits the applicability of state-of-the-art methods in real-time tasks, such as motion segmentation, a fundamental task for dynamic scene understanding. Incorporating normal flow as an intermediate representation to compress motion information from event clusters within a localized region provides a more effective solution. In this work, we propose a normal flow-based motion segmentation framework for event-based vision. Leveraging the dense normal flow directly learned from event neighborhoods as input, we formulate the motion segmentation task as an energy minimization problem solved via graph cuts, and optimize it iteratively with normal flow clustering and motion model fitting. By using a normal flow-based motion model initialization and fitting method, the proposed system is able to efficiently estimate the motion models of independently moving objects with only a limited number of candidate models, which significantly reduces the computational complexity and ensures real-time performance, achieving nearly a 800x speedup in comparison to the open-source state-of-the-art method. Extensive evaluations on multiple public datasets fully demonstrate the accuracy and efficiency of our framework. △ Less

Submitted 24 February, 2026; originally announced February 2026.

Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation

Authors: Dun Yuan, Hao Zhou, Xue Liu, Hao Chen, Yan Xin, Jianzhong, Zhang

Abstract: Large language models (LLMs) have shown strong potential across a variety of tasks, but their application in the telecom field remains challenging due to domain complexity, evolving standards, and specialized terminology. Therefore, general-domain LLMs may struggle to provide accurate and reliable outputs in this context, leading to increased hallucinations and reduced utility in telecom operation… ▽ More Large language models (LLMs) have shown strong potential across a variety of tasks, but their application in the telecom field remains challenging due to domain complexity, evolving standards, and specialized terminology. Therefore, general-domain LLMs may struggle to provide accurate and reliable outputs in this context, leading to increased hallucinations and reduced utility in telecom operations.To address these limitations, this work introduces KG-RAG-a novel framework that integrates knowledge graphs (KGs) with retrieval-augmented generation (RAG) to enhance LLMs for telecom-specific tasks. In particular, the KG provides a structured representation of domain knowledge derived from telecom standards and technical documents, while RAG enables dynamic retrieval of relevant facts to ground the model's outputs. Such a combination improves factual accuracy, reduces hallucination, and ensures compliance with telecom specifications.Experimental results across benchmark datasets demonstrate that KG-RAG outperforms both LLM-only and standard RAG baselines, e.g., KG-RAG achieves an average accuracy improvement of 14.3% over RAG and 21.6% over LLM-only models. These results highlight KG-RAG's effectiveness in producing accurate, reliable, and explainable outputs in complex telecom scenarios. △ Less

Submitted 19 February, 2026; originally announced February 2026.

OpAgent: Operator Agent for Web Navigation

Authors: Yuyu Guo, Wenjie Yang, Siyuan Yang, Ziyang Liu, Cheng Chen, Yuan Wei, Yun Hu, Yang Huang, Guoliang Hao, Dongsheng Yuan, Jianming Wang, Xin Chen, Hang Yu, Lei Lei, Peng Di

Abstract: To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Conventional paradigms predominantly rely on Supervised Fine-Tuning (SFT) or Offline Reinforcement Learning (RL) using static datasets. However, these methods suffer from severe distributional shifts, as offline trajectories fail to capture the stochastic state… ▽ More To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Conventional paradigms predominantly rely on Supervised Fine-Tuning (SFT) or Offline Reinforcement Learning (RL) using static datasets. However, these methods suffer from severe distributional shifts, as offline trajectories fail to capture the stochastic state transitions and real-time feedback of unconstrained wide web environments. In this paper, we propose a robust Online Reinforcement Learning WebAgent, designed to optimize its policy through direct, iterative interactions with unconstrained wide websites. Our approach comprises three core innovations: 1) Hierarchical Multi-Task Fine-tuning: We curate a comprehensive mixture of datasets categorized by functional primitives -- Planning, Acting, and Grounding -- establishing a Vision-Language Model (VLM) with strong instruction-following capabilities for Web GUI tasks. 2) Online Agentic RL in the Wild: We develop an online interaction environment and fine-tune the VLM using a specialized RL pipeline. We introduce a Hybrid Reward Mechanism that combines a ground-truth-agnostic WebJudge for holistic outcome assessment with a Rule-based Decision Tree (RDT) for progress reward. This system effectively mitigates the credit assignment challenge in long-horizon navigation. Notably, our RL-enhanced model achieves a 38.1\% success rate (pass@5) on WebArena, outperforming all existing monolithic baselines. 3) Operator Agent: We introduce a modular agentic framework, namely \textbf{OpAgent}, orchestrating a Planner, Grounder, Reflector, and Summarizer. This synergy enables robust error recovery and self-correction, elevating the agent's performance to a new State-of-the-Art (SOTA) success rate of \textbf{71.6\%}. △ Less

Submitted 29 April, 2026; v1 submitted 13 February, 2026; originally announced February 2026.

SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents

Authors: Danlong Yuan, Wei Wu, Enhan Zhao, Zhengren Wang, Xueliang Zhao, Huishuai Zhang, Dongyan Zhao

Abstract: Reinforcement learning (RL) has become a key paradigm for training software engineering (SWE) agents, but existing pipelines typically rely on per-task containers for isolation. At scale, pre-built container images incur substantial storage overhead, slow environment setup, and require container-management privileges. We propose SWE-MiniSandbox, a lightweight, container-free method that enables sc… ▽ More Reinforcement learning (RL) has become a key paradigm for training software engineering (SWE) agents, but existing pipelines typically rely on per-task containers for isolation. At scale, pre-built container images incur substantial storage overhead, slow environment setup, and require container-management privileges. We propose SWE-MiniSandbox, a lightweight, container-free method that enables scalable RL training of SWE agents without sacrificing isolation. Instead of relying on per-instance containers, SWE-MiniSandbox executes each task in an isolated workspace backed by kernel-level mechanisms, substantially reducing system overhead. It leverages lightweight environment pre-caching techniques to eliminate the need for bulky container images. As a result, our approach lowers disk usage to approximately 5\% of that required by container-based pipelines and reduces environment preparation time to about 25\% of the container baseline. Empirical results demonstrate that SWE-MiniSandbox achieves evaluation performance comparable to standard container-based pipelines. By removing the dependency on heavy container infrastructure, SWE-MiniSandbox offers a practical and accessible foundation for scaling RL-based SWE agents, particularly in resource-constrained research environments. △ Less

Submitted 30 May, 2026; v1 submitted 10 February, 2026; originally announced February 2026.

Biaxial Strain Control of Helimagnetism via Chemical Expansion in Thin Film SrFeO3

Authors: Jennifer Fowlie, Jiarui Li, Danilo Puggioni, Lucas Barreto, Lin Ding Yuan, James M. Rondinelli, Ronny Sutarto, Teak D. Boyko, Fabio Orlandi, Pascal Manuel, Dmitry Khalyavin, Eder G. Lomeli, Brian Moritz, Thomas P. Devereaux, Skylar Koroluk, Robert J. Green, Steven J. May, Harold Y. Hwang

Abstract: We demonstrate control of helimagnetic order in biaxially strained SrFeO3 thin films using neutron diffraction and resonant soft x-ray scattering. SrFeO3, a negative charge-transfer oxide, exhibits a complex magnetic phase diagram that includes multi-q spin structures. Tensile epitaxial strain produces a pronounced shortening of the helimagnetic ordering length and a tilting of the magnetic orderi… ▽ More We demonstrate control of helimagnetic order in biaxially strained SrFeO3 thin films using neutron diffraction and resonant soft x-ray scattering. SrFeO3, a negative charge-transfer oxide, exhibits a complex magnetic phase diagram that includes multi-q spin structures. Tensile epitaxial strain produces a pronounced shortening of the helimagnetic ordering length and a tilting of the magnetic ordering vector. We interpret this behavior in terms of chemical expansion: lattice dilation under tensile strain lowers the energetic cost of oxygen vacancies, leading to an expanded unit cell that modifies Fe-O hybridization and enhances superexchange relative to double exchange. These results reveal how epitaxial strain can indirectly tune helimagnetism through defect-driven chemical expansion, highlighting the strong coupling between lattice, chemistry, and magnetic order in transition-metal oxides. Our findings establish chemical expansion as an effective mechanism for engineering complex magnetic textures in oxide thin films, with implications for spintronic, magnonic, and quantum information applications. △ Less

Submitted 18 February, 2026; v1 submitted 10 February, 2026; originally announced February 2026.

IFNSO: Iteration-Free Newton-Schulz Orthogonalization

Authors: Chen Hu, Qianxi Zhao, Xiaochen Yuan, Hong Zhang, Ding Yuan, Yanbin Wu, Xiying Li

Abstract: The Newton-Schulz (NS) iteration has become a key technique for orthogonalization in optimizers such as Muon and for optimization on the Stiefel manifold. Despite its effectiveness, the conventional NS iteration incurs significant computational overhead due to repeated high-dimensional matrix multiplications. To overcome these limitations, we propose Iteration-Free Newton-Schulz Orthogonalization… ▽ More The Newton-Schulz (NS) iteration has become a key technique for orthogonalization in optimizers such as Muon and for optimization on the Stiefel manifold. Despite its effectiveness, the conventional NS iteration incurs significant computational overhead due to repeated high-dimensional matrix multiplications. To overcome these limitations, we propose Iteration-Free Newton-Schulz Orthogonalization (IFNSO), a novel framework that consolidates the traditional iterative structure into a unified and Iteration-Free formulation. By analyzing the contribution of individual matrix powers, we streamline the process by removing insignificant terms and introducing a polynomial with learnable coefficients. These coefficients are optimized to ensure both superior computational efficiency and stable convergence. Extensive experiments demonstrate that IFNSO achieves superior performance compared to existing methods. Our code is available at: https://github.com/greekinRoma/Unified_Newton_Schulz_Orthogonalization. △ Less

Submitted 20 March, 2026; v1 submitted 18 January, 2026; originally announced February 2026.

Comments: The paper is under consideration at Pattern Recognition Letters

Nonlinear tails of massive scalar fields around a black hole

Authors: Caiying Shao, Zhen-Tao He, Jiageng Jiao, Jingqi Lai, Jun-Xi Shi, Yu Tian, Dandan Yuan, Hongbao Zhang

Abstract: Nonlinear effects play a fundamental role in the late-time ringdown of black holes, with direct implications for gravitational-wave observations. For massive fields, these dynamics become richer, yet their nonlinear signatures remain poorly understood. Here, we systematically study nonlinear tails of massive scalar perturbations, from a toy model with ingoing and outgoing sources to a self-interac… ▽ More Nonlinear effects play a fundamental role in the late-time ringdown of black holes, with direct implications for gravitational-wave observations. For massive fields, these dynamics become richer, yet their nonlinear signatures remain poorly understood. Here, we systematically study nonlinear tails of massive scalar perturbations, from a toy model with ingoing and outgoing sources to a self-interacting scalar model, revealing nonlinear tails and contrasting the results with their linear counterparts. We find that the nonlinear tails of massive scalar fields, opposite to massless ones, decay as the same rate as linear tails in the intermediate time, independent of source parameters or initial conditions. Nevertheless, quadratic quasinormal modes could serve as a probe to the nonlinear effects of massive fields. △ Less

Submitted 12 April, 2026; v1 submitted 22 January, 2026; originally announced January 2026.

Comments: 10 pages, 6 figures. Version published in PRD

Journal ref: Phys. Rev. D 113, 084016 (2026)

Differential Privacy on Affine Manifolds: Geometrically Confined Privacy in Linear Dynamical Systems

Authors: Zihao Ren, Lei Wang, Deming Yuan, Guodong Shi

Abstract: In this paper, we present a comprehensive framework for differential privacy over affine manifolds and validate its usefulness in the contexts of differentially private cloud-based control and average consensus. We consider differential privacy mechanisms for linear queries when the input data are constrained to lie on affine manifolds, a structural property that is assumed to be available as prio… ▽ More In this paper, we present a comprehensive framework for differential privacy over affine manifolds and validate its usefulness in the contexts of differentially private cloud-based control and average consensus. We consider differential privacy mechanisms for linear queries when the input data are constrained to lie on affine manifolds, a structural property that is assumed to be available as prior knowledge to adversaries. In this setting, the definition of neighborhood adjacency must be formulated with respect to the intrinsic geometry of the manifolds. We demonstrate that such affine-manifold constraints can fundamentally alter the attainable privacy levels relative to the unconstrained case. In particular, we derive necessary and sufficient conditions under which differential privacy can be realized via structured noise injection mechanisms, wherein correlated Gaussian or Laplace noise distributions, rather than i.i.d. perturbations, are calibrated to the dataset. Based on these characterizations, we develop explicit noise calibration procedures that guarantee the tight realization of any prescribed privacy budget with a matching noise magnitude. Finally, we show that the proposed framework admits direct applications to linear dynamical systems ranging from differentially private cloud-based control to privacy-preserving average consensus, all of which naturally involve affine-manifold constraints. The established theoretical results are illustrated through numerical examples. △ Less

Submitted 21 January, 2026; originally announced January 2026.

Hybrid Cloud Architectures for Research Computing: Applications and Use Cases

Authors: Xaver Stiensmeier, Alexander Kanitz, Jan Krüger, Santiago Insua, Adrián Rošinec, Viktória Spišáková, Lukáš Hejtmánek, David Yuan, Gavin Farrell, Jonathan Tedds, Juha Törnroos, Harald Wagener, Alex Sczyrba, Nils Hoffmann, Matej Antol

Abstract: Scientific research increasingly depends on robust and scalable IT infrastructures to support complex computational workflows. With the proliferation of services provided by research infrastructures, NRENs, and commercial cloud providers, researchers must navigate a fragmented ecosystem of computing environments, balancing performance, cost, scalability, and accessibility. Hybrid cloud architectur… ▽ More Scientific research increasingly depends on robust and scalable IT infrastructures to support complex computational workflows. With the proliferation of services provided by research infrastructures, NRENs, and commercial cloud providers, researchers must navigate a fragmented ecosystem of computing environments, balancing performance, cost, scalability, and accessibility. Hybrid cloud architectures offer a compelling solution by integrating multiple computing environments to enhance flexibility, resource efficiency, and access to specialised hardware. This paper provides a comprehensive overview of hybrid cloud deployment models, focusing on grid and cloud platforms (OpenPBS, SLURM, OpenStack, Kubernetes) and workflow management tools (Nextflow, Snakemake, CWL). We explore strategies for federated computing, multi-cloud orchestration, and workload scheduling, addressing key challenges such as interoperability, data security, reproducibility, and network performance. Drawing on implementations from life sciences, as coordinated by the ELIXIR Compute Platform and their integration into a wider EOSC context, we propose a roadmap for accelerating hybrid cloud adoption in research computing, emphasising governance frameworks and technical solutions that can drive sustainable and scalable infrastructure development. △ Less

Submitted 7 January, 2026; originally announced January 2026.

PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback

Authors: Dehao Yuan, Tyler Farnan, Stefan Tesliuc, Doron L Bergman, Yulun Wu, Xiaoyu Liu, Minghui Liu, James Montgomery, Nam H Nguyen, C. Bayan Bruss, Furong Huang

Abstract: Strict privacy regulations limit access to real transaction data, slowing open research in financial AI. Synthetic data can bridge this gap, but existing generators do not jointly achieve behavioral diversity and logical groundedness. Rule-driven simulators rely on hand-crafted workflows and shallow stochasticity, which miss the richness of human behavior. Learning-based generators such as GANs ca… ▽ More Strict privacy regulations limit access to real transaction data, slowing open research in financial AI. Synthetic data can bridge this gap, but existing generators do not jointly achieve behavioral diversity and logical groundedness. Rule-driven simulators rely on hand-crafted workflows and shallow stochasticity, which miss the richness of human behavior. Learning-based generators such as GANs capture correlations yet often violate hard financial constraints and still require training on private data. We introduce PersonaLedger, a generation engine that uses a large language model conditioned on rich user personas to produce diverse transaction streams, coupled with an expert configurable programmatic engine that maintains correctness. The LLM and engine interact in a closed loop: after each event, the engine updates the user state, enforces financial rules, and returns a context aware "nextprompt" that guides the LLM toward feasible next actions. With this engine, we create a public dataset of 30 million transactions from 23,000 users and a benchmark suite with two tasks, illiquidity classification and identity theft segmentation. PersonaLedger offers a realistic, privacy preserving resource that supports rigorous evaluation of forecasting and anomaly detection models. PersonaLedger offers the community a rich, realistic, and privacy preserving resource -- complete with code, rules, and generation logs -- to accelerate innovation in financial AI and enable rigorous, reproducible evaluation. △ Less

Submitted 20 January, 2026; v1 submitted 6 January, 2026; originally announced January 2026.