Systems and Control
Attack-Resiliency Analytics for Wide-Area Control Systems in Smart Grids
Mohammad Zakaria Haider, Prabin Mali, Nur Imtiazul Haque et al. (+3 more)
Wide-area monitoring, protection, and control (WAMPAC) systems damp inter-area oscillations in large interconnected grids, but their reliance on synchronized PMU measurements carried over wide-area networks exposes them to false data injection (FDI) attacks. This paper presents an attack-resiliency analytics framework that formally models the coupled dynamics of the wide-area damping loop, automatic generation control, and the governor and excitation systems, and formulates the optimal stealthy FDI attack as a mixed-integer linear program (MILP). The anomaly detection model (ADM) enters as a replaceable constraint set: either a static bad-data detection (BDD) rule or a boundary learned from benign operation calibrated to a common false-positive rate. On the IEEE 39 and 118 bus systems, with reference dynamics validated on an OPAL-RT hardware-in-the-loop testbed, the optimal attack with wide-area access attains 3.3 and 8.1 times the benign objective and reaches a 0.5Hz frequency excursion more than twice as fast as an attack confined to automatic generation control. Learned boundaries reduce the attack objective by 15.5 55.0% and prevent over-frequency relay trips in all configurations, with feasible stealthy attacks remaining in every case, and residual risk tracks the width of the learned boundary rather than the detector family.
Read the full abstract ▾
All authors Mohammad Zakaria Haider, Prabin Mali, Nur Imtiazul Haque, Muhammad Nadeem, Sumit Paudyal, Mohammad Ashiqur Rahman
Robotics
Inferring Soil Friction Angle from Robot Foot-Ground Force Histories: A Bayesian Inverse Approach to Proprioceptive Soil Sensing
Dawei Xu and Zhijie Wang
Foot-ground interaction signals recorded by quadruped
robots may enable spatially distributed, in situ characterization of soil strength. As a first step, we test whether the internal friction angle $\phi$ of cohesionless soil can be identified from the force history of a simplified rotating leg. A two-dimensional continuum model implemented with the material point method, benchmarked against measured rotating-leg force histories, generates the training data, and two Gaussian-process surrogates support
Bayesian inversion of the full histories. In matched-model experiments, the framework recovers 14 off-grid friction angles with a median absolute error of approximately $0.1^\circ$ (maximum $\sim 0.7^\circ$); the reported credible intervals contain the true value in every case. These results establish that $\phi$ is identifiable when the forward model is correctly specified, and support further development of proprioceptive soil sensing for spatially variable terrain, with applications from physics-grounded world models for
robot training to post-wildfire slope assessment.
Read the full abstract ▾
Artificial Intelligence
MemEvo: Automatic Discovery of Streaming Video Memory Mechanisms
Guohong Liu, Jialei Ye, Shanhui Zhao et al. (+2 more)
Query-agnostic streaming
video understanding requires
vision-
language models to continuously compress an indefinitely growing visual stream into a bounded
memory before future queries are known. The performance depends critically on the
memory mechanism–what observations to preserve, how to represent and consolidate them, and what information to retrieve when a query eventually arrives. Rather than designing a single
memory architecture by hand, we formulate
memory design as a search problem over executable
memory programs. We introduce a lightweight domain-specific
language that expresses
memory mechanisms through structured primitives for representation, admission, retention, consolidation, budgeting, and
retrieval, while enforcing causal and bounded-
memory constraints. Although structured, the derived program space remains large and contains heterogeneous, conditionally dependent design choices whose effects can only be assessed via downstream execution. We therefore propose MemEvo, an LLM-driven auto-research framework that uses pretrained LLM as a semantics-aware proposal model to iteratively generate and refine candidate
memory programs based on accumulated experimental feedback. At runtime, a deterministic evaluation pipeline validates and evaluates each candidate, while the underlying
vision-
language model remains frozen throughout discovery. We finally produce a training-free, bounded-
memory mechanism. Extensive experiments on StreamingBench and OVO-Bench demonstrate strong streaming
video understanding performance together with substantial context and inference efficiency.
Read the full abstract ▾
All authors Guohong Liu, Jialei Ye, Shanhui Zhao, Yunxin Liu, Yuanchun Li
Artificial Intelligence
SafeCoEvo: Co-Evolving Safety Harnesses and Guards for LLM Agents at Test-Time
Yu Cheng, Yongkang Hu, Shuaijie Ma et al. (+12 more)
LLM
agents deployed in real-world environments continually encounter new tasks and safety risks, while execution feedback typically becomes available only after each task is completed. However, existing self-evolving approaches commonly rely on multiple rounds of optimization over fixed and repeatedly accessible task distributions, fundamentally differing from test-time adaptation in real-world deployment, where only experience accumulated from past tasks can be used to improve safety decisions on future unseen tasks. To address this limitation, we propose SafeCoEvo, a test-time Harness-Guard co-
evolution framework for LLM
agent safety that enables the external safety system to continually adapt from accumulated runtime experience. SafeCoEvo jointly improves two complementary safety capabilities at different timescales: S-Harness rapidly externalizes recent runtime experience into updatable explicit safety knowledge that can promptly influence subsequent tasks, while GuardVPO internalizes accumulated runtime safety experience over a longer timescale into parametric risk-judgment capabilities. By combining short-term rapid adaptation with long-term capability consolidation, SafeCoEvo continually improves the
agent's safety capabilities, reducing the unsafe outcome rate by 10.05% while improving the task success rate by 12.15% over the strongest baseline, thereby achieving simultaneous gains in safety and task utility.
Read the full abstract ▾
All authors Yu Cheng, Yongkang Hu, Shuaijie Ma, Zhihang Lin, Weicheng Meng, Jingyang Qiao, Jiuan Zhou, Yushuo Zhang, Yihang Chen, Weilin Luo, Kun Shao, Dong Li, Zhizhong Zhang, Yuan Xie, Zhaoxia Yin
Machine Learning
Factorized Scheduling Principle: Learning Interpretable and Transferable Policies via Structured Additive Functions
Hong Je-Gal and Hyun-Suk Lee
Scheduling problems arise from repeatedly selecting one item from a set of candidates based on their states. These problems often reduce to assigning priority scores and choosing the highest-ranked item. In this work, we propose a factorized scheduling principle (FSP) framework to learn interpretable and transferable scheduling rules. The FSP framework represents system states as condition distributions and decomposes a global scheduling principle into additive univariate and pairwise components with identifiability constraints. The scheduling principle enables the framework to maintain a simple priority-based structure during deployment. This principle is learned by using a policy-based objective combined with a temporal-difference signal defined on the condition distribution. Experiments on synthetic and realistic scheduling tasks demonstrate the FSP framework's strong performance, interpretability, and zero-shot generalization across different system scales.
Read the full abstract ▾
Sound
Long-Term Memory-Guided Enhancement for Target Perception in Audio-Language Models
Zhenhong Zhou, Xuanyue Zhao, Youji Liu et al. (+4 more)
Audio large
language models (ALLMs) can reason about the content of audio recordings to perform complex tasks. However, these capabilities usually collapse in real-world environments when background noise and competing sources mix the target sound. Inspired by long-term
memory in human listening, we propose Long-Term
Memory-Guided Audio Enhancement (LTM-AE) to improve selective target perception by refining the audio representations of ALLMs without training. LTM-AE extracts representations in hidden states from separate clean reference recordings as long-term
memory for each category, guiding enhancement toward a user-specified listening target. We reconstruct incoming audio
tokens in the selected category long-term
memory and interpolate the reconstructions with the original
tokens before
language backbone decoding. This interpolation controls the influence of stored auditory experience while keeping all ALLM parameters fixed. Diagnostic readouts across twenty sound categories and three ALLMs show that LTM-AE strengthens responses to a specified target amid three interfering sources. Averaged over constrained and free-form classification, accuracy gains over raw mixtures range from 29.53 to 46.15 percentage points across multiple open source models. For
speech content recovery, LTM-AE with an additional learned
token-level gate reduces Qwen2-Audio's word error rate from 23.07% to 14.77%. This work takes an initial step toward using principles of human long-term
memory to enhance ALLMs for real-world listening. Our code is available at https://github.com/aynlp/ltm-audio-code
Read the full abstract ▾
All authors Zhenhong Zhou, Xuanyue Zhao, Youji Liu, Yuanhe Zhang, Xiaoyu Ma, Lianyu Hu, Yang Liu
Artificial Intelligence
Divide and Inject: Can Agents Reconstruct an Indirect Prompt Injection from Fragments?
Michael Lee, Zhipeng Wei, Yue Dong et al. (+1 more)
Agentic systems are now being widely used to orchestrate tools and reason over long contexts. However, the improving capabilities of the large
language models powering these
agents also create new attack surfaces for indirect
prompt injection. In particular, an attacker may not need to place a complete malicious instruction in retrieved content if the
agent can reconstruct the objective from incomplete fragments distributed across a long context. In this work, we introduce adaptive long-context
prompt injection (AdaLCPI), which combines long-context fragmentation with adaptive search. AdaLCPI splits an attack objective into incomplete fragments, embeds them in external content retrieved through the
agent's tools, and uses a reconstruction cue to
prompt the
agent to combine them. It then iteratively refines the fragments and cue with OpenEvolve using graded scoring and natural-
language execution feedback from the target
agent. Empirically, AdaLCPI achieves higher attack success than strong adaptive baselines, reaching 61.4% macro-average ASR compared with 32.8% for Trojan Hippo-style and 30.0% for AgentVigil. Safety evaluations should therefore test whether
agents remain robust when harmful objectives must be reconstructed from incomplete fragments.
Read the full abstract ▾
All authors Michael Lee, Zhipeng Wei, Yue Dong, N. Benjamin Erichson
Robotics
EquivDP3: A SIM(3)-Invariant Point-Cloud Encoder for Data-Efficient Humanoid Loco-Manipulation
Abu Hanif Muhammad Syarubany and Chang D. Yoo
Visuomotor policies for
humanoid loco-manipulation must generalize across object poses and lighting from only a handful of demonstrations. 3D
Diffusion Policy (DP3) conditions a
diffusion-based action generator on point-cloud features, but its PointNet-style encoder has no built-in equivariance to the rotations, translations, and scalings (SIM(3)) that manipulation tasks respect. EquiBot closed this gap for wheeled manipulators with a SIM(3)-equivariant Vector
Neuron Network (VNN) encoder. We extend this to a substantially more complex embodiment, the 43-joint Unitree G1
humanoid, and propose EquivDP3: a two-stage policy where a high-level
diffusion planner with a SIM(3)-equivariant VNN encoder emits 6 Hz whole-body command chunks, executed at 50 Hz by a frozen, pre-trained RL locomotion policy and a differential inverse-kinematics module for the arms, trained end-to-end by behavior cloning. Across two simulated IsaacLab
benchmarks and four non-equivariant baselines (5-100 demonstrations, in- and out-of-distribution), EquivDP3's advantage concentrates in the low-data regime: at 5-10 demonstrations it reaches 67.1% success versus 38.2-52.4% for the baselines, while by 50-100 all encoders converge (74.3-85.2%) and the ordering is no longer meaningful. A proprioception-only control confirms this gap is genuinely perceptual: with the point cloud removed, success drops to 31% vs. 60% (EquivDP3) at 5 demonstrations and 78% vs. 99% at 10, but vanishes by 50-100, showing the high-data plateau reflects a
benchmark ceiling, not five encoders learning the same invariance. The encoder costs only 0.8 ms of extra latency per action chunk over the PointNet encoder it replaces. Baking
geometric symmetry into a hierarchical
diffusion policy's perception backbone is a practical, nearly free way to improve data efficiency for
humanoid loco-manipulation when demonstrations are scarce.
Read the full abstract ▾
Cryptography and Security
CyberPersistBench: Evaluating LLM-Based Cyber Attackers on Installation and Persistence
Sujin Chen, Lijun Li, Xuhong Wang et al. (+1 more)
While LLM-based attackers exhibit growing proficiency in vulnerability exploitation, most existing cybersecurity
benchmarks suffer from single-stage truncation, prematurely terminating evaluation upon initial access. In practice, initial footholds are exceptionally fragile across operational disruptions such as service restarts and host reboots. Whether LLM-based attackers can establish and maintain durable footholds beyond initial compromise remains a central blind spot in cybersecurity evaluation. To bridge this gap, we introduce CyberPersistBench, the first
benchmark dedicated to post-compromise installation and persistence. Decoupled from upfront exploitation, CyberPersistBench frames persistence as an
adversarial survival task in which
agents use native host mechanisms to maintain footholds across staged system disruptions. Deterministic checks support a six-level scoring method (L1–L6) spanning installation and persistence. The
benchmark comprises 203 core tasks across seven categories, augmented by multi-host and active defense extensions. Empirical evaluations across five frontier
agents show that autonomous persistence remains limited (27.6%–44.8%) and drops further on defense-enabled tasks (5.5%–13.3%); nonetheless, these results reveal an emerging cyberattack risk, underscoring the necessity of benchmarking post-compromise persistence. CyberPersistBench thus establishes a foundational
benchmark for post-compromise installation and persistence, delineating the operational boundaries of autonomous cyber
agents.
Read the full abstract ▾
All authors Sujin Chen, Lijun Li, Xuhong Wang, Jing Shao
Artificial Intelligence
Visual sensitivity is not claim retractability: persistence-aware credit assignment for multimodal reinforcement learning
Zhongan Bi, Kepeng Lin, Xuanang Gao et al. (+2 more)
Reinforcement Learning with Verifiable Rewards (RLVR) has been extended to Large
Vision-
Language Models (LVLMs), and perception-aware methods further encourage policies to rely on visual evidence. Yet relying on the image does not guarantee that visual claims are supported by it. Before RL training, 27.81% of the correctly answered responses of Qwen2.5-VL-7B on four
multimodal reasoning benchmarks contain at least one direct visual claim that the image does not support. Since outcome-level RL rewards each response as a whole, these claims inherit the positive credit of the correct answer. We introduce a fixed-rollout counterfactual diagnostic that re-scores the same response under an intervened image to separate Evidence-Function Sensitivity (EFS), how strongly the model's predictions change, from claim persistence, whether the model keeps supporting the same claim rather than retracting it. The diagnostic reveals Sensitivity-Persistence Decoupling (SPD): under DAPO and VPPO, EFS increases and claims become more retractable overall, yet unsupported claims become significantly more persistent, whereas GRPO raises EFS without this deterioration. We therefore propose Persistence-Aware Credit Gating (PACG), which attenuates positive credit for unusually persistent visual claims and leaves all other credit unchanged. It requires no supported/unsupported labels and adds no inference cost. On Qwen2.5-VL-7B, PACG raises the nine-
benchmark average over three seeds from 58.1% to 59.9% with DAPO and from 59.8% to 60.9% with VPPO, while making unsupported claims more retractable. The gains extend to a larger model, a newer backbone, and the accuracy of HallusionBench also improves consistently. These results suggest that visual sensitivity and claim retractability are complementary dimensions of
multimodal credit assignment.
Read the full abstract ▾
All authors Zhongan Bi, Kepeng Lin, Xuanang Gao, Yuhan Sun, Lianrun Zhang
Cryptography and Security
CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering
Mark Russinovich
Indirect
prompt injection makes an LLM
agent treat untrusted retrieved text as instructions. We present CounterSteer, an inference-time defense that suppresses this behavior inside the model. Per model, a five-step recipe fits a residual-stream direction from paired episodes differing only in whether an embedded instruction is followed, and retains it only if it passes pre-specified causal and capability gates. At deployment, the direction is subtracted from every tool-result
token during prefill. The edit is always on–there is no detection decision to evade–and requires no fine-tuning, auxiliary model, or added
tokens, only white-box serving and tool-result span boundaries. Across five open-weights models (8B-106B, five vendor lineages), held-out attack success falls from 0.21-1.00 undefended to 0.00-0.17 defended, and AgentDojo compromise rate from 0.10-0.49 to 0.006-0.079, at 93-100% typography-normalized benign utility, with larger task-dependent costs when
reasoning over steered content. A
benchmark-level adaptive attacker reaching 0.67-0.73 undefended is held to roughly a quarter of that on the two most deeply evaluated models. Among the defenses we measured on capable models, those achieving lower compromise rates either lost 22-89% of benign utility or fine-tuned the served weights. White-box gradient attacks through the deployed vector compromise at most 2 of 52 episodes, and none of 2,052 replayed human red-team attacks succeeds. CounterSteer largely neutralizes instructional takeover: a black-box framing search cracks 3 of 18 development samples. Parameter manipulation–attacker-chosen arguments in otherwise legitimate calls–is only partially resisted (13 of 18); the decision becomes linearly readable at argument emission but not at the examined pre-generation sites, and is not removed by the tested prefill- or decode-time steering, motivating argument-provenance controls.
Read the full abstract ▾
Machine Learning
From Checkpoint Variation to Selection Gains in Supervised Fine-Tuning
Yupeng Chang, Wenxuan Zhang and Yuan Wu
Checkpoint selection is a routine decision in supervised fine-tuning (SFT): training produces multiple checkpoints, but only one is retained. Yet fixed-budget comparisons do not by themselves distinguish three empirical claims: whether more validation data improve checkpoint selection, whether a selection rule outperforms validation-loss selection, and whether it improves over simply retaining the final checkpoint. We therefore treat checkpoint selection as a finite-information decision problem. Holding completed training trajectories, candidate checkpoints, and independent test items fixed, we vary the validation budget and separately measure improvement from additional validation data, gain over negative log-likelihood (NLL) selection, and gain over the final checkpoint. Across 60 mathematical SFT trajectories and 19 configurations, increasing the validation budget from 32 to 305-313 examples raises independent-test accuracy by 0.32 percentage points (pp) for generated-accuracy selection and 0.29 pp for checkpoint agreement, with 95% configuration-bootstrap CIs of [0.10, 0.56] and [0.11, 0.50], respectively. At the full validation budget, the two generation-based rules outperform matched NLL selection by 0.71 and 0.85 pp, respectively, while their gains over the final checkpoint remain unresolved. A cross-domain replication on 12 newly trained Commonsense trajectories shows the same qualitative separation: increasing the validation budget from 32 to 1,024 questions improves generated-accuracy and checkpoint-agreement selection by 0.87 and 0.27 pp, while gains over the final checkpoint again remain unresolved. Together, these results show that benefiting from more validation data, outperforming NLL selection, and outperforming the final checkpoint are distinct empirical claims that require separate evidence.
Read the full abstract ▾
Machine Learning
Sharp Convergence and Sampling Trade-offs for Riemannian Diffusion under Nonnegative Ricci Curvature
Yuhao Liu and Longbo Huang
Diffusion models have emerged as state-of-the-art generative models, with recent extensions from Euclidean spaces to Riemannian
manifolds. However, existing convergence guarantees for Riemannian
diffusion models typically require $\tilde{O}(\mathrm{poly}(d,T)/\epsilon^2)$ score evaluations, with potentially unfavorable dependence on the dimension. In this work, we develop a general framework that separates score discretization from Brownian-motion simulation and allows multiple geodesic random-walk steps per score evaluation. Under nonnegative
Ricci curvature assumption and an exact Brownian-motion simulation oracle, we show that $\tilde{O}(d/\epsilon^2)$ score evaluations suffice to achieve an $\epsilon^2$ KL divergence from the target distribution, matching the existing convergence rate of Euclidean
diffusion models. We further show that $\tilde{O}(d^4T/\epsilon^2)$ geodesic random-walk steps suffice to approximate the required drifted Brownian motion to $\epsilon$ total variation error. Combining these results yields a sampling scheme with $\tilde{O}(d/\epsilon^2)$ score evaluations and $\tilde{O}(d^4T/\epsilon^2)$ geodesic random-walk steps, motivating multiple random-walk steps between consecutive score evaluations. Our results provide a sharper characterization of the convergence and sampling complexity of Riemannian
diffusion models.
Read the full abstract ▾
Information Theory
On the Lack of Periodicity of Walker Satellite Constellation Routing Tables
Chang-Sik Choi and François Baccelli
In a referential that rotates with Earth, the dynamics of the configurations of satellites in a Delta Walker constellation can be analyzed as a dynamical system as a function of a translation on the
torus. This paper extends this type of analysis to routing over a Walker constellation, leveraging fixed ground relays. It considers any deterministic, time-invariant routing rule on such a collection of satellites and relays. The main object of interest is the routing table associated with this rule, which specifies, for all source-destination pairs, a route made of satellites and relays between them, for instance the shortest, together with angular information on next hop at each step of the route, which is essential for beamforming in this context. The main result is that, for each such routing rule, the routing table inherits the dichotomy of the
torus flow: when the ratio of the Earth-
spin and satellite angular speeds is rational, the routing table process is periodic with the same period as the constellation. When it is irrational, the table is non periodic but admits ergodic long-run averages which can be computed as ensemble averages w.r.t. an explicit invariant measure. Simulations with mixed satellite–gateway greedy routing illustrate these findings in terms of both temporal and spectral properties of the Walker routing table time series. The paper also discusses how to define metrics that cope with such non periodic fluctuations when present. This is illustrated by a comparison of the round-trip times between two far away cities as offered by a Walker constellation using greedy routing on one side, and by the currently available terrestrial fiber on the other side. It is shown how ergodicity can be used to make this comparison between the time varying instantaneous round trip times of the constellation and the constant round trip time of the fiber network meaningful.
Read the full abstract ▾
Multimedia
Toward Generative Video Communication: A Dual-Stream Digital Transmission Framework
Bingyan Xie, Longyu Zhou, Tianhao Liang et al. (+4 more)
Generative
video communication has shown promise for bandwidth-constrained wireless transmission and has the potential to support personalized content delivery. In this article, we propose a dual-stream digital generative
video communication (DGVC) framework that integrates a traditional digital link with a generative link. The traditional link provides source-grounded visual references, while the generative link conveys compact semantic and perceptual information for receiver-side generation. We further discuss three bandwidth-dependent operating regimes and key technologies for dual-stream coordination, synchronization, reliability, and latency control. A practical case study demonstrates the perceptual and temporal-quality benefits of DGVC under wireless fading channels. Finally, we discuss open challenges and future research directions for generative
video communication.
Read the full abstract ▾
All authors Bingyan Xie, Longyu Zhou, Tianhao Liang, Yongpeng Wu, Zehui Xiong, Wenjun Zhang, Tony Q.S. Quek
Computer Vision
AffectReveal: Event-Grounded Emotion Recognition Beyond Visual Appearances
Yihao Qian, Runhao Zeng, Sicheng Zhao et al. (+3 more)
Visual
emotion recognition commonly assumes that all evidence required for prediction is contained in the observed image or
video. Yet the same visible reaction can convey different
emotions depending on events beyond the input: tears, for example, may indicate grief or joy. We formulate Event-Grounded
Emotion Recognition (EGER), where
emotion recognition requires recovering the affect-determining event. We construct EGER-Bench, comprising 10,052
videos and 10,734 images across 11
emotions, two source domains, and four visual settings. A study with six annotators shows that event context raises human recognition accuracy from 33.96% to 72.08%, confirming that visual evidence alone is often insufficient. Semantic relevance alone does not solve EGER: a plausible event may imply the wrong
emotion if its identity, focal-person role, relationship, or outcome is misinterpreted. We therefore propose AffectReveal, a tuning-free framework that first constructs and independently verifies evidence-grounded alternatives over these affect-critical factors. It then cross-checks the recovered event against face-masked in-media facts through bidirectional atomic evidence support, while retaining the original unmasked input for final prediction. Across three downstream models and four input settings, AffectReveal yields average UAR gains of 5.26–10.53 points. For three fine-tunable models, it also enables untuned models to outperform their fine-tuned visual-only counterparts in all 12 accuracy comparisons, without updating downstream parameters.
Read the full abstract ▾
All authors Yihao Qian, Runhao Zeng, Sicheng Zhao, Feng Liang, Hongmin Cai, Mingkui Tan
Computer Vision
ThinkingGuard: Decoding Implicit Hazards via Step-by-Step Risk Attribution in Multimodal Large Language Models
Ruochen Zhang, Yao Huang, Yitong Sun et al. (+5 more)
While
Multimodal Large
Language Models (MLLMs) are increasingly deployed in safety-critical domains, their reliability is threatened by
multimodal implicit risks. Unlike explicit threats, these hazards emerge when individually benign text and neutral visual entities logically converge to induce unsafe outputs. Current detection methods fail to address this because they overlook the underlying risk activation mechanisms that govern cross-modal risk activation, leading to single-modality shortcut learning and hallucinated rationalizations. To bridge this gap, we first construct TriggerBench, the first
dataset explicitly modeling risk compositionality (5,600 instances). By formally isolating Key Elements and Trigger Elements to build counterfactual contrastive pairs, TriggerBench eliminates risk residues and forces models to perform genuine logical deduction rather than superficial pattern matching, which provides a rigorous foundation for both large-scale training and fine-grained evaluation. Building on this, we propose a Step-Supervised Structured
Reasoning training framework and employ it to train ThinkingGuard, a specialized guard model. Inspired by Situation Awareness theory, we decouple implicit risk identification into progressive cognitive stages, and utilize a step-reward Monte Carlo Tree Search
algorithm to explore optimal
reasoning trajectories, which are then distilled into the model through Dual-Constraint Preference
Alignment. Extensive experiments across both standard and implicit safety
benchmarks demonstrate that ThinkingGuard achieves strong performance. Project resources are available at https://github.com/FroggyChen/ThinkingGuard.
Read the full abstract ▾
All authors Ruochen Zhang, Yao Huang, Yitong Sun, Jiahe Xie, Jin Yan, Jifan Ma, Yuanfang Guo, Xingxing Wei
Robotics
Riemannian Splat Regression Models for Learning Time Fields on Arbitrary Riemannian Manifolds
Anastasios Manganaris, Rohit Banerjee, Gokul Prabhakaran et al. (+1 more)
Motion planning on arbitrary Riemannian
manifolds is an important and difficult problem that frustrates typical planning methods for Euclidean spaces. In particular, motion planning methods that approximate optimal time-to-go functions with neural networks, e.g., Neural Time Fields (NTFields), cannot be directly applied without using ad-hoc coordinate projections into higher dimensions. Using these methods directly without such projections is desirable, as it promises to provide the lowest-possible-runtime method for obtaining optimal plans on high-dimensional
manifolds while using minimal model capacity. In this work, we develop a model that requires no coordinate projection and can learn arbitrary functions on Riemannian
manifolds by combining splat regression models with splats defined by wrapped Gaussian distributions. We successfully apply this model for learning arrival time fields on several Riemannian
manifolds, and we compare the accuracy and model size of this approach with multi-layer perceptrons adapted to work on each
manifold individually.
Read the full abstract ▾
All authors Anastasios Manganaris, Rohit Banerjee, Gokul Prabhakaran, Ahmed H. Qureshi
Computer Vision
FM-ReID: Selective Competitive Token Routing for Object Re-Identification
Zhiqi Li, Xiaowei Zhou, Zeyuan Sun et al. (+2 more)
Object re-identification (ReID) faces a recurring challenge: different identities can share highly similar global appearances, while the cues that distinguish them are localized, heterogeneous, and visible only under particular viewpoints. This challenge arises in animal ReID through markings, contours, and scars, in person ReID through subtle clothing and accessory cues, and in vehicle ReID through localized appearance details. Although visual foundation models encode such information in dense
tokens, a single holistic descriptor can obscure discriminative local signals. We propose FM-ReID, an end-to-end framework that formulates local representation learning as selective competitive
token routing. Its Competitive Fine-grained Mining module uses multiple mining queries and a residual query to compete for dense DINOv3
tokens. Above-prior selection retains
tokens preferentially allocated to each mining query, while the residual slot receives
tokens excluded from the
retrieval descriptors. The resulting multi-query descriptors are jointly trained with a holistic representation for
retrieval, without fixed spatial partitions or equal-area constraints. FM-ReID achieves strong results on animal, person, and vehicle ReID
benchmarks, supporting competitive
token routing as an effective way to augment holistic foundation-model representations.
Read the full abstract ▾
All authors Zhiqi Li, Xiaowei Zhou, Zeyuan Sun, Feng Gao, Junyu Dong
Machine Learning
HiTS-CL: A Continual Learning Framework for Long-Horizon Temporal Knowledge Graph Extrapolation
Yansong Liu, Rui Liu, Yuan Zuo et al. (+6 more)
Extrapolative temporal knowledge graph
reasoning (TKGR) predicts future facts from historical snapshots. Most existing methods train once on an early prefix of the timeline and then use a frozen model for all future timestamps. We argue that this fixed-prefix protocol is misaligned with extrapolation. It learns from a static prefix, whereas the target stream is non-stationary: new entities and facts emerge, temporal dependencies shift across regimes, and recurring historical signals must be refreshed online. As a result, models trained only on early snapshots become outdated and degrade over long horizons. We address this mismatch by formulating extrapolative TKGR as continual learning over streaming snapshots. Under this view, effective extrapolation must jointly handle current dynamics, stable knowledge, and recurring historical evidence. Based on these requirements, we propose History-enhanced Two-Step Continual Learning (HiTS-CL), a backbone-agnostic continual learning framework for extrapolative TKGR. HiTS-CL tracks current dynamics via continual fine-tuning, preserves stable knowledge via multi-teacher adaptive
distillation, and retains recurring historical evidence via a selective
memory of recent and frequent facts. We integrate HiTS-CL into five representative TKGR backbones and evaluate it on four
benchmark datasets. HiTS-CL consistently improves extrapolation accuracy, reduces long-horizon degradation, and outperforms strong continual-learning baselines, including a recent method for temporal knowledge graphs. Source code and data are available at https://github.com/liuyansong98/HiTS-CL.
Read the full abstract ▾
All authors Yansong Liu, Rui Liu, Yuan Zuo, Hongwei Zhao, Da Fu, Fuwei Zhang, Fuzhen Zhuang, Yong Chen, Zhe Li
Robotics
A robust single-sensing-element tactile sensor for concurrent pressure and tackiness detection with real-time signal decoupling capability
Ying Yang, Mingwei Gu, Jia-Sen Xie et al. (+8 more)
Integrating tackiness sensation into the artificial skin of
humanoid robots significantly enhances their cognitive and operational capabilities. However existing tactile sensors face challenges in decoupling of the
multimodal signal and stability. Here we present a surface-soft tactile sensor that incorporates a
Hall effect sensor and a soft
magnetic composite within a robust elastic framework. The sensor surface indents under pressure and bulges prominently when retracted from sticky surfaces dynamically altering the
Hall sensor-magnet distance. This generates whole-process-traceable and baseline-separated signals enabling real-time differentiation between pressure and pull-off force. This single-sensing-element design facilitates bimodal sensing at the same contact spot while eliminate stress cross-talk enhancing both accuracy and sensitivity. The fusion of a robust framework and magneto-mechanical sensing mechanism equips the sensor with exceptional reliability and excellent signal baseline stability. This tactile sensor holds substantial potential for advancing robotic capabilities in evaluating adhesive properties monitoring rubber aging precisely handling lightweight objects and cognizing natural objects surface characteristics.
Read the full abstract ▾
All authors Ying Yang, Mingwei Gu, Jia-Sen Xie, Xingyu Ma, Yan-Na Lu, Lin Zheng, Jinhui Gu, Junshuai Chen, Yunjie Lu, Denys Makarov, Jin Ge
Computer Vision
How Medical VLMs Underutilize Their Vision Encoders: A Dermatology Perspective
Janet Wang, Yunbei Zhang, Xiao Wang et al. (+1 more)
Medical
Vision-
Language Models (VLMs) show significant promise for clinical image understanding, offering accurate diagnosis with interpretable
reasoning. However, a critical performance gap exists between their strong
vision encoders and the full
multimodal model: in dermatology, the MedSigLIP encoder outperforms MedGemma by an average of 10.26 percentage points even when both use zero target-task labels; few-shot linear probing provides further evidence of strong visual representations. This gap motivates an investigation of how visual information is used in end-to-end diagnosis and why plausible-sounding predictions can lack grounding in image evidence. Using dermatology as our primary testbed, we systematically investigate three hypotheses for this phenomenon. We further provide a mechanistic analysis of the model's internal
attention patterns, showing that a simple describe-then-decide prompting strategy increases
vision attention by 30-40% during generation. Task-specific fine-tuning improves dermatology classification but reduces cross-domain medical question-answering performance in our evaluation. To address these challenges, we combine label-free prompting with low-label encoder-assisted reranking while keeping the VLM frozen. We validate the interventions across five VLM backbones in dermatology and provide supporting representation and
attention analyses across additional medical modalities.
Read the full abstract ▾
All authors Janet Wang, Yunbei Zhang, Xiao Wang, Jihun Hamm
Artificial Intelligence
MAADBench: The Refreshable Paradigm for Anomaly Detection in Multi-Agent Systems
Lei Ma, Dennis Hofmann, Haowen Xu et al. (+4 more)
Recent studies report that LLM-based multi-
agent systems (MAS) fail at rates of 41%-87%, yet to our knowledge, no
benchmark to date supports systematic anomaly detection (AD) for them. Building MAS AD
benchmarks is hard because they must remain fresh as LLM systems evolve: tasks may leak into training data and thus be memorized by LLMs, traces and anomaly patterns expire as backbones evolve, and labels must be provided reliably for each refresh. To address these challenges, we present MAADBench (MA: multi-
agent; AD: anomaly detection), the first refreshable MAS AD
benchmark designed for diverse, evolving LLM backbones underlying the
agents. MAADBench combines (1) sampled-and-coupled generative tasks over an approximately 10^37-task space to mitigate task leakage, (2) refreshable trace generation under configurable LLM backbones, and (3) automated provision of cost-free, deterministic step-level labels for fine-grained AD evaluation. Beyond offering the paradigm itself, we run MAADBench with five state-of-the-art LLM backbones and release the MAADBench-Full
dataset with 5,200 step-labeled traces. Benchmarking 25 AD methods on the MAADBench
dataset reveals substantial limitations in current approaches: they rely heavily on supervision, struggle with subtle MAS-specific anomalies, and lack robustness across LLM backbones. These gaps point to a rich research agenda for MAS-specific anomaly detection, with MAADBench providing a systematic and refreshable testbed for method development and evaluation. We open-source MAADBench-Full at https://huggingface.co/
datasets/hww123/MAADBench-full.
Read the full abstract ▾
All authors Lei Ma, Dennis Hofmann, Haowen Xu, Joshua DeOliveira, Peter VanNostrand, Lei Cao, Elke Rundensteiner
Robotics
Learning to Explore Hidden Kinematics for Articulated Object Manipulation
Ruiyao Liu, Boshu Lei, Zhuoyang Pan et al. (+1 more)
The kinematics of an articulated object is often ambiguous from
vision alone. Interaction resolves the ambiguity, and active perception methods exploit this by searching for the single action that most sharpens a belief over the kinematic parameters at each step. Such greedy search cannot be extended over a horizon without forward models of the contact and inertial dynamics, which are themselves unknown. We instead amortize action selection into training. We maintain a belief distribution over joint type and parameters, initialized from a generative prior and updated by
Bayesian filtering on the observed part motion. To condition the policy on this belief, we render it as a per-point articulation flow field, the motion that the current posterior predicts for every point on the object. Carrying the inductive bias of articulated motion, this representation generalizes better than a latent encoding of the belief or flow tracked from observation. We train the policy with reinforcement learning, rewarding the
entropy that each interaction removes from the posterior, so that informative exploration becomes learned behavior rather than a search at every step. Our method outperforms previous approaches across door and drawer manipulation on the PartManip
benchmark, and reaches 61.7% success on ArticuRiddle, a new
dataset of objects whose appearance implies the wrong articulation, against 44.4% for the best previous method. Project Website: https://hiddenkinematics.github.io/
Read the full abstract ▾
All authors Ruiyao Liu, Boshu Lei, Zhuoyang Pan, Kostas Daniilidis
Machine Learning
SERA: Scale-Equalized Rollout Allocation for Maximum Likelihood Reinforcement Learning
Zihao Chen, Fanxiang Xiong, Hongran Ren et al. (+6 more)
Maximum Likelihood Reinforcement Learning (MaxRL) targets
prompt-wise log-success and has shown strong performance on
reasoning tasks. Under finite rollout budgets, however, the
estimator used by MaxRL attenuates each
prompt's likelihood gradient by a factor that depends on its success probability and rollout count. Under uniform rollout allocation, the common rollout count fails to compensate for success-dependent attenuation, leaving low-success
prompts more strongly attenuated and distorting their relative contributions to the expected aggregate gradient. We introduce SERA (Scale-Equalized Rollout Allocation), which redistributes a fixed rollout budget to approximately equalize these finite-rollout scaling factors. Building on our theoretical analysis of how finite rollouts distort
prompt-wise likelihood gradients, we formulate the allocation as a fixed-budget max–min problem, derive a waterline solution to its continuous relaxation, and introduce a multiplicity correction to remove the additional
prompt weighting induced by heterogeneous rollout counts. Experiments show stronger
alignment with exact likelihood gradients in a controlled ImageNet setting and improved multi-sample solution coverage over MaxRL on maze
navigation and mathematical
reasoning under matched training rollout budgets.
Read the full abstract ▾
All authors Zihao Chen, Fanxiang Xiong, Hongran Ren, Xuefeng Bai, Zhongxiang Dai, Kehai Chen, Zhiguo Zhang, Zhiyong Wang, Yu Cheng
Computation and Language
Grounded Revision vs. Prior Injection: Probing Retrieval-Augmented Patent Claim Amendment
Josepha Michiko Leo, Hyun-seok Min, Yehoon Jang et al. (+3 more)
Retrieval-augmented generation is widely used in professional writing, yet whether
retrieval grounds revision or merely injects templates is rarely tested where "correct" has a definable meaning. Patent claim amendment supplies that signal: the examiner names the attacked limitation and cites prior art, providing per-case ground truth. We release three artifacts: (i) a corpus of 7,385 USPTO prosecution cases with XML-aligned pre/post claims, rejection, and cited prior art; (ii) a seven-probe battery comparing random and structural-match
retrieval as two policies under a fixed
prompt scaffold; (iii) a deterministic five-channel metric (C1-C3 and C5 in main, C4 supplementary) requiring no LLM evaluation. Across 9,600 pre-registered calls on four frontier LLMs (Claude Sonnet 4, Claude Haiku 4.5, GPT-5.4, GPT-4o-mini), no tested model exhibits detectable classical prior-injection behavior;
retrieval effects are small and direction-inconsistent between random and structural
retrieval, and the null is unchanged under a dense (semantic) retriever, across
retrieval depths k in {1,3,5,10}, and under a paraphrase-sensitive grounding metric. Revision locality reveals a model-specific difference that the template channel misses. The four-
cell taxonomy, which we treat as exploratory, leaves the prior-injector
cell unoccupied.
Read the full abstract ▾
All authors Josepha Michiko Leo, Hyun-seok Min, Yehoon Jang, Irvan Zidny, Jin-Woo Chung, Sungchul Choi
Machine Learning
Interactive-Policy Distillation with Bidirectional Propose-and-Verify
Shutong Wu, Xiwen Chen, Brendan Rappazzo et al. (+4 more)
On-policy
distillation (OPD) trains a student model on its self-generated trajectories with dense
token-level teacher feedback. However, naive OPD may suffer from teacher unanchoring, where the student's
reasoning trajectory drifts far from the teacher, causing the teacher to be queried on states it would hardly visit and thus provide unreliable supervision. We propose Interactive-Policy
Distillation (IPD), which applies adaptive teacher intervention to the student rollout. Under a bidirectional propose-and-verify state machine, the student and teacher alternately exchange their roles as proposer and verifier, and collaboratively generate mixed-source trajectories. Then different supervisions are applied according to the source of each
token. This bidirectional propose-and-verify mechanism and the source-split loss make IPD not only a more performant
distillation method, but also a unified bridge between on-policy and off-policy paradigms. To make the interleaved dual-model rollouts more efficient, we also design a dedicated fused inference engine that co-hosts both models in one serving instance with separate KV caches and instantiates the state machine model to distribute, collect, and process requests. On math
reasoning tasks and across multiple teacher-student model pairs, student models trained with IPD not only outperform those trained with OPD, but also demonstrate higher data efficiency. Specifically, when distilling Qwen3-30B-A3B into Qwen3-1.7B-Base, IPD brings a +3.28 mean@8 and a +3.28 best@8
benchmark-averaged accuracy improvement compared with OPD. Besides, IPD only consumes about 1/4 of the training examples and steps to outperform OPD trained on the whole training
dataset for one epoch. We also investigate the impact of different loss variants and takeover / handback configurations, and demonstrate the robustness of IPD on different training data.
Read the full abstract ▾
All authors Shutong Wu, Xiwen Chen, Brendan Rappazzo, Daiheng Zhang, Anderson Schneider, Yuriy Nevmyvaka, Jiawei Zhang
Computer Vision
SCCM: Spherically Consistent Coarse Matching for ERP Dense Feature Correspondence
Gyeonggwan Lee, Eunsoo Im, Seunghwan Hong et al. (+1 more)
Dense feature matching between 360$^\circ$ panoramas underpins omnidirectional pose estimation, 3D reconstruction, and SLAM. Such panoramas are stored in the equirectangular projection (ERP), which unrolls the viewing sphere onto a flat chart and thereby introduces three distinct distortions – a longitudinal seam (
topology), latitude-dependent stretch (metric), and non-uniform pixel area (area) – that the coarse stage of perspective-trained dense matchers does not model, so these matchers degrade systematically on ERP. We show that correcting the three distortions at the coarse-stage interfaces where they arise – pairwise distortions in
attention, per-pixel distortion in covisibility gating – improves PCK@1$^\circ$ from 0.230 to 0.275 on Matterport3D under a fixed coarse scaffold, with the refiner architecture unchanged – our central result. Concretely, SCCM (Spherically Consistent Coarse Matching) augments a chart-naive cross-
attention/dual-softmax coarse matcher with two sphere-derived priors: Spherical Positional
Attention (SPA) pairs a yaw-periodic RoPE (
topology) with a tangent-plane bias (metric), and Area-Aware Covisibility (AAC) applies a pre-sigmoid log-area correction (area). The chart-naive scaffold serves as a controlled reference, separating the scaffold-replacement effect from the spherical-prior effect. Instantiated in the RoMa V1 framework with the same frozen encoder, refiner architecture, and loss, SCCM also outperforms the ERP-native EDM (0.163) and an ERP-retrained RoMa V1 (0.198) under a unified ERP dense matching protocol, while perspective-trained matchers largely fail on ERP. It further transfers zero-shot to Stanford2D3D and, when trained on outdoor Holo360D, leads there as well.
Read the full abstract ▾
All authors Gyeonggwan Lee, Eunsoo Im, Seunghwan Hong, Junghun Suh
Computation and Language
DraftTrace: A Multi-View Analytics Environment for AI-Integrated Writing
Divyansh Chandarana, Sandipan De and Vivek Gupta
Generative AI has changed how students produce writing assignments. The final artifact is no longer sufficient to understand the process through which it was produced. We introduce DraftTrace, a writing environment that jointly captures three complementary views of writing: the final product, the writing process and interactions with an integrated AI-assistant. DraftTrace reconstructs how a document develops over time and organizes these signals into submission, longitudinal, and class-level analytics for instructors. We deployed DraftTrace in a graduate NLP course with 81 students and compared their sessions with LLM-generated responses entered by automated tools and with copy-typed responses. While product measures distinguish differences in text formulation, process measures distinguish differences in how text is entered. Considering both views together helps characterize cases such as copy-typing. Interaction traces show that students use the assistant differently across stages of writing: to clarify the question at an early stage and to verify answers at a later stage. A preliminary instructor survey highlights the importance of multi-view writing analytics and their interpretability.
Read the full abstract ▾
Robotics
Planning Oriented 3D Scene Completion via Coupled TUDF Occupancy Representation Learning from Partial Observations
Tianyou Yu, Pengfei Zhao and Chao Xu
Partial observability remains a fundamental challenge in robotic
navigation, where limited sensor coverage and occlusions leave large portions of the environment unobserved. Existing scene completion methods primarily focus on improving incomplete mapping or reconstructing partially observed 3D structures, but rarely investigate how scene completion can be designed to benefit downstream tasks such as path planning. In this work, we propose a path-planning-oriented 3D scene completion framework that moves beyond pure occupancy modeling toward a coupled
geometric formulation. Specifically, given partial
LiDAR observations as input, the proposed framework jointly predicts completed Truncated Unsigned Distance Field (TUDF)-based continuous
geometric representations and voxel-wise occupancy maps. This coupled representation allows the network to better reason about obstacle boundaries and free-space geometry. To fully exploit the synergy between the two representations, we introduce a bidirectionally coupled learning scheme, where TUDF features provide dense
geometric guidance to improve occupancy reconstruction, while occupancy features in turn offer complementary structural constraints that refine distance-field estimation. Consequently, the proposed network directly predicts complete occupancy and TUDF representations, allowing seamless integration of TUDF into trajectory planning without post-processing. Extensive experiments on unseen environments demonstrate that the proposed method consistently improves both
geometric reconstruction quality and downstream planning performance.
Read the full abstract ▾