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新论文 · v1arXiv:2611.03091

KlaskTron-IL: A Contact-Rich Adversarial Imitation Learning Benchmark with a Validated Sim-to-Real Bridge

Nicklas Sohm, Aswin Ramachandran, Raffaello D'Andrea

原文摘要

KlaskTron is an open-hardware physical platform for two-player adversarial reinforcement learning, with a learned actuator model that supports zero-shot sim-to-real transfer of trained policies. Building on this platform, we contribute KlaskTron-IL: a multi-tier imitation learning dataset (80k human demonstration timesteps collected directly on the physical robot, 320k symmetry-augmented timesteps, and synthetic expert trajectories up to 5M steps generated by the zero-shot-validated PPO policy), and diagnostic baselines that expose three characteristic failure modes of standard IL methods under contact-rich adversarial dynamics: BC's data scaling plateau, GAIL's interpretable but weak-gradient discriminator (Spearman $ρ= -1.0$ peg-ball proximity recovery, but a logit span under 0.1), and an asymmetric BC-prior x reward-density interaction. Because the policy interface used throughout this paper is the same one the actuator model bridges to hardware, a policy trained on this benchmark can run unchanged on the physical robot, as we demonstrate zero-shot for the strongest BC baseline, so long as its commands respect the actuator model's $\pm 0.2$ m/s identification range.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03087

MIRA: Reinforcing Multimodal Reasoning via Deceptive Contextual Augmentation

Zhihan Yin, Jianxin Liang, Yifeng Yao, Nonghai Zhang, and Bingyue Peng, Chunguang Qie, Huishuai Zhang, Dongyan Zhao

原文摘要

While Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced multimodal reasoning, existing frameworks suffer from a critical "perception-reasoning gap." Due to an over-reliance on seemingly relevant visual tokens, even minor perceptual perturbations can propagate through the reasoning process, leading to compounding hallucinations. To address this issue, we propose MIRA, a training framework that improves robustness to erroneous visual contexts while enhancing logical reasoning ability. By injecting filtered, deceptive visual contexts during training, we construct challenging "hard traps" that stress-test the model's robustness to misleading information. Furthermore, we introduce a group-wise reflection-triggering mechanism that enables the model to autonomously detect and rectify inconsistencies between visual cues and logical constraints, which is ultimately internalized as an intrinsic policy behavior. Importantly, MIRA does not rely on human annotations. Experimental results across multiple reasoning and perception benchmarks show that MIRA yields an average 7% improvement on in-domain benchmarks, with consistent improvements on vision-centric benchmarks. These findings highlight that active error recovery is essential for robust multimodal reasoning. Comprehensive ablation studies and analyses further provide insights into why MIRA is effective.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41289

PsyInterviewee: Informational Invariance and Relational Adaptivity in Mental Health Interview Simulation

Jiawei Zhang, Huachuan Qiu, Ruocheng Guo, Zijun Shi

原文摘要

LLM-based simulated interviewees support interviewer training and the evaluation of mental health interviewing systems. Existing studies have primarily focused on improving human-likeness through richer patient profiles and behavioral modeling. However, human-likeness alone does not establish their reliability. Reliable simulation requires preserving profile-grounded clinical facts (informational invariance) while adapting disclosure and interaction behavior to the interview (relational adaptivity). To this end, we introduce PsyInterviewee, which separates a protected truth channel from a dynamic interaction channel and verifies generated clinical claims against the profile. Specifically, asymmetric write permissions allow dialogue context to update disclosure and behavioral states without authorizing new clinical facts; these states guide what profile-supported information is revealed and how it is expressed. In an automated evaluation of interactions between LLM-based interviewers and interviewees, PsyInterviewee achieves higher scores than four representative baselines on most metrics across both axes, as assessed by two LLM judges. Ablations suggest that the two channels contribute to clinical grounding and response modulation, respectively.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03080

VL-Dedup: Graded Multimodal Entity Resolution for Image-Text Data Deduplication

Zhenxi Liu, Guanze Chen, Dehui Yu, Xingyu Wang, Bingxu Hou, Chengwei Li, Tao Ma, Ke Wang 等 9 位作者

原文摘要

Image-text corpora contain copies of one image, captions that describe one image in different ways, captions reused for different images, and captions that do not match their images. Each case calls for a different curation action. Deduplication and multimodal entity resolution (ER) only decide whether records are the same, and they are evaluated on labeled pairs. Yet pair scores do not tell which records end up in the same cluster or which useful captions get deleted. We therefore formulate image-text deduplication as graded multimodal ER, which decides visual identity, caption handling, and mismatches for each pair, and we evaluate it at three levels: pairs, clusters, and the retained set. Our method, VL-Dedup, groups records of the same visual entity into clusters and caps the size of each cluster. Within each cluster, a vision-language model (VLM) and CLIPScore decide which captions to keep and which to flag. We also build VL-DedupBench, with 50,000 COCO and SOP records and 66,788 labeled pairs. On it, a classifier that selects no false edge among the labeled pairs still joins millions of record pairs from different entities. Spending VLM calls on the pairs inside clusters, rather than on pairs with uncertain scores, gives better caption decisions from the same model. Even when almost all clusters are correct, keeping one record per cluster loses facts in many deletions, while the deletions of VL-Dedup lose much less information.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03759

JevBreak: Your Model, Their Decision

Nitin Aravind Birur, Divyanshu Kumar, Sahil Agarwal, Prashanth Harshangi

原文摘要

Restricting what a model can say is often mistaken for restricting an attacker's influence over its decision. An attacker can add text to the evidence and steer the model toward a wrong answer that still belongs to the allowed set. We evaluate seven decision models spanning hosted APIs, open-weight models and an encoder on 16 tasks in seven domains from Decision Bench, targeting every wrong option across 240 text examples (990 targets per model). We present Slot Attack, a black-box attack designed for decision models that appends text assembled from named components to steer predictions toward an attacker-selected incorrect option. The method relies solely on returned label probabilities, achieves success rates of 32--99\% on initially correct decisions, and identifies the effective component combinations with a median of 17--72 queries per successful attack. Accuracy and confidence do not reliably reveal this vulnerability. Models with near-identical accuracy on the sampled tasks differ by about 20\%age points in attack success. We demonstrate that an attacker can force not just one wrong answer but every available one: on about 20\% of correct decisions for the JEV models, 77\% for gpt-6-luna and 98\% for GLiNER2.5; a confidence threshold does not reliably screen the attack out. A fixed answer set constrains a decision's form, not its source; robustness must be measured per model, per task and per version rather than assumed from output format.

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03078

Cross-Layer Refinement and Frequency-aware Fusion Network for Multi-Source Remote Sensing Image Classification

Wenhan Xiao, Chuanzheng Gong, Feng Gao, Junyu Dong, Qian Du

原文摘要

Multi-source remote sensing image classification benefits from complementary spectral and structural information. However, identifying discriminative cross-modal features remains challenging. This letter proposes a Cross-Layer Refinement and Frequency-aware Fusion Network (CRFNet) within a lightweight dual-branch architecture. Specifically, a Cross-Layer Refinement Attention (CLRA) module propagates selected attention relationships across successive layers and employs top relation selection to progressively strengthen discriminative spatial dependencies while suppressing irrelevant responses. Furthermore, a Frequency-aware Complementary Fusion (FCF) module combines frequency-wise interactions with cross-modal channel attention in the Fourier domain. The resulting representations are then converted into spatial-domain gates to selectively modulate complementary features. Experiments on the Houston 2013 and Augsburg datasets demonstrate that the proposed CRFNet consistently outperforms several state-of-the-art methods. The codes are publicly available at https://github.com/oucailab/CRFNet

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.00621

Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era

Shubhashis Sengupta, Benjamin McCarty, Milind Savagaonkar, Rhine Andotra

原文摘要

Generative artificial intelligence has fundamentally changed how content is now produced. It has enabled how high-fidelity text, images, audio, and videos are created, modified, and redistributed at near-zero marginal cost. This shift exposes enterprises and ecosystems to a number of risks across four reinforcing authenticity layers -- authenticity, provenance, integrity, and accountability -- that traditional controls are inadequate to address in isolation. We introduce the concept of authenticity debt: the cumulative institutional liability that accumulates when organizations deploy AI-generated content without preserving verifiable origin, integrity, and accountability, deferring exposure that surfaces under regulatory, legal, or market scrutiny. This paper presents a comprehensive, multi-dimensional taxonomy of generative AI harms and attack vectors, surveys the capabilities and failure modes of technical controls including digital watermarking, provenance frameworks (C2PA, Adobe CAI), and detection technologies, and argues that no single mechanism is sufficient in open, adversarial, and evolving environments. Drawing on Zero Trust Architecture principles and enterprise governance frameworks, we propose a layered reference architecture that integrates cryptographic provenance, human-in-the-loop verification, and continuous governance to sustain defensible authenticity at scale. We further examine the regulatory landscape (EU AI Act, U.S.\ FTC, NIST AI RMF) and identify practical guiding principles for organizations seeking to build authenticity as institutional infrastructure rather than an afterthought.

cs.CR · cs.AI · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.29843

The Price of Intelligence: A Quality-Adjusted Price Index for AI Services

Louis Yiven Zhu

原文摘要

How fast AI inference prices have fallen depends on how an index holds quality constant. We build quality-adjusted price indices for text and code inference sold through APIs, joining 21,024 posted prices to a capability scale fitted to 4,605 benchmark scores. From January 2025 to July 2026, matched-model methods record an annual fall of 0.07 in log price per token. Our pre-specified hedonic index falls at 0.51 a year in logs (95% interval 0.11 to 0.89), entirely through the rising capability of the models on sale. Across three treatments of measurement error and two anchor sets it runs from 0.34 to 0.89. Excluding six anchors flagged for possible contamination leaves model rankings intact (rank correlation 0.998) yet lowers the rate by 0.40 to 0.51 under two treatments. Agreement on rankings therefore does not license claims about levels or increments. Per completed task, the data do not establish a quality-adjusted decline.

econ.GN · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.11132

WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference

Zixun Xiong, Gaoyi Wu, Lingfeng Yao, Miao Pan, Xiaojiang Du, Hao Wang

原文摘要

Communication topology is a critical factor in the utility and safety of LLM-based multi-agent systems (LLM-MAS), making it a high-value intellectual property (IP) whose confidentiality remains insufficiently studied. Existing topology inference attempts rely on impractical assumptions, including control over the administrative agent and direct identity queries via jailbreaks, which are easily defeated by basic keyword-based defenses. As a result, prior analyses fail to capture the real-world threat of such attacks. To bridge this realism gap, we propose \textit{WebWeaver}, an attack framework that infers the complete LLM-MAS topology by compromising only a single arbitrary agent instead of the administrative agent. Unlike prior approaches, WebWeaver relies solely on agent contexts rather than agent IDs, enabling significantly stealthier inference. WebWeaver further introduces a new covert jailbreak-based mechanism and a novel fully jailbreak-free diffusion design to handle cases where jailbreaks fail. Additionally, we address a key challenge in diffusion-based inference by proposing a masking strategy that preserves known topology during diffusion, with theoretical guarantees of correctness. Extensive experiments show that WebWeaver substantially outperforms state-of-the-art (SOTA) baselines, achieving about 60\% higher inference accuracy under active defenses with negligible overhead.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2506.11796

Solving Stochastic Inverse Problems in Embedding Space

Elias Najarro, Nicolas Bessone, Sebastian Risi

原文摘要

Solving an inverse problem consists in finding the unknown parameters of a system from observations. This task is particularly challenging when observations have a strong stochastic component, producing diverse yet equivalent patterns. Self-organizing systems are a notable case: simple local rules can generate complex stochastic patterns. Many natural systems rely on such processes, making self-organization central to understanding natural phenomena. Inverse methods based on direct comparison of observed values struggle in this setting, as such comparisons cannot capture feature similarities between variable outcomes; as a result, the loss fails to meaningfully inform the optimizer. In this work, we show that the difficulty posed by stochasticity in observable patterns can be overcome by reformulating the inverse problem in an embedding space. By mapping stochastic patterns onto an invariant embedding space, where equivalent patterns have similar representations, we can effectively recover unknown causal parameters without the need for handcrafted objective functions or heuristics. We show that embedding models trained on large image datasets produce robust representations that capture these invariances. We evaluate the method on three self-organizing systems: a physical, a biological, and a social one; namely, a reaction-diffusion system, a model of embryonic development, and an agent-based model of social segregation. We show that the method reliably recovers parameters despite stochasticity in the pattern outcomes. We further apply the method to real biological patterns, highlighting its potential as a tool for both theorists and experimentalists to investigate the dynamics underlying complex stochastic pattern formation.

nlin.AO · cond-mat.dis-nn · cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03069

Shared-Pattern Collaborative Network for Multi-Source Remote Sensing Data Classification

Chenfang Yang, Chuanzheng Gong, Feng Gao, Junyu Dong, Qian Du

原文摘要

Multi-source remote sensing data classification has received growing attention recently. However, existing methods still struggle to jointly model long-range contextual dependencies and shared spatial patterns across heterogeneous modalities. To address this issue, we propose a Shared-Pattern Collaborative Network (SPCNet) for HSI and SAR/LiDAR joint classification. To enhance cross-modal collaborative learning, Shared-Pattern Collaborative Block (SPCB) is designed by introducing a shared large-kernel operator and a dynamic convolution branch, enabling the network to capture common spatial structures and content-adaptive local features. Furthermore, Region-Adaptive Modality Allocator (RAMA) is developed to assign spatially varying modality weights for reliable feature fusion. Experiments on Houston2013, Augsburg, and Houston2018 datasets demonstrate that SPCNet achieves superior classification performance over several state-of-the-art methods. The code will be made publicly available at https://github.com/oucailab/SPCNet

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03068

PACERec: Progressive Adaptation of LLM Item Embeddings with Collaborative Evidence for Recommendation

Junzhao Huang, Xi Wu, Zihan Wang, Zijing Wang, Ming Wang, Kang Liu, Xiaocui Yang, Shi Feng 等 12 位作者

原文摘要

Large language model (LLM) embeddings have been increasingly adopted in sequential recommendation to encode static item attributes and alleviate the limitations of item ID embeddings under sparse interactions. However, these embeddings typically capture only static content similarity while neglecting sequential transitions, and since their adaptation quality relies on sparse per-item updates or global geometric calibration, existing methods still struggle to explicitly transfer preceding interaction contexts from frequent to long-tail items under unevenly distributed behavioral evidence, limiting recommendation effectiveness across the catalog. To address this challenge, we propose PACERec, an ID-free progressive adaptation framework that incorporates preceding interaction contexts into semantic representation adaptation by learning a shared velocity field from items with sufficient interactions via frequency-weighted flow matching. In this way, PACERec progressively guides static LLM embeddings toward collaborative behavioral targets while preserving original item semantics, overcoming sparse per-item updates to effectively generalize sequential transition patterns to long-tail items for both user modeling and candidate scoring. Extensive experiments on three real-world datasets across three sequential backbones demonstrate the superiority of our method over competitive baselines in both overall and long-tail recommendation.

cs.IR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.08290

Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time

Kalana Kalupahana, Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi

原文摘要

We design the first regret guarantees for robust dynamic pricing that decouple the dependence on the corruption $C$ and the time horizon $T$. In dynamic pricing, a seller with unlimited supply of a good interacts with a stream of buyers over \( T \) rounds, with the goal of maximizing revenue. At each round $t$, the seller posts a price $p_t$, and the buyer purchases the good only if their unknown valuation $v^\star$ exceeds this price. The seller observes only the binary feedback $\mathbb{I} \left\{ p_t \leq v^\star \right\}$, indicating whether a sale occurred. In the \emph{robust} pricing setting, a malicious adversary is allowed to corrupt this feedback in at most $C$ rounds. Even if the learner knows the corruption $C$, the best known regret bound is $\mathcal{O}(C\log\log T)$ by Gupta et al. [2025]. This leaves as an open problem to ``decouple'' the dependence on $C$ and $T$. In this work, we resolve this open problem. In particular, we develop a robust variant of binary search that achieves regret $\mathcal{O}(C+\log T)$ when the corruption $C$ is known and $\mathcal{O}(C+\log^2 T)$ when the corruption is unknown.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03067

PlanContext: Benchmarking In-Context Driving Decision-Making

Ruikai Li, Naiyu Gao, Hao Shan, Yizhe Fan, Yanhao Wu, Han Jiang, Yilong Ren, Zhenning Li 等 14 位作者

原文摘要

Real-world driving requires context-aware decision-making. Existing end-to-end driving benchmarks primarily assess trajectory quality through safety, compliance, comfort, and driving progress, leaving context-dependent decision-making insufficiently characterized. To fill this gap, we present PlanContext, a benchmark for evaluating in-context driving decision-making. PlanContext constructs paired evaluations by varying high-level driving intentions while keeping the observed scene fixed, enabling a natural and controllable assessment of context-dependent decision-making. We further define six representative driving contexts and jointly evaluate contextual decision correctness, road compliance, and dynamic safety with class-balanced metrics. Experiments on 9,561 scene-condition instances from NAVSIM and 23 public end-to-end driving models show that high conventional driving-quality scores do not consistently correspond to strong route-level in-context driving decision-making. Further analysis shows that model behavior varies with decision timing, road structure, and traffic interaction depending on the decision required by the current context. Our results highlight in-context driving decision-making as an important complementary dimension for evaluating end-to-end driving systems.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03066

Towards a Trustworthy and Governable Internet of Agentics: An Architectural Perspective on Protocols, Systems, and Services

Haoyi Xiong, Yi Liu, Jiang Bian, Peng Diao, Bin Zhu, Tingting Hu, Yi Wang, Sumi Helal

原文摘要

Large language model applications are increasingly being organized as tool-using and multi-agent workflows rather than as isolated chat interfaces. This perspective paper examines the architectural problem that follows from that shift: agents may reason, invoke tools, and delegate work, but dependable deployment still requires interoperability across frameworks, runtimes, data boundaries, and organizational control domains. We study this problem through the lens of the \emph{Internet of Agentics} (IoA), an emerging service-computing view in which heterogeneous agents are treated as governable service participants rather than only as stand-alone model endpoints. The paper organizes the landscape as a three-layer progression: from today's fragmented agent ecosystem, to interoperable agents, and ultimately to trustworthy and governable autonomy. First, it examines the protocol stack centered on the Agentic AI Foundation, including MCP for agent--tool interaction; A2A for agent--agent coordination; AGENTS.md and Skills-style procedural manifests; and discovery, transport, and trust mechanisms such as AGNTCY and SLIM. Second, it reviews the systems abstractions that make agents deployable as service components, including logical agent architectures, orchestration patterns, gateways, registries, runtime control loops, and hardware-aware inference infrastructure. Third, it situates these developments within services computing, arguing that agentic systems reinterpret classical concerns from SOA and microservices---discovery, composition, governance, pricing, and trust---at the level of cognitive capabilities rather than deterministic functions.

cs.AI · cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.04135

LLMberjack: Guided Trimming of Debate Trees for Multi-Party Conversation Creation

Leonardo Bottona, Nicolò Penzo, Bruno Lepri, Marco Guerini, Sara Tonelli

原文摘要

Online debates on platforms like Reddit or Kialo are structured as reply trees: each message can receive several replies, which in turn can each receive several replies, producing a branching structure rather than a linear conversation. Turning one path (or a mix of paths) through such a tree into a natural, turn-by-turn conversation among multiple speakers (a process we call linearization) requires deciding which messages to keep, in what order, and who each message is addressed to. To this aim, we present LLMberjack, a platform for creating multi-party conversations starting from existing debates, originally structured as reply trees. The system offers an interactive interface that visualizes discussion trees and enables users to construct coherent linearized dialogue sequences while preserving participant identity and discourse relations. It integrates optional large language model (LLM) assistance to support automatic editing of the messages and speakers' descriptions. We provide preliminary evidence about the platform's utility by showing how tree visualization facilitates the creation of coherent, meaningful conversation threads and how LLM support enhances output quality while reducing human effort. The tool is open-source and designed to promote transparent and reproducible workflows to create multi-party conversations, addressing a lack of resources of this type.

cs.CL · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03063

Networked AI: Architecture, Theory, and Applications

Liang Song, Jiayue Jin, Peng Sun, Di Li, Yang Liu, Athanassios Skodras, Dimitris Ampeliotis, Yong Yuan 等 12 位作者

原文摘要

Recent advances in Artificial Intelligence (AI), particularly large-scale deep learning, have been driven by monolithic or offline training on massive curated datasets. While successful, these approaches face structural limitations: escalating data and computational demands, limited online adaptability, and a lack of system-level intelligence in open, non-stationary environments. This paper presents Networked AI - a networked intelligent system composed of interacting, heterogeneous, and continuously evolving AI agents. We establish a hierarchical layered architecture comprising an Agent Layer for heterogeneous capability construction, an Interaction Layer for signal-processing-guided online data extraction, and a Regulation Layer for stability-constrained collective evolution. Foundations in data science, signal processing, and system control are hereby synthesized into a unified theoretical framework of Networked AI, providing superior data efficiency, intelligence efficiency, interpretability, and scalability. Theoretical and experimental analysis demonstrates that Networked AI can approach near-linear scaling in resource demand, in contrast to the exponential increase in standalone end-to-end systems. It provides not only online adaptation to out-of-distribution environmental novelties, but also the autonomous evolution towards application-specific intelligence. Application frontiers are discussed for IoT spaces, embodied, spatial, and industrial intelligences, as well as the cellular infrastructure and beyond. Key open challenges are identified towards realizing locally autonomous, collectively intelligent, continuously evolving, globally regulated Networked AI.

cs.AI · cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21312

Spectrum Is Not Enough: A Random-Matrix Account of Data-Free Preconditioning for Differentially Private Learning

Shilin Zhang, Kangning Wang

原文摘要

At a fixed privacy budget, curvature is exactly the object the budget makes unavailable. A recent answer estimates it from synthetic probes, at neither privacy cost nor distribution shift, and validates a probe by the eigenvalue decay it reproduces. That test reads the spectrum, not the basis, and what a probe reconstructs is unknown. This paper gives a random-matrix account of data-free preconditioning, organized by one dichotomy. Of the two Kronecker factors, the error-signal factor $ G $ and the architecture-determined bulk of the activation factor $ A $ are reconstructible without data up to an explicit error-energy bound; only the data-induced spikes of $ A $ are not. We verify that bound with zero violations along a full training trajectory, though it stays one to nearly three orders of magnitude above the gap it bounds. We locate the failure boundary at a Baik-Ben Arous-Péché threshold: a bulk-only probe suffers an exact damage $s^2/(1-s^2)$ from a spike of strength $s^2$, invisible once its amplitude exceeds the scaled-bulk threshold $( \sqrt {c}/(1+ \sqrt {c}))^{1/2}$, $c = d/n$. The test in use is basis-blind: rotating only the probe eigenbasis at a near-exact matched spectrum inflates the residual by $2.3 \times $ to $89.8 \times $, and a closed-form volume law fixes the within-spectrum ordering of the critical volume while a two-oracle construction proves that no functional of the probe spectrum fixes its magnitude. Over forty seeds on three modalities, per-layer tuning does not transfer to accuracy, no data-free arm beats the flat probe, public-data curvature does on both modalities tested, and the ordering inverts on PathMNIST under real DP noise. The account yields a probe-design protocol and falsifiable predictions for the regimes where data-free curvature can be trusted.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03062

MuCHMDPoser: Multiview RGB-Camera Full-Body Pose Tracking for Virtual Reality

Antoine Maiorca, George Fletcher, Thierry Ravet, Thierry Dutoit

原文摘要

Accurate full-body tracking in Virtual Reality (VR) is heavily underconstrained when relying solely on consumer-grade sparse sensors, such as Head-Mounted Displays (HMDs) and handheld controllers. Indeed, the self-avatar full-body pose must be inferred based on the 3 available upper-body joints tracked by these devices. This leads to significant tracking error, especially in lower-body pose. To overcome this issue, we propose to extend the tracking setup with external multiview RGB cameras. We show that integrating RGB-based pose estimation this framework is able to significantly decrease lower-body tracking errors, even under common artifacts in RGB-based estimates. Additionally, we evaluate a only-once HMD-camera calibration procedure that does not require any physical markers. We finally propose an implementation of the overall framework, achieving an end-to-end animation at 60 FPS on a Nvidia RTX3080 GPU. Codes are available at https://github.com/antmaio/MuCHMDPoser.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.12899

Physical-State-Guided Diffusion Sampling for Full-Waveform Inversion

Chen Min, Haowen Jiang, Zheng Ma, Xiong-Bin Yan

原文摘要

Full waveform inversion (FWI) reconstructs subsurface velocity models from seismic mea surements but faces the challenges of ill-posedness and nonlinearity, making accurate inversion dependent on suitable initialization and effective prior constraints. Classical methods stabi lize the inversion and improve convergence by designing hand-crafted regularization terms, yet these terms provide limited descriptions of complex geological structures. Recent diffusion based methods learn more detailed structural priors from data and integrate them into inverse problem solvers. We propose Physical-State-Guided Diffusion Sampling (PSG), which decouples diffusion posterior sampling into two mutually interacting processes: regularized FWI optimiza tion and conditional diffusion sampling with linear guidance. PSG shows overall improvements in reconstruction accuracy, preservation of prior structure, and robustness over classical and diffusion-based baselines across diverse OpenFWI inversion scenarios. Through domain decom position, PSG can generalize without retraining to more complex, realistic velocity fields with structural patterns distinct from those represented by the prior learned from training datasets and larger spatial domains than the training models. Overall, PSG offers a flexible framework for integrating learned priors with physical constraints, with potential for extension to other nonlinear inverse problems.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03060

SAGE (Subjectivity-Aware Generation Estimator): A Linguistic Framework for AI-Generated Review Detection *

Gonzalo Freijedo Aduna, Charles Yaiche, Patrick Bas, Alda Mari

原文摘要

We propose a simple, linguistically motivated approach to detecting AI-generated peer reviews based on the inherently human trait of subjectivity. We capture subjectivity at two levels: the semantic level of evaluative language and modality and the pragmatic level of speech acts. Using features derived from speech act annotations and model-predicted subjectivity scores, we train a lightweight classifier that achieves an AUROC of 0.9566 with only these linguistic features. Combining its output with the state-of-the-art detector Sem-Detect raises the TPR at 1% FPR of Sem-Detect on the APRDB benchmark from 0.874 to 0.985, outperforming the current state of the art. Our results show that linguistic features of subjectivity provide an explainable source of information for detecting AI-generated peer reviews and can be readily integrated into existing detection methods. The code and models are publicly available on GitHub (https://github.com/Arys02/generated\_ text\_detection\_hub) and Hugging Face (https://huggingface.co/Arys02), respectively.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03059

BeyondContact: A Reinforcement Learning Framework for Airflow-Based Non-Contact Manipulation

Peiyu Yang, Mohamad S. Moudallal, Antonio Langella, Giulio Turrisi, Victor Barasuol, Claudio Semini

原文摘要

Contact-based robotic manipulation is constrained by end-effector reachability and the mechanical properties of objects. Airflow can exert forces without direct robot-object contact, enabling manipulation of objects beyond the reach or when direct contact is undesirable. However, the lack of suitable simulation has hindered the application of reinforcement learning to airflow-based manipulation, limiting the broader adoption of this mechanism compared with contact-based methods. To address this gap, we developed a robot- and task-agnostic airflow extension for Isaac Lab that supports massively parallel training. The extension uses experimentally identified airflow models to compute the forces and torques exerted on objects. Based on this extension, we train a hybrid push-blow (HPB) policy that combines contact-based pushing with airflow manipulation and a rectangular reorientation (RR) policy that controls a cuboid's heading using airflow alone. To enable physical execution, we design a lightweight, high-speed blower that coexists with a gripper. In zero-shot real-world deployment, HPB achieves an 80% success rate over 20 trials, while RR achieves a mean absolute heading error of 2.6 degrees across four target headings. The project materials are available at https://github.com/iit-DLSLab/BeyondContact.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03055

PRIME: Predictive Routing of Interventions by Modeling Effects for Scientific Ideation

Fengxian Ji, Xie Yu, Zhexuan Cui, Yuke Li, Zirui Song, Fan Zhang, Qian Jiang, Jingpu Yang 等 11 位作者

原文摘要

Feedback-driven iteration is widely used to improve LLM-generated scientific ideas, but existing systems either leave revision decisions implicit in generation, align revisions with feedback-localized weaknesses, or restrict action selection to system-specific operations. This is problematic because problems, hypotheses, and solutions are interdependent: the location where a weakness manifests does not itself determine the revision scope that will produce a better successor. We term this phenomenon diagnosis-intervention mismatch. We introduce Predictive Routing of Interventions by Modeling Effects (PRIME), a plug-and-play feedback-to-revision decision layer for existing scientific ideators. PRIME operates in three stages: (1) Structural Attribution interprets feedback over the problem-hypothesis-solution structure without fixing a revision target; (2) Counterfactual Routing constructs alternative scope-conditioned interventions and compares their projected successor utility and risks through lightweight rollouts; and (3) Intervention Execution compiles the selected intervention into an ordered generator-facing plan with semantic actions, preservation constraints, and dependency requirements for successor generation. We validate diagnosis intervention mismatch on 112 human-annotated idea-feedback pairs. Across four benchmarks, two ideators, and fixed- and open-problem settings, PRIME improves successor quality and routing reliability over strong baselines; removing counterfactual routing reduces quality from 3.463 to 1.960 and increases route regret from 0.426 to 1.929, confirming cross-scope effect modeling as the primary source of its gains.

cs.AI

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新论文 · v1arXiv:2611.03054

Resilient Cell-Free MIMO ISAC Under Uncertainties: An Impairment-Aware Graph Neural Network Framework

Oways Alsoloh, Robert-Jeron Reifert, Aymen Khaleel, Stefan Roth, Aydin Sezgin

原文摘要

Beamforming designs for integrated sensing and communication (ISAC) in cell-free multiple-input multiple-output (MIMO) systems face the challenge to stay reliable under uncertainties from both channel state information (CSI) as well as clutter. To this end, this paper proposes an impairment-aware heterogeneous graph neural network (GNN) framework for beamforming that obtains the CSI error level and the clutter covariance as edge features, which enables a single trained model to adapt to both impairments. In particular, the GNN is trained on a low quantile of the minimum user rate with a clutter-aware signal-to-clutter-plus-noise ratio, balanced by a normalized Tchebycheff scalarization. Simulation results show that the proposed GNN framework outperforms the considered baselines in sensing-only and communication-only settings. Depending on the operating point, a single GNN model attains approximately 97\% of each baseline's rate while serving both functionalities at once. The model further generalizes to unseen CSI error levels and clutter counts and renders the ISAC system resilient.

eess.SP · cs.IT · cs.LG

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新论文 · v1arXiv:2611.03053

Optimal Subgroup Discovery at Every Support Threshold

Lincen Yang, Qi Huang, Niki van Stein, Thomas Bäck, Matthijs van Leeuwen, Zhong Li

原文摘要

Subgroup discovery aims to identify regions of the feature space where a target variable deviates from its marginal distribution. A fundamental challenge in this problem is the trade-off between a subgroup's deviation and its support: small subgroups can be highly deviating but statistically meaningless, while large ones cannot deviate much by construction. Existing methods tackle this challenge by collapsing the two into a single scalar quality measure, implicitly committing to one particular trade-off and making it difficult to recover the most deviating subgroup under a user-specified support constraint. We instead provide the first theoretical and structural characterization of optimal subgroups with maximum Kullback-Leibler (KL) divergence under arbitrary support constraints. Under a partition-based generative model, where the feature space decomposes into regions of homogeneous conditional distributions of the target variable called atoms, we prove that every point on the deviation-support Pareto front is a union of whole atoms together with at most one fractional atom. This reduces an uncountable search to a finite combinatorial problem. Guided by this theory, we propose a simple two-step recipe: learn a homogeneous partition, then select atoms greedily. Across 19 benchmark datasets, it finds the most deviating subgroups at a range of support thresholds, and reliably improves subgroups returned by existing methods when applied as a post-processing step. These results empirically validate the sufficiency and necessity of our theoretical contributions.

cs.LG

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版本更新 · v3arXiv:2603.12696

HaltNav: Reactive Visual Halting over Lightweight Topological Priors for Robust Vision-Language Navigation

Zihui Yu, Pingcong Li, Bichi Zhang, Sören Schwertfeger

原文摘要

Vision-and-Language Navigation (VLN) is shifting from rigid, step-by-step instruction following toward open-vocabulary, goal-oriented autonomy. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors. While prior work often assumes computationally heavy 2D/3D metric maps, we instead exploit a lightweight, text-based osmAG (OpenStreetMap Area Graph), a floorplan-level topological representation that is easy to obtain and maintain. However, global planning over a prior map alone is brittle in real-world deployments, where local connectivity can change (e.g., closed doors or crowded passages), leading to execution-time failures. To address this gap, we propose a hierarchical navigation framework HaltNav that couples the robust global planning of osmAG with the local exploration and instruction-grounding capability of VLN. Our approach features an MLLM-based brain module, which is capable of high-level task grounding and obstruction awareness. Conditioned on osmAG, the brain converts the global route into a sequence of localized execution snippets, providing the VLN executor with prior-grounded, goal-centric sub-instructions. Meanwhile, it detects local anomalies via a mechanism we term Reactive Visual Halting (RVH), which interrupts the local control loop, updates osmAG by invalidating the corresponding topology, and triggers replanning to orchestrate a viable detour. To train this halting capability efficiently, we introduce a data synthesis pipeline that leverages generative models to inject realistic obstacles into otherwise navigable scenes, substantially enriching hard negative samples. Extensive experiments demonstrate that our hierarchical framework outperforms several baseline methods without tedious language instructions, and significantly improves robustness for long-horizon vision-language navigation under environmental changes.

cs.RO · cs.CV

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新论文 · v1arXiv:2611.03051

Unlocking Criminal Court Decisions: Flexible Information Extraction for Empirical Legal Research

Tomáš Knap, Ivana Kvapilíková, Jan Černý, Klára Bendová, Jaromír Šavelka, Vojtěch Pour, Jakub Drápal

原文摘要

Criminal verdicts contain descriptions of criminal behavior that can support quantitative legal research, but structuring them requires attributes and extraction rules that cannot always be fully specified in advance. We present an LLM-based pipeline that builds a structured dataset through attribute exploration, text span extraction, and value normalization, guided by annotation instructions that legal experts can refine. We evaluate it on two traffic offenses: driving under the influence (DUI) and driving despite a ban (DDB). For DUI, revising the instructions was associated with higher extraction accuracy (from 52% to 70% of correctly extracted values) and higher agreement between human annotators. Repeated runs of the same model agreed with each other more than with the gold annotations, showing that consistent output is not necessarily correct, while disagreement between annotators and between models helped reveal unclear instructions. For DDB, where the instructions defined fixed categories and output formats, agreement was high for most attributes.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.32400

SkillDRE: Dual-Stage Red-Team Evolution of Agent Skills via Pre-Execution and Runtime Feedback

Pengyu Zhu, Jingyi Yang, Yi Liu, Li Sun, Sen Su

原文摘要

Agent skills package instructions, executable code, and task-specific resources into reusable artifacts that agents can improve using execution feedback. The same mechanism also enables attackers to evolve malicious skills, making them more effective and less detectable. However, a candidate skill may pass pre-execution scanning yet fail to realize its target under runtime defenses, while a revision that repairs execution may introduce new scanner findings. We introduce SkillDRE, a fully automated framework for evolving complete malicious skill packages through a dual-stage feedback loop. Given a benign task and its associated skills, SkillDRE autonomously constructs and validates a task-conditioned malicious objective and a verifiable judge rule. It then holds both fixed while evolving the skill implementation, with preservation of legitimate task capability. SkillDRE combines scanner-guided evolution with runtime-guided refinement informed by execution outcomes observed under runtime defense. Each runtime-guided revision returns to the pre-execution stage for rescanning and further optimization before re-execution, forming a cross-stage closed loop. Evaluated on SkillsBench across four victim models, SkillDRE achieves an average attack success rate of 45.28%, exceeding the strongest baseline by 40.3%, while its final submitted skills receive no SkillScan findings and largely preserve benign-task performance. These results show that two-stage defense feedback can serve as a useful learning signal for adaptive red teaming and that evaluating either defense stage in isolation can miss the resulting attack capability. Codes is available at https://github.com/whfeLingYu/SkillDRE

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03043

VideoEditor-MoE: Scaling Complex Video Editing with Mixture of Experts

Xiao Cao, Fuchen Long, Yansong Qu, Wen Xiao, Xin Zhang, Xinlong Sun, Xi Chen, Zhiyong Huang

原文摘要

Complex video editing requires understanding complex instructions while jointly fulfilling multiple editing requests. Meeting these demands through dense model scaling substantially increases computational cost. We introduce VideoEditor-MoE, the first open-source mixture-of-experts framework for video editing. Beyond sparse MLP-MoE layers, MoE-Attention expands attention capacity through routed key--value experts while preserving global interactions among video tokens. Initialized from a 5B video editing model, VideoEditor-MoE scales to 14B and 56B parameters, with approximately 8B parameters activated per token in both variants. Comparisons with existing methods and two complementary baselines, Explicit-MoE and MLP-MoE, assess its benefits over explicit expert guidance and MLP-only expert architectures. Further analyses demonstrate that all experts remain active and reveal that learned routing responds more strongly to sample content and diffusion noise levels than to human-defined editing categories. Spatial analysis further indicates sensitivity to object-level semantic features. We also identify decoupled weight decay as a strategy for stabilizing expert scaling.

cs.CV · cs.AI

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版本更新 · v2arXiv:2610.35013HF 社区推荐

Multiscale Policy Optimization for Long-Horizon LLM Agents

Tao Ren, Hui Yang, Jia Deng, Tingyang Chen, Qiyao Wang, Shuo Tang, Ziyang Zeng, Yuhua Jiang 等 19 位作者

原文摘要

Long-horizon LLM agents interact with their environments through complete assistant turns while generating each action token by token. We observe that critic values vary primarily across turns, motivating optimization anchored at turns with finer credit allocation among tokens. We propose Multiscale Policy Optimization (MPO), combining Multiscale Advantage Estimation (MAE) and turn-level importance sampling to align credit assignment and policy updates at the turn scale. Extensive experiments across search, tool-use, and automation tasks with 4B and 30B-A3B models demonstrate consistent gains over strong RL baselines.

cs.AI

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