Artificial Intelligence · Cross-listed from cs.CY
Scaling, Lock-In, and Proxy Compliance: A Political Economy of Responsible AI
Florian A. D. Burnat and Brittany I. Davidson
AI accountability at scale is an institutional problem: who can observe, verify, and change deployed systems. We develop a sequential political-economy model in which an AI vendor chooses auditability and substantive mitigation, a deployer monitors after adoption while facing switching costs, and enforcement depends on verifiable evidence. Anticipating the deployer's monitoring response, the vendor may stop at an observable procurement floor while mitigating below the social first best, producing a proxy-compliance
equilibrium. We characterize the unique interior
equilibrium and the corner in which harm is fully mitigated. Independent audit rights raise enforcement exposure directly; portability restores deployer leverage; incident reporting adds a regulator-visible evidence channel; and outcome-linked liability creates incentives that do not depend on vendor-controlled detection. The results explain why documentation and standardized evaluations can coexist with persistent post-deployment harms, and generate testable implications for monitoring, mitigation, and the gap between formal compliance and operational outcomes.
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Artificial Intelligence · Cross-listed from cs.LG
Flux-OPD: On-Policy Distillation with Evolving Contexts
Yuran Wang, Zekun Wang, Bohan Zeng et al. (+10 more)
Large
language model training in open-ended domains lacks verifiable rewards, making task preferences difficult to formalize as effective supervision. Contexts derived from task feedback can provide richer supervision, yet fixed contexts cannot adapt as the student evolves, motivating contexts that evolve with student performance. However, directly using evolving contexts as in-training supervision results in unstable
distillation targets and conflicting distributions, requiring mechanisms to stabilize the target and downweight conflicts. In this paper, we characterize context
distillation through a reverse-KL decomposition, showing that the student is distilled toward the
geometric mean of context-conditioned teachers, while the objective contains a conflict term measuring disagreement among these teachers. Motivated by this characterization, we propose Flux-OPD, an OPD paradigm that uses evolving contexts as in-training supervision to incorporate contextual signals derived from task feedback. Flux-OPD treats differences between context-conditioned and context-free teachers as contextual difference signals, injects them as contextual corrections into the context-free teacher, and weights the correction strength using the conflict term. Experiments show that Flux-OPD achieves the best results among OPD paradigms, highlighting the potential of combining teacher supervision with evolving contexts as in-training supervision.
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All authors Yuran Wang, Zekun Wang, Bohan Zeng, Ruixu Zhang, Wenxuan Liu, Liu Yang, Yifan Dai, Yang Shi, Bozhou Li, Chengzhuo Tong, Daili Hua, Yuanxing Zhang, Wentao Zhang
Artificial Intelligence · Cross-listed from cs.CL
RepBench: Compiling Benchmarks into Capability Representations for Large Language Models
Yanshi Li, Xueru Bai, Shuman Liu et al. (+1 more)
Representation engineering reads and steers capability directions in large
language models, yet methods are typically evaluated on paper-specific synthetic data. The resulting measurements are difficult to compare or reproduce and may reflect surface patterns rather than capabilities. We present RepBench, a
benchmark-grounded data layer for capability-aligned representation probing. Crawling 13,427
benchmark papers yields a taxonomy of 182 capability clusters in 13 families; harvesting 353 public
benchmark datasets yields 46,149 audited probe texts covering 94 capabilities, each supported by at least two independent
benchmarks. This multi-
benchmark design reduces dependence on any single source: raw per-text vectors exhibit no natural cluster granularity, whereas
benchmark-pooled capability vectors show an interior clustering optimum at a small number of clusters on all 12 evaluated models, with low agreement to the human taxonomy. Under cross-
benchmark transfer evaluation across twelve models completed by all four readouts, difference-in-means attains the highest model-level mean on ten models, while logistic regression wins the most capability-model
cells. This disagreement shows that the readout method and aggregation criterion are meaningful evaluation dimensions. The pipeline, corpus, and evaluation code are released as a reusable closed-loop workflow.
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All authors Yanshi Li, Xueru Bai, Shuman Liu, Long Zhang
Artificial Intelligence · Cross-listed from cs.CV
Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures
Sweta Banerjee, Alireza Teimoury, Nils Porsche et al. (+11 more)
Pathology foundation models (FMs) are models trained on vast amounts of typically unlabeled data and have been shown to yield regularized latent spaces that can be used effectively in downstream classification tasks. This is also true for the classification of mitotic figures vs. other
cells. However, it is so far unclear if the latent space of current FMs provides features that are discriminant and spatially suitably resolved to also serve as a backbone for dense object detection paradigms. In this work, we investigate this question for common current pathology FMs (UNI, UNI2-h, Virchow, Virchow2, H-optimus-0, H-optimus-1) and compare their performance against a fully end-to-end trained baseline based on a ResNet50 architecture. We combine FM backbones with representatives of single stage, dual stage and self-
attention-based detectors (RetinaNet, Faster R-CNN, Deformable DETR respectively) on the multi-domain MIDOG++
dataset, and on the TUPAC16
dataset as an out-of-domain case. We show that the H-optimus-0 and Virchow models yielded competitive performance, indicating that the latent spaces of current FMs, all trained on image-level self-supervision, are suitable for direct mitotic figure detection and may be slightly more robust on our out-of-domain test case. All code is made available publicly at https://github.com/DeepMicroscopy/FM4MFdet.
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All authors Sweta Banerjee, Alireza Teimoury, Nils Porsche, Alexandra K. Stoll, Viktoria Weiss, Niklas Hargarter, Jonas Ammeling, Thomas Conrad, Christoph Stroblberger, Christopher Kaltnecker, Robert Klopfleisch, Christof A. Bertram, Katharina Breininger, Marc Aubreville
Artificial Intelligence
MMLDSum-LLM: Multimodal Long-Document Summarization with Visual-Alignment and Keyword-Aware
Xianpeng Zhang, Jiahua Yang, Dongyu Chen et al. (+7 more)
Multimodal long documents are core carriers of professional knowledge, where critical evidence is sparsely distributed across paragraphs and modalities. This easily causes key information omission and cross-modal
hallucinations in summarization by
multimodal LLMs. These issues stem from
attention drift in long-range dependency modeling and gaps in inter-modal
alignment. To address this, we introduce MMLDSum-Bench, a high-quality
benchmark for
multimodal long-document summarization, covering multiple domains, context-length scales, and visual-textual modality distributions. We further propose MMLDSum-LLM, a reproducible two-stage training framework that combines supervised fine-tuning with visual-
alignment weighted loss and keyword-aware weighted loss, followed by GRPO with a multi-objective reward (keyword coverage, image-text
alignment, ROUGE, and length control). Extensive experiments on MMLDSum-Bench, comparing against leading closed-source and open-source
multimodal models under a unified evaluation protocol - including LLM-as-a-judge scoring, atomic-claim precision/recall, image-text
alignment (ITA), and ROUGE - demonstrate that our approach significantly improves key-information coverage and cross-modal consistency.
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All authors Xianpeng Zhang, Jiahua Yang, Dongyu Chen, Lei zhang, Jian Ma, Xu guohuan, Haonan Lu, Tianhuang Su, Chuangchuang Wang, Kai Tang
Artificial Intelligence
SKIMIX: Multi-Agent Harness-Time Scaling with Skill Mixture for Dynamic Harness Engineering
Jia Luo
AI
agents increasingly rely on large skill libraries, but selecting, combining, and maintaining skills remains difficult. We propose SKIMIX, a multi-
agent framework in which
agents with different skill portfolios collaborate through iterative refinement. SKIMIX combines
embedding-based skill
retrieval, submodular anti-dilution routing, and adaptive skill
evolution. Across six
reasoning benchmarks, multi-
agent collaboration substantially improves open-ended mathematical
reasoning but offers limited or negative gains on multiple-choice tasks.
Agent-count scaling is non-monotonic, and most improvements arise during the first refinement round. These results show that task characteristics determine whether skill-level ensembles help and provide practical guidance for scalable
agent design.
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Artificial Intelligence · Cross-listed from cs.CR
Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks
Spyridon Raptis and Haralampos-G. Stratigopoulos
Spiking Neural Networks (SNNs) communicate through sparse binary spike events rather than dense activations, enabling energy-efficient inference on neuromorphic hardware and motivating their use in always-on, battery-powered edge systems. We show that this same efficiency advantage creates a distinct security risk: sponge attacks can increase inference-time spike activity and synaptic workload, inflating energy consumption while remaining difficult to detect through correctness-based monitoring alone. Prior input-space efficiency attacks on SNNs have focused on per-sample optimization, primarily in rate-coded settings. We extend this threat to native event-based binary inputs and study two attack models. First, we develop a per-sample sponge attack that crafts a custom
adversarial spike train for each input via gradient-based optimization. This attack increases per-inference SynOps by 1.5-2.6x on three SNN models for the NMNIST, SHD, and IBM DVS Gesture
datasets, while preserving the predicted class on at least 98% of evaluated samples. Second, to the best of our knowledge, we introduce the first universal sponge attack for native event-based SNN inputs: a fixed binary perturbation computed offline and applied via XOR to all subsequent inputs. Although weaker, it still inflates SynOps by 1.09-1.24x across all three
datasets and represents a more realistic deployment threat because it requires no per-input optimization. Mapping SynOp inflation to estimated Loihi-1 energy yields per-inference overheads from 14 $\mu$J to 13.24 mJ. These results show that native event-based SNNs are vulnerable to practical input-space efficiency attacks, and that reusable universal perturbations can accumulate into meaningful battery drain in continuously deployed edge systems.
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Artificial Intelligence
Share the Judge, Learn the Deferral: Where Specialization Helps LLM Evaluation
Ye Chen and Weining Zhang
Agentic systems generate outputs faster than human review. We contrast two LLM evaluator specialization strategies: specialized judge weights, or rule-based deferral policies for safe judgment acceptance. On 99,952 rubric-conditioned samples, correct rubrics improve accuracy by 2.11 points, while incorrect rubrics reduce performance by 2.66 points. Splitting training data across eight criterion-specific LoRA experts lowers accuracy by 10.05 points and reduces 5% error-bound coverage from 24.44% to 5.43%. This loss is independent of model size and training settings, with most performance recoverable by warm-starting experts from a unified judge. Sweeping data budgets confirms scratch expert specialization yields no empirical gains. Warm-started splits appear competitive with unified models, yet under limited data, unified training outperforms split training, with specialization beneficial only after unified training plateaus. Results hold on HealthBench, where physician rubrics improve accuracy while flawed rubrics degrade performance. Unlike weight specialization, deferral policies enable efficient evaluation. On RewardBench 2, lightweight deferral heads form a 0.6B-4B-8B reward cascade with no core scoring modification. Across 20 splits, the cascade achieves 89.40% accuracy versus 84.75% for a standalone 8B judge at 41.5% compute, satisfying 95% risk constraints. Margin-based deferral matches accuracy at far higher compute cost. The design generalizes across models, improving Tulu-3-8B and Skywork-8B performance with a lightweight DeBERTa frontend. We derive simple, robust evaluator design rules: unify judgment training or warm-start split models, and use audited deferral cascades for low-cost, reliable LLM evaluation.
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Artificial Intelligence · Cross-listed from cs.LG
TAPO: Transition-Aware Policy Optimization for LLM Agents
Cong Li, Peixi Peng, Yisen Zhao et al. (+4 more)
Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large
Language Model (LLM)
agents. However, existing methods predominantly rely on sparse task rewards for policy optimization, failing to fully exploit another class of inherently dense supervisory signals naturally present during online interaction: environmental feedback following action execution. Recent theoretical studies suggest that generalization in multi-step, goal-oriented tasks hinges on predictive knowledge of environmental consequences. Inspired by this, we propose TAPO: Transition-Aware Policy Optimization for LLM
Agents, a unified training framework that alternates between policy optimization and transition supervision. Beyond standard RL updates, TAPO repurposes rollout data to apply action-conditioned next-observation prediction supervision on a shared backbone model. This approach enhances the model's sensitivity to environmental transition dynamics and action consequences while concurrently optimizing the policy. It serves as a computationally lightweight, plug-and-play enhancement module for existing
agent RL
algorithms, requiring no additional expert data, extra sampling costs, or inference-time overhead. We conduct systematic experiments on WebShop and ALFWorld, integrating foundation models of various scales with different policy optimization
algorithms. Empirical results demonstrate that TAPO consistently improves task performance over pure policy optimization baselines.
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All authors Cong Li, Peixi Peng, Yisen Zhao, Xinyu Hu, Shudong Liu, Zhan Su, Zhuojian Li
Artificial Intelligence
MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation
Dawei Wang, Di Zhao, Xinyuan Liu et al. (+5 more)
Credit assignment is a fundamental challenge in cooperative multi-
agent reinforcement learning, particularly in
embodied AI settings characterized by limited and delayed feedback as well as dynamically changing numbers of active
agents. We propose MARS-RA, a framework that reformulates credit assignment as a rank aggregation problem using contribution-based pairwise comparisons among
agents generated by large
multimodal models. This shift from absolute to relative estimation ensures robustness against noise and dynamic
agent participation, converting comparison results into contribution scores for potential-based reward shaping. We provide theoretical justification for the convergence and robustness of the proposed framework, and show that Shapley values can be used as an interpretive reference. Experimental results on challenging tasks of different types indicate that MARS-RA can guide
agents toward effective cooperation.
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All authors Dawei Wang, Di Zhao, Xinyuan Liu, Marci Chi Ma, Xiaoyang Liu, Chengming Zhou, Gary Ushaw, Richard Davison
Artificial Intelligence · Cross-listed from cs.SE
Specification-Guided Synthesis of Deadlock-Free Communication Protocol Refinements with Large Language Models
Yang Li, Ping Hou and Nobuko Yoshida
Ensuring behavioural correctness in communication protocols is a central challenge in distributed software systems, as subtle inconsistencies can lead to deadlocks. In such settings, protocol refinement - the safe substitution of a protocol that preserves correctness and compatibility with other components - is essential. Large
language models (LLMs) have demonstrated strong capabilities in code generation and program synthesis, yet lack mechanisms to reliably produce outputs with correct behaviour. Formal specification approaches, such as multiparty session types (MPST), offer rigorous guarantees, including deadlock freedom, but provide limited support for automatically constructing protocol refinements. In this paper, we present Syntropy, a framework for synthesising protocol refinements guided by MPST specifications and LLMs. It incorporates refinement constraints directly into the generation process, ensuring the generated variants satisfy these guarantees. Our comprehensive evaluation indicates that Syntropy achieves 95.6%-99.5% validity while maintaining high syntactic correctness, and produces diverse, non-trivial refinements across multiple LLMs.
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Artificial Intelligence · Cross-listed from cs.MA
$\Sigma$-Mem: An Online Reliability Memory for LLM-based Multi-Agent Systems
Peilin Feng, Suorong Yang and Soujanya Poria
Memory is central to long-horizon LLM
agents, yet existing
memory systems primarily preserve interaction content rather than modeling which
agents can be trusted and under what conditions. This limitation is particularly important in multi-
agent systems, where a central model may be unable to directly verify plausible or correlated peer responses. We introduce $\Sigma$-Mem, an online reliability
memory that records historical competence evidence for individual peers and peer relationship evidence across the peer set. Both forms of evidence are maintained as real symmetric states and updated from post-decision correctness feedback. By Weyl's inequality, the spectral change caused by each event-level update is bounded, enabling stable online adaptation without retraining the underlying models. $\Sigma$-Mem provides a general write-and-read interface: the same
memory can be used for residual steering of a central model, response-free peer routing, or reliability-weighted voting. Across five Qwen-family models, $\Sigma$-Mem adapts to counterfactual reliability shifts and generalizes to unseen peers and task domains. Direct
memory readouts also outperform majority voting and the best fixed peer over the full OOD evaluation set. Moreover, performance improves consistently as more correctness feedback becomes available, indicating that $\Sigma$-Mem progressively accumulates actionable reliability information. These results establish reliability
memory as a reusable foundation for adaptive coordination in LLM-based multi-
agent systems.
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Artificial Intelligence · Cross-listed from cs.DL
SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions
Jennifer D'Souza, Sameer Sadruddin, Anisa Rula et al. (+23 more)
Scientific processes are often described in heterogeneous article discourse, with details needed for comparison, reproducibility, reuse, and automation dispersed across prose, tables, figures, protocols, and supplementary files. We present the first release of SciSchema.org, a multidisciplinary collection of 16 expert-annotated schemas spanning Biology & Biotechnology, Materials & Chemistry, Imaging & Measurement, Physics, and Psychology. Each schema defines reusable fields for describing process instances, including inputs, outputs, materials, instruments or software, parameters, conditions, procedural steps, measurements, and provenance-related information. The schemas were created through a human-in-the-loop schema-mining workflow in which large
language models generated candidate structures from process specifications, scientific articles, and expert feedback, followed by domain-expert construction of final master schemas. The
dataset contains final schemas in JSON Schema and SHACL formats, intermediate model-generated schemas, expert-feedback records, source-paper metadata, community-development materials, and analysis scripts. Technical validation assessed schema structure, development provenance, expert review, and syntactic conformance. The collection supports structured annotation, metadata enrichment, scientific knowledge graphs, information extraction, semantic publishing, and cross-study comparison.
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All authors Jennifer D'Souza, Sameer Sadruddin, Anisa Rula, Ana Bossler, Andrés Fullana, Enric Bas, Syed Ather, Defne Circi, Anlan Chen, L. Catherine Brinson, Alyssa Columbus, George Demetriou, Dongjun Jeong, Tarun Kumar, Frank Krüger, Sascha Genehr, Kai Budde-Sagert, Anamaria Leonescu, Francesco Lodola, Chiara Florindi, Gagana Balasubramanya Murthy, Samson Oluwapelumi Olagbile, Nazia Riasat, Yan Sha, Kevin Shen, Shaokai Yang
Artificial Intelligence · Cross-listed from cs.CV
LAST: The Last Query Token Guides Visual Token Pruning for Edge-Cloud Collaborative MLLM Inference
Feng Yang, Xinrui Ju, Keyang Zhang et al. (+6 more)
Multimodal foundation models are reshaping edge-cloud visual intelligence from task-specific feature pipelines into
token-based interfaces, where edge devices encode visual inputs into
tokens for a general-purpose cloud MLLM. However, dense visual-
token sequences increase cloud-side inference costs. Existing
pruning methods mainly target centralized inference:
vision-driven methods can operate before cloud execution but are typically query-agnostic, whereas query-guided methods often rely on internal states of the target MLLM and cannot determine
token relevance before transmission. Compact guidance models offer an alternative, but existing designs may require costly
attention aggregation or auxiliary generation. We propose LAST, a training-free framework for query-dependent visual
token pruning in edge-cloud collaborative MLLM inference. LAST uses a compact edge-side VLM as a guidance proxy and derives a lightweight importance signal from the last query
token's
attention to visual
tokens. Under causal
attention, the last query
token can attend to the full visual sequence and the entire query context, enabling query-aware
pruning without cloud-model access, autoregressive generation, or costly aggregation over multiple query positions. LAST then retains a diverse set of query-relevant visual
tokens under a fixed
token budget. We evaluate LAST on 11
multimodal benchmarks under multiple
token budgets against
pruning methods with different guidance strategies. Experiments show that LAST consistently achieves the strongest performance, preserving 95.4% of the full-
token accuracy while retaining only 12.5% of the visual
tokens, with low edge-side selection overhead and reduced cloud-side computation.
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All authors Feng Yang, Xinrui Ju, Keyang Zhang, Xiandong Meng, Rongqun Lin, Howard Leung, Shiqi Wang, Haoliang Li, Chris Xing Tian
Artificial Intelligence · Cross-listed from cs.CR
Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs
Pingyu Wu, Lingyao Zhu, Weiming Zhang et al. (+1 more)
Large
language model safeguards decide whether to answer before seeing how an answer will be used. This creates a basic problem for dual-use tasks: the same answer can help an authorized professional or an attacker, while an attacker can imitate a benign request and interaction history. We separate the capability released by the model from the evidence available about downstream use. When that evidence is copyable, we derive the exact worst-case floor on attacker assistance while preserving useful answers. The result yields a safety trilemma: Useful Capability, Reliable Safety, and Open Access cannot coexist. We then show how a trusted credential can complement existing safeguards by adding hard-to-copy information that predicts actual downstream use, and identify the stronger condition needed to eliminate the floor. Evidence from dual-use evaluations, adaptive attacks, and deployed trusted-access programs supports the practical relevance of these conditions.
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All authors Pingyu Wu, Lingyao Zhu, Weiming Zhang, Nenghai Yu
Artificial Intelligence · Cross-listed from quant-ph
Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting
Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang et al. (+14 more)
Sequence models must decide what to write into
memory and what to retain. In
quantum and
quantum-inspired sequence learning, nonlinear recurrent updates often require repeated
circuit evaluations and sequential backpropagation through time, making long contexts costly. Gated fast-weight programmers (FWPs) based on
quantum-inspired Kolmogorov-Arnold networks (QKANs) alleviate this bottleneck by storing context in time-varying fast parameters. However, their scalar gate applies one retention-write balance to every fast-state coordinate, forcing all parameters to share a
memory timescale. We introduce Self-Modulating QKAN-based FWPs, which replace this broadcast gate with low-rank-generated element-wise modulation of the new-proposal branch, a bounded old-state branch, or both. We further propose Complementary
Matrix Gating (CMG), which uses one sigmoid
matrix gate to retain the old state and its complement to write the new proposal. CMG provides coordinate-wise
memory control while preserving the bounded convex update and affine prefix-scan structure of scalar gating, at the modulation-head cost of a single-branch rule. We compare four self-modulating rules with scalar gating across four FWP architectures combining classical and QKAN-based slow and fast programmers. Across seven single-step
forecasting benchmarks and five sequence lengths, CMG gives the most consistent improvements for architectures whose fast programmer incorporates a QKAN-based module. In direct multi-step
forecasting of Jaynes-Cummings and transmon-resonator dynamics simulated with CUDA-Q Dynamics, CMG models maintain mean-squared errors on the order of 0.001 or lower across
forecasting horizons of 4, 8, and 16 steps, while improving on their scalar-gated counterparts by at least 91.2%. These results establish coordinate-wise complementary modulation as a stable and effective update for QKAN-based FWPs.
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All authors Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang, Chen-Yu Liu, En-Jui Kuo, Yun-Yuan Wang, Tzung-Chi Huang, Prayag Tiwari, Chi-Sheng Chen, Chun-Hua Lin, Yu-Chao Hsu, Tai-Yue Li, Saif Al-Kuwari, Simon See, Kuan-Cheng Chen, Nan-Yow Chen, Hsi-Sheng Goan
Artificial Intelligence · Cross-listed from cs.IR
Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation
Long Zhang, Hao Jiang, Sheng Yu et al. (+3 more)
While large
language models (LLMs) have advanced ID-based recommendation through Semantic ID (SID) modeling, existing SID generation frameworks largely follow a single-representation-then-quantization paradigm. This design faces two bottlenecks: semantic
entanglement mixes heterogeneous attributes, such as geography, brand, and category, causing information loss during quantization, low-quality SIDs, and severe collisions; moreover, black-box representation learning provides neither explicit attribute semantics nor clear geographic or semantic meanings for SID positions. These limitations weaken both
retrieval reliability and the ability to diagnose or control SID generation. We propose Interpretable Representation via LLM-Driven Generative Disentanglement for Local-Life Service Recommendation (LGRID). LGRID introduces a generative disentanglement paradigm through an Encode -> Disentangle -> Align -> Quantize pipeline. It first uses joint LLM encoding to preserve cross-attribute geographic-semantic dependencies, rather than encoding fields independently. A Structured Disentangled Block then routes hidden states into attribute-aligned slots for geographic and semantic factors. Synergistic
Alignment Learning makes these slots both generatively decodable and discriminative for
retrieval, while Dual-Stream Residual Quantization separately discretizes the two streams into compact SIDs with explicit attribute correspondence. This design yields interpretable SIDs with positions grounded in item attributes and local-service semantics. Experiments on Kuaishou and Foursquare show that LGRID consistently outperforms strong SID baselines, achieving up to a 5.44 percent relative AUC gain. It also achieves over 99 percent attribute-decoding accuracy for coarse geographic fields and reduces the full-SID collision rate to 39.9 percent, compared with 97.0 percent for LGSID.
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All authors Long Zhang, Hao Jiang, Sheng Yu, Fei Pan, Peng Jiang, Kun Gai
Artificial Intelligence
From Scoring to Acting: Outcome-Verified Comparative Self-Distillation for LLM Agents
Xu Xia, Jinghua Piao, Min Yang et al. (+3 more)
Recent work on LLM
agents is shifting from external capability elicitation to capability internalization, enabling
agents to retain useful skills without
retrieval at inference time. On-policy self-
distillation (OPSD) offers a promising direction, but many existing methods typically supervise students by scoring actions along student-generated trajectories. Such supervision has two limitations: teacher preferences are not validated by environment outcomes, and action-level scores underuse information from student rollouts, teacher rollouts, and their behavioral relationship. We therefore advocate outcome-verified teacher supervision and comparative learning over teacher-student trajectories. Based on this view, we propose Outcome-Verified Comparative Self-
Distillation (OVCSD). OVCSD organizes failed student rollouts into a prefix tree, adaptively invokes a skill-conditioned teacher from student-reached states, and retains only outcome-verified successful continuations. It then applies localized comparative learning at the first state-aligned divergence and distills the post-divergence teacher suffix to transfer completion behavior. Experiments on ALFWorld and WebShop across three model scales show that OVCSD consistently outperforms skill-free RL and existing self-
distillation baselines, achieving up to 29.7 and 5.4 absolute success-rate gains over the strongest baselines on ALFWorld and WebShop, respectively, while adding less than 3% privileged interaction during training.
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All authors Xu Xia, Jinghua Piao, Min Yang, Xiaochong Lan, Jiaju Chen, Yong Li
Artificial Intelligence
Shapes from Examples: Foundations of Shape Learning in Recursive SHACL
Bente Gortworst, Cem Okulmus, Magdalena Ortiz et al. (+1 more)
SHACL shapes enable data graph validation, making automatic shape learning essential for knowledge graph applications. We investigate the well-known fitting approach to this task: given sets P and N of positive and negative example nodes from an input graph, compute a shape expression C, possibly using shape names defined in a recursive shape catalogue, that validates at every node in P and none in N. We focus on the case where C is written in a core fragment of SHACL corresponding to the Description Logic ELI*. For the catalogue, we consider the well-founded, stable, and supported semantics. We address fitting existence and most specific fitting computation, establish tight exponential-time upper bounds for both problems, and obtain
polynomial bounds for relevant special cases.
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All authors Bente Gortworst, Cem Okulmus, Magdalena Ortiz, Anni-Yasmin Turhan
Artificial Intelligence
The Geometry of Flow-Matching Uncertainty: A Cost-free Uncertainty Proxy and Its Application in Flow-based VLA Failure Detection
Ziyang Rao, Yiren Zhao, Weiyu Guo et al. (+3 more)
Flow matching (FM) has become a popular action head paradigm for modern
embodied models. However, as a conditional generative model, it does not explicitly expose its inherent uncertainty, producing faulty action chunks even when it misinterprets the scene or encounters out-of-distribution (OOD) inputs. Therefore, determining when an FM-generated action can be trusted is essential for safe deployment, yet existing uncertainty estimation methods on real-time control suffer from several issues: extra training budget, high computational overhead, and low generalization ability. In this work, we provide a
geometric interpretation of FM uncertainty in the velocity field, showing that uncertainty manifests as deviation from an ideal affine-isotropic contraction field. Building on this observation, we introduce denoising acceleration ($\mathrm{accel}$), a highly-generalizable and cost-free uncertainty proxy that measures the bending of the denoising trajectory from a single forward pass, without additional model evaluations, training, or resampling. We theoretically and empirically demonstrate that $\mathrm{accel}$ is a faithful proxy for FM uncertainty and further test its utility in online failure detection. Results show that $\mathrm{accel}$ identifies failing rollouts well before termination, matching or even outperforming costly resampling- and training-based baselines across settings under realistic deployment budget. Code and demos available at: https://github.com/rrrrrrzy/fm-geometry.
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All authors Ziyang Rao, Yiren Zhao, Weiyu Guo, Ben Fei, Yandong Guo, Hui Xiong
Artificial Intelligence
Meta-Task: Turning Terminal Task Synthesis into a Terminal Task for Scalable Agent Training
Zhihong Pan, Jiyuan He, Kai Zhang et al. (+5 more)
Training terminal
agents at scale requires diverse, verifiable terminal tasks and high-quality interaction trajectories, yet acquiring such data remains a significant challenge. Existing synthesis methods face two key limitations: (1) weak reliability caused by the disconnect between task generation and real execution, and (2) limited diversity and scalability due to dependence on existing repositories. We propose Meta-Task, a framework that redefines terminal task synthesis as a Terminal-Bench-format task itself: an
agent operates within a real container environment to iteratively generate, execute, and verify tasks, so that synthesized components are checked for internal consistency and executability within the generation loop itself. Building upon this, we decouple the target task requirements along multiple dimensions, introduce a multi-phase mechanism that dynamically designs novel task specifications before producing the actual tasks, and incorporate optional external material support to enhance diversity and realism. We additionally apply LLM-as-Judge filtering to ensure the quality of the final training data. Experiments on Terminal-Bench 2.0 show that fine-tuning on only 3,221 Meta-Task synthesized trajectories achieves 22.5% and 31.8% Avg Pass@1 for Qwen3-14B and Qwen3-32B respectively, outperforming concurrent approaches with significantly less training data.
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All authors Zhihong Pan, Jiyuan He, Kai Zhang, Yupeng Han, Ze Liu, Yuze Zhao, Yongcong Ye, Zhaohua Yang
Artificial Intelligence · Cross-listed from cs.CV
ARD-REFSM: Enhancing Reflection Symmetry Detection with Asymmetric Denoising and Rotation Equivariance
Dongfu Yin, Rourou Su, Cong Zhao et al. (+1 more)
Reflection
symmetry detection remains challenging due to interference from asymmetric regions and arbitrary orientations of symmetric patterns. Asymmetric regions introduce background clutter that disrupts symmetric pattern matching, whereas conventional convolutional neural networks lack rotation equivariance, leading to inconsistent feature representations under rotational transformations. To address these issues, we propose an Asymmetric Region Denoising (ARD) module and a Rotation Equivariant Feature Similarity Matching (REFSM) module. The ARD module suppresses asymmetric interference to refine symmetric patterns, while the REFSM module enhances rotation equivariance through feature similarity matching between original and rotated images. Specifically, our dual-input REFSM framework leverages rotation loss to maximize consistency between the score maps of original and rotated images, thereby enabling precise prediction of rotation-equivariant
symmetry axes. Furthermore, we introduce GMSYM, a new
benchmark dataset that categorizes images into diverse scenarios and incorporates various interferences to address the limitations of existing reflection
symmetry detection
benchmarks. Extensive experiments on four standard
datasets (DENDI, NYU, LDRS, SDRW) and our proposed GMSYM
dataset demonstrate that our method achieves state-of-the-art performance in both accuracy and robustness.
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All authors Dongfu Yin, Rourou Su, Cong Zhao, Fei Yu
Artificial Intelligence
Enyi Shi, Fei Shen, Chuancheng Shi et al. (+4 more)
As large
vision-
language models (LVLMs) are deployed globally, the combination of
multilingual instructions and visual information makes malicious attacks more covert and sophisticated than ever before. However, existing methods isolate
language and modality defenses, which, coupled with the scarcity of safety data and high fine-tuning costs, makes it difficult for models to defend against compound attacks. To address this severe challenge, we propose a
neuron-level cross-dimensional safety
alignment framework driven by modality- and
language-shared safety
neurons (MLS-
Neurons). First, we identify monolingual and unimodal safety
neurons by comparing responses to harmful and benign samples, quantifying functional saliency through activation strength and downstream impact. Then, by intersecting these unimodal
neurons within each
language, we extract modality-shared safety
neurons (MS-
Neurons) responsive to both visual and textual risks, bridging the safety representation gap between modalities. Furthermore, using English as a semantic anchor, we intersect MS-
Neurons across
languages to identify modality- and
language-shared safety
neurons (MLS-
Neurons), serving as key defenses against compound attacks. Finally, we update only this minimal subset of shared
neurons (~0.03% of parameters), transferring English-only safety supervision to
multilingual and
multimodal scenarios. Extensive experiments show that our method significantly outperforms state-of-the-art approaches across diverse
multilingual and
multimodal safety
benchmarks while preserving general utility.
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All authors Enyi Shi, Fei Shen, Chuancheng Shi, Linxia Zhu, Shuyi Miao, Jinhui Tang, Tat-Seng Chua
Artificial Intelligence
IFHierBench: Hierarchical Instruction Following for Large Language Models
Yuetian Mao and Chunyang Chen
Instruction-following ability is critical for deploying large
language models in real-world applications, where downstream components depend on the output satisfying specific constraints. Modern deployments increasingly handle the full task in a single LLM call, with one
prompt specifying a layered output whose overall artifact, structural sections, and nested fields must each satisfy concrete constraints. Existing instruction-following
benchmarks treat the constraint set as a flat list applied uniformly to the response, so they cannot scope a check to a particular section of the output. We introduce IFHierBench, a hierarchical instruction-following
benchmark of 600
prompts stratified across four constraint-tree depths and 35 distinct constraints, each
prompt paired with a deterministic checker that verifies satisfaction at every scope. Evaluating seven leading proprietary and open-weight models, we find that even the strongest model only marginally exceeds 50%
prompt-level accuracy and that accuracy degrades sharply as constraint depth grows. Reliably following nested constraints remains a substantial gap for current LLMs, motivating future training methods that consider constraint adherence at finer granularity to achieve better instruction-following ability.
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Artificial Intelligence
A Cross-Architecture Audit of Direction-Based Inference-Time Defences in Vision-Language Models
Xiangyu Yin, Tora Bodin, Rohan Menon et al. (+1 more)
Inference time defences against
vision language model jailbreaks often subtract a calibrated direction from the residual stream at a chosen decoder layer. We compare five defence candidates across 15 model and layer
cells from four architectural families under a magnitude controlled protocol that matches the intervention size for each
prompt and pairs every direction with a random control of the same norm. The candidates are the mean image conditioning shift, a CMRM style refusal direction, a ShiftDC style attack specific residual, a
prompt instruction to ignore the image, and a random control. No single candidate dominates on both refusal recovery and utility preservation. The image conditioning shift leads on LLaVA 1.5 and Pixtral 12B and is the only candidate whose utility loss remains at the measurement noise floor in every family. The
prompt instruction leads on Qwen2.5 VL, while the attack specific residual leads on Qwen2 VL 2B. The image conditioning direction is direction specific in 13 of 15
cells, but strongly architecture specific and nontransferable across the only dimension compatible pair, LLaVA 1.5 13B and Pixtral 12B. We also connect text only and
multimodal refusal geometry. The CMRM direction has positive cosine
alignment with the image conditioning shift in all 15
cells, with mean 0.35, range 0.17 to 0.65, 15 to 25 times the random vector null, and a sign test p value of about 3e-5. These results show that the two recipes recover partially overlapping geometry and that direction based defences should be calibrated separately for each
language decoder family.
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All authors Xiangyu Yin, Tora Bodin, Rohan Menon, Chih-Hong Cheng
Artificial Intelligence · Cross-listed from cs.LG
Class-Aware Reinforcement Learning for Counterfactual Explanation Generation
Muhammad Adil Saleem, Syed Ali Raza and Mary-Anne Williams
Counterfactual explanations (CFEs) enhance the interpretability of black-box models by generating alternative instances with adjusted feature values that achieve a contrastive outcome. Reinforcement learning (RL) offers a promising approach for CFE generation, enabling efficient exploration of counterfactual instances while ensuring control over key metrics like validity, sparsity, and proximity. Previous studies have formulated RL states exclusively using features derived from the predictors in the supervised
dataset. This study explores the impact of including an instance's predicted class, alongside features derived from the predictors, in the RL state representation for generating CFEs. The hypothesis is that class-awareness enhances exploration efficiency and improves policy optimality. We compare the proposed class-aware RL method with the class-blind RL method, which is similar but excludes the instance's class information from the state representation. The comparison was conducted using seven
datasets from diverse domains, varying in size. The results show that during training, class-aware RL offers benefits in terms of convergence speed, reward optimization, and episode length reduction. Moreover, it generates significantly more valid CFEs compared to class-blind RL. Finally, the instance's class-based feature consistently ranks among the most influential predictors in RL's action-selection, as shown by the SHAP and LIME values, underscoring the significance of class-awareness in RL for CFE generation. The impact is heightened clarity, faster learning, improved validity, and more effective counterfactual generation across diverse
datasets.
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Artificial Intelligence
MMHBench: A Multi-Perspective Benchmark for Mental Health Understanding in Long-Form Videos
Jinpeng Hu, Erqiang Wang, Shan Wang et al. (+4 more)
Mental health understanding in long-form
videos requires nuanced
reasoning over observable behavior, interpersonal context, and latent psychological states. Existing
benchmarks largely reduce this task to coarse-grained classification, providing limited insight into whether models truly understand psychological phenomena or rely on superficial correlations. To address this limitation, we introduce MMHBench, a comprehensive
multimodal benchmark for multi-perspective mental health understanding, comprising 268 long-form
videos and 2,184 carefully curated questions. MMHBench organizes the evaluation into two complementary settings: (1) third-person assessment, consisting of 605 questions that focus on the interpretation of observable behaviors and
multimodal evidence, and (2) first-person perspective-taking, comprising 1,579 questions that require perspective-conditioned
reasoning to identify the interpretation of the mental state supported by the available
multimodal evidence. We propose a Multi-
Agent Question Generation (MAQG) framework that simulates diverse social roles to synthesize questions from multiple perspectives. The generated questions are refined through multi-role feedback and iterative optimization, followed by expert-guided verification to ensure quality and validity. Extensive evaluation of 22 representative
multimodal large
language models (MLLMs), spanning both open-source and leading closed-source models, demonstrates that long-form
video mental health understanding remains highly challenging.
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All authors Jinpeng Hu, Erqiang Wang, Shan Wang, Zhuo Li, Peipei Song, Xun Yang, Meng Wang
Artificial Intelligence
Dynamic Spectral Filtering for Temporal Graph Learning: Learning Evolving Propagation Operators
Yan Kong
Temporal graph learning is commonly organized around the
evolution of node states or the encoding of interaction histories. We study an underexplored, operator-centric question: should the graph propagation mechanism itself evolve over time? We introduce Dynamic Spectral Filtering (DSF), which represents propagation at snapshot t by a Chebyshev
polynomial filter with vector-valued, time-dependent coefficients. DSF explicitly treats these compact multi-order coefficients as recurrent temporal states. A recurrent branch proposes updates, while multiplicative global and order-specific gates regulate their magnitude. The temporal state is independent of the number of nodes. On MOOC, Wikipedia, and Reddit temporal link-prediction
benchmarks, converged DSF runs attain AP scores of 0.7851, 0.9088, and 0.9860, respectively, with 93K to 133K trainable parameters, 68 to 182 MB peak GPU
memory, and 1.6 to 2.1 seconds of training per epoch. Against the closely related DEFT baseline, DSF is better on MOOC, within 0.001 AP on Reddit, and modestly lower on Wikipedia, while using 8.3 to 8.6 times fewer parameters, 25 to 33 times less GPU
memory, and 5 to 19 times less time per epoch. Relative to all measured alternatives, it uses 3.3 to 38.6 times less GPU
memory. These results support direct spectral-response
evolution as a useful temporal inductive bias when computational efficiency is a first-class requirement.
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Artificial Intelligence
Not All Tokens Deserve Equal Credit: Counterfactual Sensitivity Credit Reallocation for Long-CoT Reasoning
Qiangqiang He, Zhongheng Wu and ZiJian Wang
Reinforcement learning with verifiable rewards (RLVR) is central to improving long-CoT
reasoning in large
language models. Critic-free methods such as GRPO convert response-level rewards into advantages and uniformly broadcast them across
tokens, overlooking their unequal contributions to the final outcome. On-policy self-
distillation (OPSD) instead provides dense distributional supervision by minimizing the forward KL divergence between an unprivileged policy and a privileged self-teacher, implicitly assuming that the resulting likelihood shifts encode reliable answer-aligned information. We test this premise by fixing each sampled trajectory and re-scoring it under two opposing outcome conditions, one asserting correctness and the other incorrectness. Most affected
tokens shift in the same direction under both conditions, with few sign reversals and substantial overlap in the induced optimization signals. Large shifts also concentrate on highly substitutable surface-form
tokens, whereas
tokens carrying problem-specific
reasoning content are less sensitive. These findings show that privileged shifts fail to provide reliable answer-aligned directions, while their magnitudes primarily reflect counterfactual sensitivity rather than
token-level learning value. Based on these observations, we propose Counterfactual Sensitivity Credit Reallocation (CSCR), a simple extension of GRPO that reduces credit for highly sensitive
tokens and renormalizes
token-level advantages to preserve both the original credit budget and verifier-determined direction. On long-CoT mathematical
reasoning benchmarks, CSCR consistently outperforms GRPO baseline with the same number of policy updates. Targeted ablations further corroborate our diagnosis: privilege-induced directions are unreliable, moderate downweighting is most effective, and stronger modulation destabilizes optimization.
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Artificial Intelligence · Cross-listed from cs.RO
RoboBRIDGE: A Modular Framework for Bridging Policies to Robust Real-World Robotic Agents
Sihyung Yoon, Minjong Yoo, Sanghyun Ahn et al. (+2 more)
Vision-
Language-Action (VLA) models have attracted growing interest as a scalable approach to robotic manipulation. While these models are effective action predictors, deploying them as robotic
agents exposes critical gaps: no mechanism for failure recovery, inconsistent execution over long horizons, and limited robustness to shifts in observations, tasks, or embodiments. Existing solutions address these limitations individually through model retraining or environment-specific modules, yet what is needed is a general framework that systematically transforms a pretrained VLA into a robotic
agent. We present RoboBRIDGE, a modular framework that provides an orchestration layer over five coordinated modules, namely Monitor, Perceptor, Planner, Controller, and
Robot Interface, to compose robust robotic
agents from off-the-shelf components, including pretrained VLAs. The Monitor pairs rapid failure detection with hierarchical recovery to correct errors before they cascade. When the environment diverges from the current plan, the Planner triggers replanning while the Perceptor updates scene understanding asynchronously, avoiding execution stalls. Within the Controller, primitive skill fine-tuning factors manipulation into domain-invariant primitives with dedicated LoRA adapters, reducing sensitivity to domain shifts when a VLA is used. Across LIBERO, RoboCasa, and real-world case studies spanning multiple
robot platforms and VLA backbones, RoboBRIDGE consistently outperforms both standalone policies and prior augmented VLA deployments. These results suggest that reliable robotic agency does not arise from scaling action predictors alone, but from structured orchestration around them.
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All authors Sihyung Yoon, Minjong Yoo, Sanghyun Ahn, Seojeong Choi, Honguk Woo