Robotics
Chunk-wise Model-based Policy Optimization
Weixi Song, Donglin Wang and Yong-Lu Li
Model-based reinforcement learning has shown superior sample efficiency by leveraging the learned latent dynamics either in the form of derivative-free planning/imagined rollout training or dynamics gradient-based policy updates. While these approaches achieve comparatively fast learning, they all suffer, to varying degrees, from issues of recurrent compounding errors, high inference-time latency, and instability in backward propagation through time (BPTT). In this work, we propose a model-based RL approach that uses the chunk world model as a differentiable training-time evaluator to optimize the chunk actor without recurrent model invocation. Through the collaborative chunk-wise prediction of the world model and policy, we can generate H-step hypothetical rollouts in a single forward pass and directly optimize value expansion, avoiding the costly and unstable BPTT. We evaluate our method on 19 challenging high-dimensional humanoid tasks from HumanoidBench and deploy our method to the real-world for quadruped bipedal walking. Project Website: https://song-wx.github.io/cmpo.github.io/
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Robotics
From Video to Vision: Learning Generalizable Humanoid Carrying from Human Motion
Ziluo Ding
Most existing approaches rely on
video or motion-capture data for reinforcement learning (RL) training; however, all of these methods require an upper-level module to generate reference trajectories in real time. In object carrying tasks, the humanoid can only lift objects from designated locations along the reference trajectory, resulting in poor spatial generalization. Moreover, existing policies can only be evaluated inside a motion-capture studio. To address these limitations, we propose MOVER, a multi-stage, Teacher-Assistant-Student, RL training framework for humanoid object carrying. We first collect data from
videos and employ GENMO and GMR to retarget the
video data, combining this with data augmentation techniques to generalize the data across objects of varying shapes and sizes. In the first stage, building upon the DeepMimic \cite{peng2018deepmimic} motion-training framework, we train the humanoid to transport objects from specified locations along designated trajectories. In the second stage, we relax the tracking of the reference motion and adjust the reward so that the humanoid learns to transport objects from different positions. In the third stage, we apply a Dagger-PPO
distillation method to distill away the proprioceptive position observations and reference joint position of the humanoid. This allows the deployed policy to operate without relying on global position information from a motion-capture system; instead, the humanoid can directly perceive the object's pose from its onboard RGB-D camera to complete the carrying task. We design a series of simulation experiments to compare the success rates across stages (Stages 1 vs. 2) and before versus after
distillation. We further transfer the policy to a real humanoid, successfully transporting objects at varying positions in a motion-capture-free setting.
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Machine Learning
PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging
Muqing Liu, Chongjie Si, Zhuoya Liu et al. (+1 more)
Large pretrained models have achieved remarkable success across diverse domains, yet adapting them to multiple downstream tasks remains challenging: full fine-tuning is costly, while maintaining separate task-specific models is storage-inefficient and fails to produce a unified multi-task model. Low-Rank Adaptation (LoRA) offers an efficient alternative for Model Merging by representing each task adaptation as a low-rank update over frozen pretrained weights. However, existing LoRA merging methods often treat task-specific updates as directly comparable objects and merge them in their original low-rank parameter spaces. This overlooks two key factors: which update directions provide reliable shared support across tasks, and how these directions should be re-parameterized with respect to the pretrained weights before merging. To address this limitation, we propose Pretrained-Guided Shared Basis (PGSB), a two-stage framework for LoRA model merging. PGSB first identifies shared directions among task-specific LoRA updates, and then re-parameterizes these directions using their interaction with the pretrained weights, producing a more suitable basis for merging. Extensive experiments demonstrate that PGSB consistently outperforms state-of-the-art baselines across multiple
benchmarks. Codes are available at https://github.com/Kitschnada/Pretrained-Basis .
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All authors Muqing Liu, Chongjie Si, Zhuoya Liu, Yuheng Jia
Computer Vision
Adversarial Gradient Forcing for Robust Few-Step Autoregressive Video Generation
Chenxi Xia, Yixuan Xu, Shan Liu et al. (+7 more)
Autoregressive
video generation models generate
videos sequentially, with each new block conditioned on previously generated blocks. By training on the model's own rollouts, Self Forcing and its follow-up methods substantially mitigate exposure bias caused by the mismatch between training- and inference-time history distributions. However, these models still suffer from error accumulation during long-horizon generation. We identify a key underlying limitation: the model remains highly sensitive to errors in its generated history, allowing small deviations to be amplified through subsequent generations. We refer to this underexplored problem as the local history robustness gap. To address this gap, we introduce
Adversarial Gradient Forcing (AGF), a framework designed to improve the robustness of few-step autoregressive
video models to perturbations and accumulated errors in their own generated histories. AGF uses distribution matching gradients from future block predictions to select
adversarial perturbations of self-generated histories. It then jointly trains the generator on the original and perturbed histories to improve the robustness of future generation. Extensive experiments show that AGF achieves the highest VBench Total score on 60-second
video generation and the best visual quality on four-minute
video generation. Notably, AGF achieves such long-horizon robustness while requiring only 5-second rollouts for training.
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All authors Chenxi Xia, Yixuan Xu, Shan Liu, Jiapeng Wang, Maohua Li, Hanlin Tang, Kan Liu, Tao Lan, Weiyi Lu, Xinggao Liu
Software Engineering
LASER: Defeasible Certificate Search for Long-Range Protocol Dependencies
Zihan Tang and Huibiao Zhu
Protocol specifications rarely justify executable behavior in one place: a transition may inherit its source state from a remote control block, name its events only by number, and remain subject to a later correction. Recovering the nearby clause is therefore insufficient; the system must assemble a source-linked certificate and determine whether any stronger rule applies. We introduce
LASER, a proof-carrying search method for sparse, defeasible protocol evidence.
LASER represents missing support and unresolved corrections as explicit obligations, searches typed evidence routes, and lets an occurrence-aware symbolic checker alone decide whether a certificate warrants, refutes, or leaves a behavior unresolved. On 91 BehaviorCheck families under a matched 2K budget,
LASER improves Joint Edge+Proof over the strongest local reader on two model families ($\Delta=+.132$, $p=.017$; $\Delta=+.198$, $p<.001$) while cutting false discoveries by 2–3$\times$. Controlled replays attribute the gain and its limits: neither reference routing nor deterministic citation expansion alone exceeds the local baseline; whole-document reading (29–65K
tokens), two-round iterative
retrieval, and even oracle minimal-certificate evidence all fail to reach
LASER, localizing the bottleneck in typed obligation closure rather than evidence access. An authority-corruption replay shows source-link errors only cause abstention, while the only unsafe-warrant mechanism is mis-recording a Held erratum as Verified, confined to that erratum's family.
LASER thus improves long-range dependency recovery while converting incomplete evidence and uncertain authority into explicit residual obligations rather than unsupported behavior.
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Robotics
Before We Act: Learning Action-Conditioned Team-Response Beliefs for Decentralized Multi-Robot Manipulation
Zihao Li, Yanan Zhou, Ranpeng Qiu et al. (+2 more)
In multi-
robot manipulation, an action's value depends on how teammates respond. We present \method{} (Cooperative Action–Response Belief Estimation), a framework for learning team-response beliefs from local history. A predictive belief encoder represents the interaction and conditions a nominal action. Matched reactive and replay continuations separate each candidate's direct advantage from the utility contributed by teammate adaptation. A candidate-conditioned readout learns this response distribution alongside total advantage, and calibrated selection chooses an alternative or retains the nominal action. Our analysis characterizes when response changes the preferred action and how prediction and source errors limit the recoverable gain. With the representation, proposals, and data fixed, explicit response supervision improves candidate ranking and reduces selection regret relative to learning the same information through branch utilities. The benefit is concentrated in decisions where teammate adaptation changes the preferred action. Across RoboFactory, the MARS Control Track, and physical collaboration tasks, CARE achieves the highest average success among the evaluated policies, combining a capable nominal policy with selective coordination adjustments. Together, these results show that CARE turns predictions of teammate adaptation into effective decentralized action selection. Project page: https://jeong-zju.github.io/care/.
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All authors Zihao Li, Yanan Zhou, Ranpeng Qiu, Guoqiang Ren, Weiming Zhi
Information Retrieval
LLM-based User-Product Relevance Evaluation for Dynamic Product Advertising
Wen Zhang, Xiao Bai, Hsiang-wei Chao et al. (+9 more)
Dynamic Product Advertising (DPA)
retrieval systems are a core component of modern e-commerce advertising; however, their evaluation remains challenging. Conventional offline engagement metrics are limited by sparse, biased feedback, while online evaluation is costly and time-consuming. In this work, we propose a user–product relevance metric for offline evaluation of DPA
retrieval systems. The metric first compresses the engagement history into a compact set of intent representatives via Semantic-ID-grouped, event-stratified segmentation, then assesses the relevance of a recommended product to each individual intent and aggregates them as a user-level relevance score, yielding a stable and interpretable measure of
retrieval quality. To compute this metric at scale, we leverage large
language models (LLMs) as semantic relevance judges, capturing nuanced functional and semantic relationships that heuristic methods often miss. We propose an agentic, human-aligned prompting framework that generates multi-category relevance guidelines and distills teacher-model judgments into an efficient student model for scalable evaluation. The resulting metric strongly correlates with offline and online business metrics, enabling faster and more reliable DPA
retrieval iteration before A/B testing.
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All authors Wen Zhang, Xiao Bai, Hsiang-wei Chao, Mengfan Zhu, Tong Zhao, Mingxuan Ju, Xiaodong Liu, Congfei Zhang, Yunzhi Zhou, Yajun Wang, Jinchao Li, Yu Zhang
Machine Learning
Active Neural Matter: A Finite-Window Response Framework for Physical Systems and Distributed AI Workflows
Tianchi Chen and Shihua Yu
Physical systems and distributed AI workflows can retain past information, receive delayed feedback and combine conflicting signals. These features pose a common modeling problem: determining what information about past interactions a state must retain to account for future responses. Active Neural Matter (ANM) addresses this problem through a finite-window response framework. Forward analysis uses each system's dynamics to predict state and output responses to perturbations, separating propagation from output sensitivity. Inverse analysis tests whether a candidate state retains enough information for these responses. With the system and future inputs or interaction rules fixed, distinguishable responses from histories assigned the same state identify distinctions the representation must preserve. Their distributional separation gives a lower bound on the worst-case error of approximating behavior from that state. Uncertainty bounds distinguish rejection, support within a fixed tolerance on tested comparisons, and unresolved cases. Delayed-consensus simulations check analytical predictions of response amplitude and phase over a narrow range of delays; flocking illustrates dependence on the chosen observable. An exact delayed-return construction establishes a sufficient state for a specified program. The AI studies identify distinctions in interaction history and evidence order that a representation must preserve to reproduce observed response distributions. Tests on new histories in a controlled feedback task provide bounded support for a state specified by the task instructions; the instructed rule predicts every observed action. ANM connects response prediction to measurable information requirements, separating behavioral fidelity from task correctness and initial-state adequacy from a closed
evolution law for that state.
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Distributed Computing
A Self-Defining Operator for Distributed Applications
Shihang Li, Thomas Anderson, Ratul Mahajan et al. (+2 more)
To operate distributed applications with high availability, operators must rapidly diagnose and mitigate any incident. LLM-based
agents can help, but unlike experienced human operators they do not improve as they handle more incidents. Each investigation starts from scratch, re-deriving likely causes and re-testing hypotheses even when a similar fault was resolved in the past. We present a self-defining operator (SDO) that improves with time by updating its operational behavior and
memory. When SDO resolves an incident, it distills that experience into playbooks, which describe reusable diagnostic instructions for future incidents with similar faults, and detectors, scripts that take application telemetry as input and output relevant playbook(s). SDO continuously runs the detectors and invokes LLMs only when one fires, so its
token cost scales with incident count rather than with operational time. Across 11 open-source microservice applications, SDO cuts resolution time by 52% on recurring faults while keeping per-incident cost low. SDO is open source at https://github.com/self-defining-systems/sdo.
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All authors Shihang Li, Thomas Anderson, Ratul Mahajan, Simon Peter, Luke Zettlemoyer
Information Theory
DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments
Bohao Wang, Tianen Yuan, William Yen et al. (+12 more)
Large-scale
multimodal data are a key enabler for integrating large generative artificial intelligence models into wireless networks. However, existing wireless
datasets offer limited support for heterogeneous environments, particularly in terms of reusable high-fidelity scenes, scalable differentiable ray tracing, and synchronized
multimodal data. These limitations are more pronounced in mobile and aerial scenarios with time-varying environments and channels. To address these limitations, we propose DeepTelecom, a material-aware, high-fidelity
multimodal channel
dataset and reproducible generation framework for networks involving unmanned aerial vehicles (
UAVs) and reconfigurable intelligent surfaces. DeepTelecom refines coarse indoor/outdoor reconstructions and uses a large
language model to map segmented surfaces to frequency-dependent electromagnetic parameters, yielding material-aware Level of Detail 3 digital twins. Then, a differentiable GPU-accelerated Sionna ray tracer simulates propagation across the sub-6 GHz/millimeter-wave/sub-terahertz bands, and specifically captures rotor-induced micro-Doppler signatures of stationary or moving
UAVs at different pitch angles and translational velocities. The framework synchronously produces channel tensors, coverage maps, transmission
videos, multi-view images, and weather-conditioned renderings, and supports differentiable material calibration from sparse channel anchors. The released
dataset contains 10 material-aware indoor/outdoor scenes and 50,000 synchronized
UAV samples; the framework supports scalable generation of additional data for user-defined scenes and system configurations. In 3D
UAV localization, DeepTelecom reduces the 90th-percentile error from 31.05 m to 2.43 m compared with schemes considering only channel state information, demonstrating its significance for artificial-intelligence-enabled wireless systems.
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All authors Bohao Wang, Tianen Yuan, William Yen, Zehua Jiang, Chongwen Huang, Li Wei, Chen Zhu, Jiguang He, Hang Zou, Lina Bariah, Brahim Mefgouda, Anis Bara, Yongliang Shen, Zhaoyang Zhang, Merouane Debbah
Computer Vision
InfraWorld: Motion-Aware World Modeling for Roadside 4D Occupancy Forecasting
Songkai Wang, Xiaokai Bai, Lei Yang et al. (+1 more)
Roadside 4D occupancy forecasting predicts future semantic occupancy in a fixed infrastructure coordinate system. Roadside scenes are globally structured yet locally evolving, requiring comprehensive scene-level context while precisely modeling sparse localized motion. However, existing forecasting paradigms struggle to tackle both requirements. To address this limitation, we propose InfraWorld, a world model formulating roadside occupancy forecasting as selective state
evolution. InfraWorld maintains a dense BEV state for the complete scene and persistent motion queries for moving regions, assigning complementary states to global context and local dynamics. Specifically, we propose Dynamic Query Initiation (DQI) to initialize queries in regions supported by motion in the observed history. During rollout, the dense state is updated through Long–Short Temporal Fusion (LST), which combines short-term adjacent-frame continuity with long-term motion trends to preserve stable context while capturing scene
evolution. Motion-Aware Query Injection (MAQI) then refines the queries and injects confidence-weighted query information into corresponding BEV
tokens, enabling targeted dynamic updates without perturbing global context. Together, InfraWorld concentrates predictive capacity on evolving traffic regions. Extensive experiments on the InfraOcc and Occ3D–nus
datasets demonstrate that InfraWorld achieves state-of-the-art performance for roadside 4D occupancy forecasting while retaining its effectiveness under ego-centric settings. Code will be released.
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All authors Songkai Wang, Xiaokai Bai, Lei Yang, Hui-liang Shen
Distributed Computing
Byzantine-linearizable Single-Writer Multi-Reader Register in the GPU-Inspired Shared Memory Model
Chryssis Georgiou, Sathya Peri, Manaswani Piduguralla et al. (+1 more)
The construction of fault-tolerant atomic registers is a fundamental problem in distributed computing. GPUs, widely used in scientific computing and machine learning, face Byzantine thread behaviour, while their shared
memory lacks access control, allowing any thread to modify shared objects. This raises the question of whether atomic register semantics can be maintained when any thread, including the designated writer, may behave Byzantine. We study this problem in the GPU-inspired shared-
memory model of Georgiou et al. and present implementations of a Byzantine-linearizable single-writer multi-reader (SWMR) register. CAReg uses constant
memory with a quantifiable probability of invalid reads, while SnapObject maintains a bounded window of authenticated writes, eliminating invalid reads and tolerating up to \(n-1\) Byzantine processes through lock-free and wait-free variants. Our results demonstrate how GPU phase-based, lock-step execution can partially compensate for the absence of access control.
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All authors Chryssis Georgiou, Sathya Peri, Manaswani Piduguralla, Yashi Rastogi
Artificial Intelligence
Are Multimodal Benchmarks Really Useful? Item-Level Multimodal Benchmark Diagnosis via Structure-Response Co-Calibration
Shiqi Zhang, Weixin Zeng, Ziheng Zhang et al. (+2 more)
Multimodal benchmarks are central to evaluating whether models can reason over information from multiple modalities. However, existing literature has revealed that some
benchmarks may contain shortcut items that can be answered from a single modality or from answer-option regularities alone, thus failing to reflect models' true cross-modal inference ability. Existing methods for assessing
benchmarks typically rely either on intrinsic item structure or extrinsic model response behavior, which are not adequate for a comprehensive diagnosis. To fill in this gap, we propose SRCoD, a structure-response co-calibrated diagnosis framework for item-level diagnosis of modality dependence in
multimodal benchmarks. SRCoD estimates sample-wise
multimodal information structure from
benchmark content and target answers, and uses this signal as a soft structural anchor for modality-conditioned item response modeling. The learned model produces a calibrated item-level diagnostic profile that reflects how strongly an item depends on different forms of modality evidence, enabling fine-grained
benchmark analysis beyond aggregate accuracy. SRCoD also provides interpretable attribution of the diagnosis results. Extensive experiments under two complementary protocols, i.e.,
benchmark-refinement oriented validation and human-labeled item validation, demonstrate that SRCoD outperforms state-of-the-art
benchmark diagnosis methods.
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All authors Shiqi Zhang, Weixin Zeng, Ziheng Zhang, Jiuyang Tang, Xiang Zhao
Robotics
GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models
Shaohao Zhu, Wensi Huang, Siqi Zhang et al. (+6 more)
Emerging strong
Vision-
Language Models (VLMs) have advanced navigation foundation models. However, these models still have unsatisfactory performance on goal-oriented navigation settings. In this paper, we first conduct a motivating study revealing that the key issue lies in the insufficient utilization of streaming observations and imprecise target localization. To tackle these challenges, we propose GG-Nav, a simple yet effective navigation framework that learns a structured chain-of-thought (CoT) mechanism via supervised fine-tuning to explicitly localize goal-relevant instances, accumulate grounding evidence, and support more reliable decision-making. Specifically, the structured CoT is designed with two key stages: (i) Observation-to-Evidence, which reasons over all intermediate frames between adjacent decision steps to retain useful goal-related evidence; and (ii) Evidence-to-Decision, which integrates the current observation with accumulated evidence to ground the target and support the generation of navigation actions or clarification questions. Extensive experiments show that GG-Nav outperforms the state-of-the-art methods in terms of success rates on the HM3D ObjectNav, VL-LN, and CoIN
benchmarks by 3.1%, 16.6%, and 5.3%, respectively. Ablation studies further show that accumulating evidence from intermediate observations reduces evidence-missing failures by 11.6%, while explicit grounding reduces stopping errors by 49.4%. We also deploy GG-Nav on a Unitree Go2
robot, demonstrating its zero-shot generalization capability in unseen real-world environments.
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All authors Shaohao Zhu, Wensi Huang, Siqi Zhang, Yuqiang Yang, Meng Wei, Chenyang Wan, Tianle Liu, Tai Wang, Jinming Xu
Artificial Intelligence
FDE-Bench: Evaluating End-to-End Delivery from Underspecified Real-World Business Requests
Huaiming Li, Can Huang, Chufan Wu et al. (+18 more)
Real-world business requests often leave key requirements unstated because frontline practitioners struggle to articulate rules they know from experience. Forward-deployed engineers (FDEs) must elicit these requirements through dialogue and iteratively optimize solutions until they meet customer acceptance criteria. Existing optimization
benchmarks typically start from explicit objectives, while clarification
benchmarks often introduce information gaps into existing tasks. We introduce FDE-Bench, the first
benchmark built from real customer projects to evaluate
agents on end-to-end delivery tasks that require interactive requirement clarification and iterative optimization. Its 49 cases span combinatorial optimization and machine learning. Clarification targets are distilled from discussions between frontline practitioners and FDEs.
Agents elicit missing requirements from a simulated customer, and case-specific evaluators assess final artifacts against acceptance criteria using customer-accepted FDE solutions as quality references. Across 18
agent configurations, the highest mean delivery score is only 0.5510, compared with an FDE reference of 1.0. The highest delivery success rate (DSR), defined as the fraction of planned runs reaching this reference, is only 6.12%. These results show that reliable FDE-level end-to-end delivery remains out of reach for the evaluated
agents on underspecified real-world business tasks. The
benchmark project page is available at https://baidubce.github.io/FM-
Agent/FDE-Bench/.
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All authors Huaiming Li, Can Huang, Chufan Wu, Danyu Liu, Xiaomin Yuan, Qinze Li, Yui Lo, Wenhui Bai, Qianlong Wang, Zengle Ge, Xiyu Yan, Yuheng Cheng, Jinhui Ren, Guokai Chen, Jiaqun Liu, Haoran Li, Qianhui Liu, Annan Li, Jianmin Wu, Dawei Yin, Dou Shen
Digital Libraries
A Stateful Research Information Infrastructure for Scientific Discovery
Lingyuan Kong and Zhengran Wu
Artificial intelligence (AI) is being applied to an expanding range of scientific activities, including literature analysis, hypothesis generation, software development and automated experimentation. Yet these capabilities rely on a more fundamental condition: scientific information must remain identifiable and traceable across systems and over time. In practice, different versions of a work and their associated records and files are often scattered across services and local directories. This fragmentation forces researchers to identify and contextualize the same work repeatedly, disrupting continuity from discovery to interpretation and reuse. Here we present Gewu Insight, a research information infrastructure that addresses this discontinuity by organizing scientific works as persistent, stateful research objects. It unifies venue-, topic- and author-based discovery records through a shared paper identity. Within a local library, metadata, versioned resources and research context can then accumulate around the same work. These capabilities are integrated into an iterative workflow for scientist-led, AI-assisted research, allowing earlier interpretations to be revisited and refined. A bounded evaluation showed high candidate precision across the three discovery modes, with paper identity, resource distinctions and research state preserved in controlled workflows. By making continuity an explicit property of research infrastructure, Gewu Insight can serve as an inspectable scientific
memory layer shared by researchers and AI assistants, supporting human judgment and providing a foundation for future, more epistemically grounded scientific AI.
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Machine Learning · Cross-listed from physics.acc-ph
Learn the Dynamics, Enforce the Physics: Generative Latent Operators for High-Dimensional Beam Evolution
Haohan Zou, Alexander Scheinker and Yuanyuan Shi
Particle-in-
cell (PIC) simulation is indispensable for modeling beam dynamics in particle accelerators, but its computational cost limits many-query workflows, such as configuration exploration, virtual diagnostics, and controller design. We introduce GLOBE, a physics-constrained Generative Latent Operator for Beam
Evolution, modeling the nonlinear, control-dependent, multiscale
evolution of 3D beam–field dynamics covering $10^7$ spatial
cells. GLOBE compresses beam–field states into a compact latent space and factors their
evolution into two components. A control-conditioned neural operator learns the dominant dynamics, while an amplitude-normalized generative model captures the residual distribution, recovering fine-scale structures and conditional variations. GLOBE enforces field constraints by regime-specific, parameter-free operators: a Poisson solve reconstructs the electric field in the electrostatic regime, and a spectral projection imposes Gauss's law and a divergence-free
magnetic field in the electromagnetic regime. On two high-resolution 3D particle-accelerator
benchmarks, bunched-beam transport and
plasma-wakefield acceleration, GLOBE lowers relative-$L_2$ error by 9% and 13%, low-frequency spectral error by 69% and 51%, and mid-frequency spectral error by 54% and 20% relative to the strongest baseline (DPOT), satisfies field constraints, and reduces hours of PIC simulation to a minute per rollout.
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Formal Languages and Automata · Cross-listed from math.GR
Twisted Equations and Palindrome Length
Volker Diekert, Anna E. Frid, Silas Natterer et al. (+1 more)
Twisted equations in free groups are equations in which a finite group of
symmetries acts on the free group by automorphisms and anti-automorphisms, such as inversion and reversal; moreover, the variables may be subject to rational constraints.
Algorithms from the theory of equations in virtually free groups solve such equations in PSPACE, but they are intricate. Our main result is an elementary
algorithm for quadratic twisted equations, which extends the
algorithm of Comerford and Edmunds (1981) for quadratic equations in free groups to the twisted setting and to rational constraints. Besides deciding solvability in PSPACE and yielding EDT0L descriptions of solution sets, our
algorithm computes, for every quadratic expression, finitely many parametric forms describing its values as freely reduced words. As an application, we study the palindrome length of elements of free groups, that is, the least number of palindromes whose product they are. Guided by the parametric forms that our
algorithm computes for products of palindromes, we found, and then proved directly, a characterization of the freely reduced words of palindrome length at most $n$ in terms of patterns describing how palindromes are nested by twisted conjugations, thereby confirming a Catalan structure conjectured by the second author. Consequently, these words form a context-free
language, and palindrome length is computable in cubic time as well as in LOGCFL, answering a question of Bardakov, Shpilrain, and Tolstykh (2005).
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All authors Volker Diekert, Anna E. Frid, Silas Natterer, Alexander Thumm
Operating Systems
GMM: Generalized OS Memory Management for Programmable XPUs
Weixi Zhu, Bin Wang, Zhiyuan Fang et al. (+5 more)
Since the AI boom, the fast evolving programmable XPU devices utilize high-bandwidth local
memory to accelerate domain-specific tasks offloaded from the host CPU, resulting in a proliferation of XPU
memory management systems implemented in proprietary drivers. However, existing OS core
memory management (MM) continues to lack a high-level software abstraction, excluding the XPUs from leveraging OS advanced
memory features. The XPU manufacturers have to reimplement independent XPU MM drivers, exacerbating the fragmented XPU software ecosystem. This paper presents GMM, generalized
memory management that provides unified MM subsystems for XPUs. By adding an explicit logical mapping abstraction, GMM 1) provides universal NUMA-aware
memory API for XPUs; 2) allows ease of XPU MM delegation; 3) enables sharing of advanced XPU
memory features. Lightweight XPU MM drivers are implemented for two representative XPU manufacturers, Nvidia and Huawei. Both GMM-based drivers obtain competitive performance compared with their vendor-supplied drivers. GMM also obtains up to 41% higher fault-driven page migration throughput. Two real-world industrial case studies are exercised, where GMM reduces XPU
memory utilization by 10% with up to 22.6x faster inference serving switch latency.
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All authors Weixi Zhu, Bin Wang, Zhiyuan Fang, Jiahua Wang, Yao Guo, Gingfung Yeung, Adam Barker, Xinwei Hu
Computation and Language
LongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding
Zeyu Shan, Yongxin Shi, Shihang Wu et al. (+4 more)
Chinese historical documents are invaluable cultural treasures, but their complexity poses significant comprehension challenges for non-specialists. Despite the success of Large
Language Models (LLMs) and Large
Vision-
Language Models (LVLMs) in modern document tasks, their capabilities in Long (full-book) Historical Document understanding remain largely unexplored. To bridge this gap, we introduce LongHisDoc, the first
benchmark tailored for long historical document understanding. LongHisDoc comprises 1,012 expert-annotated QA pairs derived from 101 PDF documents, with an average of 85.1 pages and 42,776.4
tokens per document. It spans 10 categories and features four QA types: single-page, cross-page, multi-hop, and unanswerable. Answering these questions requires integrating
multimodal evidence from text, layout, and visual elements. Our evaluation reveals that the top-performing LVLM and LLM achieve only 62.9% and 69.9% accuracy, respectively, highlighting a substantial performance gap. Furthermore, we assess various modal retrievers and the end-to-end RAG performance to provide references for
Retrieval-Augmented Generation (RAG) systems. We believe that LongHisDoc serves as a comprehensive
benchmark to catalyze future research in full-book historical document understanding. The
benchmark and evaluation code are available at https://huggingface.co/
datasets/qweq12433454/LongHisDoc and https://github.com/SCUT-DLVCLab/LongHisDoc.
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All authors Zeyu Shan, Yongxin Shi, Shihang Wu, Peirong Zhang, Yuanyuan Fang, Zhaopeng Wang, Lianwen Jin
Robotics
Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation
Ruiyang Wang, Hao-Lun Hsu, Haocheng Meng et al. (+2 more)
Multi-
robot systems (MRS) offer significant advantages over single-
robot platforms by enabling information sharing and coordination for different tasks in complex environments. However, reliable cooperation critically depends on inferring trust over both information sources and their reported hypotheses, particularly under partial observability and potential
adversarial interference. Existing approaches frequently depend on pre-established trust relationships or side-channel information, which may not be available or reliable in realistic MRS deployments. This work introduces NeST-Bayes, a neural-symbolic framework that jointly infers
robot and hypothesis trust using only inter-
robot observation consistency, without requiring preassigned
robot trust, reputation information, or side-channel identity signals. NeST-Bayes infers instantaneous trust evidence scores for
robots and object hypotheses by
reasoning over ego-centric relational graphs constructed from cross-
robot observation consistency. These evidence scores are accumulated over time through
Bayesian updates of Beta-distributed trust beliefs to maintain temporally consistent trust estimates. Extensive evaluations show that NeST-Bayes achieves the highest mean object precision and
robot recall across all
benchmark settings, and the highest
robot precision and object recall in most settings, compared to trust-based methods. Under aggressive optimized attacks, it achieves 98.8%
robot precision with 93.8% recall and 98.3% object precision with 95.4% recall. In an occlusion-rich Webots warehouse, it maintains 92.5%
robot precision with 92.0% recall and 97.3% object precision with 85.0% recall. These results demonstrate joint improvements in the reliability and coverage of retained information.
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All authors Ruiyang Wang, Hao-Lun Hsu, Haocheng Meng, Songqiao Xie, Miroslav Pajic
Artificial Intelligence
Energy Ring: Stabilizing Feature Energy for Reliable Test-Time Discovery
Tianle Liu, Fan Lyu, Tingliang Feng et al. (+6 more)
Test-Time Discovery (TTD) aims to recognize known classes and detect novel ones while the model is already deployed, operating directly on a continual unlabeled test-time stream. Existing TTD methods are overly conservative, making known and unknown features collapse together and difficult to separate during test-time adaptation. In this paper, we revisit TTD from an energy perspective and find that the natural energy gap between known and unknown classes is unstable and easily collapses. We therefore propose Energy Ring to stabilize this gap by anchoring the two types of classes on separate energy rings. A lightweight adapter enlarges the norm-based gap while preserving known-class directions, and a perturbation consistency module forms compact clusters for unknown classes. During testing, energy-guided confidence weighting improves prediction, and a stability-based rectification mechanism corrects unreliable updates. Experimental results show that Energy Ring significantly improves TTD performance against existing TTD methods.
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All authors Tianle Liu, Fan Lyu, Tingliang Feng, Chenggong Ni, Tianqi Wang, Zhang Zhang, Fuyuan Hu, Rui Yao, Tao Zhou
Artificial Intelligence
ScienceBuddy-PV: A Verifier-Governed Recursive Improvement Protocol for Panvascular Intervention Research
Lingsen You, Yujun Guo, Xinyu Zhong et al. (+4 more)
ScienceBuddy-PV adapts the ScienceBuddy recursive-in-recursive design to panvascular intervention research through a structured evidence ledger, device-vessel ontology, abstention contract and retained failure bank. Recursive harness optimization and outer-loop model learning, including reinforcement learning, are specified but not executed. We audit a fixed component, rather than the complete
agent system, using separate ARCADE syntax and stenosis tasks (1,000 training, 200 validation and 300 test images per task). A shared task-masked convolutional model was trained once per representation setting; its parameters and the heuristic support gate were then held fixed, with the stenosis threshold selected on validation data. On the locked test sets, syntax Dice was 0.475 (95% CI 0.459-0.490) and stenosis Dice was 0.231 (95% CI 0.214-0.249). Stenosis pixel AUROC was 0.957 and AUPRC was 0.180 at prevalence 0.00975. The gate accepted 76.7% of stenosis-frame outputs versus 100% for a score-only comparator. In a constructed mask audit, it accepted 44.3% of reference candidates and rejected 55.7%, exposing the cost of abstention. Acceptance does not establish correctness or clinical safety. This public-data component audit makes support and abstention measurable and provides a reproducible protocol for future collaborative
agent studies. It does not demonstrate recursive improvement, patient-level validity or clinical device recommendations.
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All authors Lingsen You, Yujun Guo, Xinyu Zhong, Zijie Feng, Wentong Wang, Li Shen, Junbo Ge
Robotics
Safety-Critical Quadrotor Navigation Using Fourth-Order Control Barrier Functions with Full Nonlinear Quadrotor Dynamics
Yanze Liu, Yaşar İdikut, Dhruv Madhusudan Agrawal et al. (+2 more)
Safety-critical quadrotor navigation in cluttered environments typically relies on hierarchical architectures in which a high-level safety filter modifies position references that are then tracked by a separate attitude controller. However, this separation can create a mismatch between translational safety objectives and attitude
evolution. To address this gap, we propose a safety controller based on fourth-order control barrier functions (CBFs) that explicitly incorporates the full nonlinear quadrotor dynamics, including attitude dynamics and torque-level actuation. Furthermore, we apply the integral high-order CBF technique to resolve the mixed-relative degree issue in the derived constraint formulation. The resulting formulation enforces obstacle-avoidance constraints while optimizing control torques, granting the safety layer direct authority over attitude behavior and eliminating the hierarchical disconnect inherent in reference-tracking safety filters. We validate the approaches in both high-fidelity simulation and real-world flight experiments, demonstrating reliable constraint satisfaction and successful obstacle avoidance. These results suggest that torque-level high-order CBFs provide a practical pathway to provably safer quadrotor navigation under full rigid-body dynamics.
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All authors Yanze Liu, Yaşar İdikut, Dhruv Madhusudan Agrawal, Wei Xiao, Guanrui Li
Machine Learning · Cross-listed from stat.ML
Identication and Estimation of Nonsymmetric Determinantal Point Processes
Christian Gouriéroux and Yang Lu
Determinantal point processes (DPPs) provide tractable distributions for nite random sets, but the usual symmetric-kernel specication imposes nonpositive pairwise dependence. Statistical research on nonsymmetric DPPs, which can accommodate positive dependence, remains limited. We study identication and estimation for this class. First, we establish the equivalence of two sucient conditions for a nonsymmetric kernel to dene a valid DPP. Second, we develop the identication theory for nonsymmetric DPPs. Third, we develop cardinality-truncated maximum-likelihood estimation and estimation by averaged
stochastic gradient descent. Under suitable identication and regularity conditions, we establish consistency and asymptotic normality for both
estimators. Simulations examine identication, the eects of truncation, and the nite-sample performance of the
estimators. We illustrate the methods with two cyber applications at markedly dierent sampling frequencies: weekly sector-by-breach data and second-by-second enterprise authentication data with more than ve million set observations.
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Machine Learning
Query-Adaptive Neuron Steering for LLMs
Yanxi Pan, Jingwen Wu, Yan Ma et al. (+8 more)
Effectively eliciting the capabilities of large
language models (LLMs) at inference time remains an important challenge. Existing studies address this challenge through context engineering, which shapes user queries, or fixed steering, which directly intervenes in model computation. However, predefined contexts and fixed intervention strategies may not adequately accommodate the diverse knowledge and
reasoning requirements of individual queries. This raises a natural question: Can internal steering in LLMs be made query-adaptive? In this work, we investigate whether query-induced internal activations can guide adaptive steering in LLMs. Our pilot studies reveal subject- and layer-dependent
neuron activation patterns and show that amplifying subject-relevant
neurons can improve model performance. Based on these findings, we propose Q-NeuS, a query-adaptive
neuron steering framework that dynamically determines which
neurons to modulate and by how much for each input. Specifically, Q-NeuS constructs a compact subject-guided candidate
neuron space and employs a lightweight Query-Adaptive Gate to generate
neuron-wise modulation coefficients. To learn effective intervention strategies, we introduce Neu-GRPO, which adapts group-relative optimization to train the Query-Adaptive Gate using the relative performance of multiple
neuron intervention configurations. The base LLM remains frozen throughout training and inference, and no task or subject labels are required at inference time. Extensive experiments across multiple LLMs and
benchmarks demonstrate that Q-NeuS consistently improves model performance and exhibits broad applicability across model scales and strong cross-
benchmark transferability.
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All authors Yanxi Pan, Jingwen Wu, Yan Ma, Xiang Chen, Xinyi Zeng, Xuetong Wang, Yunyan Zhang, Yue Zhang, Yu Tian, Jianfeng Pan, Hongyi Zhou
Computer Vision
We Have Been Interpreting Diffusion Transformer Backbone Behaviors Wrong
Benyuan Meng, Longtao Huang, Yang Liu et al. (+1 more)
During a network forward call,
diffusion backbones naturally produce many intermediate activations. If we evaluate each activation against an identical downstream benchmarking protocol, we can plot an inverted-U-shaped curve as the depth profile: the activation's performance goes up first and then down as we go into deeper layers. This is common knowledge in the
diffusion model community, which inspires many applications including the widely-adopted REPA. However, the common interpretation of this depth profile may be inaccurate and incomplete: (i) The cause of this curve has long been treated as signal granularity changing from coarse to fine, but there has been little evidence in DiT backbones. (ii) The community has been mainly studying this phenomenon in a static way, without investigating how it changes across different training setups. In this work, we show a different mechanism picture for the profile, where the prediction target (e.g., $\epsilon$, $u$, $x_0$) sits at the center. The semantically meaningful signal is only an intermediate by-product for predicting the target, while the prediction target is the real goal for the backbone. Furthermore, the choice of prediction targets as part of the training setup can predictably affect the curve's shape. With this updated interpretation, many applications of this depth profile can be better grounded and predicted. As an example, we show how to predict the near-optimal placement of REPA based on the prediction target of the training setup. The code is at https://github.com/Darkbblue/dit-compare.
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All authors Benyuan Meng, Longtao Huang, Yang Liu, Liang Lin
Information Theory · Cross-listed from eess.SP
UAV SAR Imaging via Diffusion Posterior Sampling under Non-Ideal Motion in ISAC Systems
Keying Zhu, Le Liang, Jie Yang et al. (+2 more)
Unmanned aerial vehicle-enabled synthetic aperture radar (SAR) offers significant potential for providing communication services to ground users while sensing the surrounding environment in low-altitude integrated sensing and communication (
ISAC) systems. However, its practical deployment is challenged by known non-ULM trajectories and unknown position errors, which violate conventional SAR assumptions and cause severe image defocusing. We address these non-ideal impacts by formulating the
ISAC SAR imaging as an exact linear inverse problem to accurately model arbitrary trajectories with multiple-input multiple-output orthogonal frequency division multiplexing waveforms. Based on this formulation, we develop a
diffusion posterior sampling (DPS)-based super-resolution SAR framework, where a pre-trained
diffusion model provides a data-driven image prior and the exact physical model enforces measurement consistency during posterior inference. The inference process is further accelerated for practical implementation. For unknown position errors, we develop a joint iterative DPS and motion compensation (MoCo) optimization
algorithm with discrete cosine transform reparameterization and coarse-to-fine spectral scheduling. Simulations demonstrate that our framework achieves high-accuracy reconstruction across diverse trajectories and remarkable super-resolution, maintaining greater than 21.3 dB peak signal-to-noise ratio (PSNR) even under half physical resolution truncation. Under unknown motion errors, the joint DPS and MoCo
algorithm achieves 23.65 dB PSNR with a residual position-error estimation error of 0.19 cm.
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All authors Keying Zhu, Le Liang, Jie Yang, Fan Liu, Shi Jin
Multiagent Systems
SEPM: Self-Evolving Procedural Memory Grounded in Task Graphs for Multi-Agent Collaboration
Yi Liu, Junwei Zhao, Li Sun et al. (+2 more)
Long-horizon multi-
agent collaboration requires
memory that preserves both successful actions and the coordination structure underlying them. Existing
memory systems support information sharing or procedural reuse, but often leave the
evolution of shared procedures disconnected from the task dependencies and execution states of collaborating
agents. This coupling also limits the direct transfer of procedural
memory designed for individual
agents to team settings. We propose Self-Evolving Procedural
Memory (SEPM), a framework that uses task dependency graphs as a common representation for online coordination and procedural
memory evolution. During execution, a lightweight shared blackboard records structured evidence alongside task dependencies, supporting the detection of goal, plan, and world-state divergence and evidence-based realignment. After execution, verified actions and dependencies are distilled into reusable procedures, while recurring failures inform warnings and constraints. The shared procedure pool evolves through creation, refinement, specialization, and retirement, guided by success and cost utility and constrained by evidence and safety requirements. Together, these mechanisms preserve the collaborative structure and applicability conditions needed to reuse experience across tasks. Evaluations on ALFWorld, WebArena, and OfficeBench under AutoGen and DyLAN show that SEPM achieves the highest task scores among the compared methods in all six
benchmark settings. Under AutoGen, it improves task scores over the no-added-
memory baseline by 29.85, 25.86, and 13.00 percentage points, respectively. The codebase is available at https://github.com/cookieApril/SEPM.
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All authors Yi Liu, Junwei Zhao, Li Sun, Jiansong Zhang, Sen Su
Computer Vision
Compositional Concepts Erasing: Disentangling Object-Style via Domain Manifold Unlearning
Yang Xiao, Kaiyuan Deng, Gen Li et al. (+4 more)
Diffusion models pose significant risks regarding the generation of illicit content and copyright violations. This has catalyzed the development of machine unlearning to erase harmful concepts or copyrighted styles from a model's generative repertoire without compromising its general performance. However, previous unlearning approaches on
diffusion models have largely focused on either object-level or style-level removal and failed to address the challenge of object-style combination unlearning. At the same time, multiple unlearning requests often lead to catastrophic forgetting. To address these challenges, we propose Domain
Manifold Unlearning (DMU), which uses an EMA-updated feature proxy extracted from intermediate
diffusion features to represent the retained target-object direction. As a lightweight estimating representation of an object, DMU enables selective unlearning of domain-specific components in a specific object while preserving other semantic representations, thereby significantly enhancing robustness in multiple combination unlearning scenarios. Extensive experiments show that DMU significantly mitigates catastrophic forgetting, outperforming state-of-the-art methods of batch unlearning and sequential unlearning.
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All authors Yang Xiao, Kaiyuan Deng, Gen Li, Zinan Ling, Zhiwen Fan, Xiaolong Ma, Bo Hui