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版本更新 · v3arXiv:2602.23934

Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans

Jingwen Wang, Johannes Kirschner, Paul Rolland, Luis Salamanca, Stefana Parascho

原文摘要

This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectural blueprints. Instead of following fixed plans, construction tasks are defined through targets and obstacles, allowing the system to adapt more flexibly to environmental uncertainty and variations during the building process. A reinforcement learning (RL) policy, trained using deep Q-learning with successor features, serves as the decision-making component. As a proof of concept, we evaluate the approach on a benchmark of 15 2D robotic assembly tasks of discrete block construction. Experiments using a real-world closed-loop robotic setup demonstrate the feasibility of the method and its ability to handle construction noise. The results suggest that our framework offers a promising direction for more adaptable and robust robotic construction in real-world environments.

cs.RO · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.17281

AIGB-R1: Self-Evolving Generative Auto-Bidding via Hierarchical Planner-Executor Optimization

Yuejia Dou, Hesong Wang, Xinyu Zhang, Yuchao Ma, Tianyu Wang, Zhilin Zhang, Chuan Yu, Jian Xu 等 10 位作者

原文摘要

Auto-bidding plays an essential role in online advertising, automatically adjusting bids for advertisers to optimize their commercial goals. The emerging AI-Generated Bidding (AIGB) paradigm widely adopts generative modeling to optimize bidding strategies, yet suffers from the limited mode coverage of offline datasets and inadequate task-state understanding, hindering effective exploration of optimal strategies. Large Language Models (LLMs), with prior world knowledge and reasoning capabilities, offer a promising approach to overcome these limitations. However, directly applying LLMs to auto-bidding tasks faces inherent challenges in limited numerical precision, hallucinations, and inference latency. To address these limitations, we propose AIGB-R1, a hierarchical self-evolving auto-bidding framework aiming to enhance AI-Generated Bidding via LLMs' Reasoning capabilities, comprising a high-level Planner module for macro-level strategy planning and a low-level Executor module for fine-grained decision-making. Building upon this, we design an experience-driven self-evolving loop, enabling autonomous strategy exploration and optimization from accumulated experience. We adopt a two-stage pipeline of offline pre-training and post-training alignment, and build an interactive bidding simulation environment for strategy rollout. Furthermore, we propose Decoupled Group Relative Policy Optimization (D-GRPO) to achieve end-to-end optimization via advantage decoupling. Experimental results on a large-scale public dataset demonstrate the effectiveness of AIGB-R1.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.28447

SemFlowRAG: Directed Semantic Flow from Abstraction to Evidence for Complex Reasoning

Houyuan Qin, Rong Wu, Qinyuan Qin, Botian Shi, Jingjing Qu, Yang Sun, Pinlong Cai

原文摘要

Retrieval-Augmented Generation (RAG) enhanced by Knowledge Graphs has shown promise in complex multi-hop reasoning tasks. However, existing graph-based retrieval methods typically rely on flat, undirected topologies. During the retrieval process, the probability flow often gets trapped in high-degree abstract concept nodes which we define as ``probability black holes'', leading to semantic drift and noise accumulation. To address this, we propose SemFlowRAG, a framework that reconstructs the flat retrieval space into a corpus-adaptive semantic gradient graph. This data-driven self-organization enables a hierarchical structure to emerge naturally from the data distribution, capturing the intrinsic semantic granularity of the corpus to suppress structural noise. By quantifying the semantic abstractness of entities through the embedding variance of their associated passages, we transform static undirected edges into directed semantic constraints. Furthermore, we design an abstractness-guided directed PageRank algorithm that forces the retrieval trajectory to follow a ``high-to-low semantic abstractness'' gradient. This mechanism ensures layer-by-layer evidence convergence, smoothly guiding the retrieval process from abstract concepts to specific document evidence. Extensive experiments on complex QA datasets demonstrate that SemFlowRAG effectively mitigates the ``probability black holes'' issue, outperforming existing baselines in both retrieval and downstream reasoning performance.

cs.IR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02779

Stop Handcrafting Your Engine! GenDB-V: Towards AI-Generated and Formally Verified Database Systems

Jiale Lao, Immanuel Trummer

原文摘要

For over 50 years, the database community has developed a wide range of successful engines to answer two fundamental questions: how can database systems run faster, and how can we ensure their correctness? Experience with these engines shows that performance depends on hardware and workloads. As both change over time, we repeatedly redesign and rebuild database systems, a time-consuming and expensive process. Correctness remains another long-standing challenge: recent studies continue to uncover wrong-answer bugs even in mature database engines, despite years of development and testing. To address these, we present GenDB-V, a system that enables AI agents to synthesize database engines specialized to hardware and workloads for performance, and formally verified for correctness. GenDB-V supports specialized implementations of SQL templates and general-purpose engines that accept any query within a supported SQL subset. First, we develop SQLSpec to formalize SQL semantics and deterministically construct fixed, implementation-agnostic synthesis targets. Second, we enable joint synthesis of fast code and formal proofs through storage--execution co-optimization, performance-first exploration, and three-layer modular proofs that bridge low-level execution to SQLSpec semantics. Third, we verify the synthesized code and proofs against the fixed synthesis targets in an isolated pipeline, proving SQL correctness and program safety. We evaluate GenDB-V on TPC-H and SEC-EDGAR. For verified template implementations, GenDB-V achieves geometric-mean speedups of 22.0-61.3x over DuckDB and Umbra. For a verified general-purpose engine, GenDB-V also achieves speedups of 1.27-3.4x on TPC-H. GenDB-V also achieves better performance than a testing-based LLM synthesis method, while our audit and tests expose incorrect answers in that method's implementations for 16 of 28 templates.

cs.DB · cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.19752

MSAlign: Aligning Molecule and Mass Spectra Representations for Metabolite Identification

Paul Krzakala, Gabriel Melo, Camille Lançon, Charlotte Laclau, Rémi Flamary, Etienne Thévenot, Florence d'Alché-Buc

原文摘要

Accurately identifying metabolites i.e. small molecules from mass spectrometry data remains a core challenge in metabolomics, with broad applications in drug discovery, environmental analysis, and clinical research. We address the Molecule Retrieval task, which consists in recovering the chemical structure of a metabolite from its MS/MS spectrum given a set of candidate molecules. We make three contributions. First, we propose a unified framework encompassing recent approaches based on representation alignment and contrastive learning. Second, we introduce MSAlign, a lightweight model that achieves state-of-the art performances by aligning two frozen foundation models (DreaMS for mass spectra and MolDeBERTa for molecules) and demonstrate that a score fusion strategy further improves the performance for a very small computational cost. Third, we investigate a long-standing evaluation problem: data splitting strategies in molecule retrieval implicitly trade off data leakage against domain shift. We formalize this tension by introducing a quantitative measure of distribution shift, and use it to evaluate splitting strategies in existing benchmarks. All datasets, splits, candidate sets, and a unified implementation of MSAlign and baselines are publicly released to support reproducible research.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.18549

Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics

Maciej Chrabaszcz, Aleksander Szymczyk, Marcin Sendera, Tomasz Trzcinski, Sebastian Cygert

原文摘要

Large Reasoning Models (LRMs) introduce new opportunities for safety monitoring through their Chain-of-Thought (CoT) reasoning. However, CoT is not always faithful to the model's final output, undermining its reliability as a monitoring tool. To address this, we investigate the hidden representations of LRMs to determine whether future behavior can be predicted from prompt and CoT representations. By evaluating a probe at each generated token, we construct a probe trajectory, the continuous evolution of a concept's probability across the reasoning process. To characterize these temporal dynamics, we extract signal-processing features that capture volatility, trend, and steady-state behavior. We find that trajectory features help precisely where the static probe is weakest, adding 21.7 AUROC points on average, and improving in every such case where that probe falls below 70. We also present two methodological insights. First, template-based training data achieves near-parity with dynamically generated model responses, eliminating the need for a costly initial inference and labeling. Second, the use of a pooling operation is necessary: last-token methods achieve poor performance, whereas average and max pooling achieve 78.5% to 97.4% AUROC and yield stable probe trajectories. Across seven reasoning models from three families and six evaluation datasets, we find probe trajectories to be a promising signal for monitoring model behavior. Warning: This article contains potentially harmful content.

cs.CL · cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.16812

DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic Regression

Yuxiao Hu, Qian Li, Ding Wang, Yekun Zheng, Jinyue Yan, Yuntian Chen

原文摘要

Symbolic regression (SR) aims to discover interpretable mathematical expressions from data and plays a key role in scientific discovery. However, existing methods face a common bottleneck: the enormous search space and severe combinatorial explosion make it difficult to achieve a favorable trade-off between accuracy and algorithmic efficiency. We argue that, in addition to designing increasingly complex solver-specific SR strategies, an equally promising direction is to equip them with a shareable auxiliary strategy that can provide reliable search guidance. To this end, we propose DASS, a solver-adaptable Dynamic Auxiliary Search Strategy that improves SR by constructing and refining a high-potential auxiliary search subspace. DASS represents the auxiliary search space as a functional subspace spanned by a set of high-potential basis function terms. It estimates term utility through multi-environment evaluation, filters unstable guidance, and updates term potential scores with a Gibbs posterior, thereby calibrating the subspace to provide more reliable guidance for downstream solvers. The high-quality solutions produced by the solver are used to update the set of basis terms. This forms a virtuous closed-loop optimization process: the subspace guides the downstream solver, and the solver feedback reshapes the subspace for subsequent exploration. DASS can be easily integrated with various types of SR solvers. Extensive experiments on LLM-SRBench show that DASS significantly improves the accuracy of original solvers with a manageable increase in runtime.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02778

Spectral Degeneration of Self-Attention under Isotropic Score Geometry

Mengda Li, Jianfeng Yao

原文摘要

We study the spectral structure of the self-attention matrix in Transformer models through an idealized spectral model for the pre-softmax score matrix. Motivated by the rotational isotropy of query and key representations, our model replaces the random token-space orientation of the score matrix by its canonical singular-value representative with a logarithmic spike--bulk separation. Empirical diagnostics on pretrained vision and language transformers motivate this model: token-space singular matrices have nearly uniform eigen-angle distributions, and score spectra are dominated by a small number of effective directions. We then prove that the row-wise softmax map transforms this structure into a degenerate attention matrix: dominant modes are preserved, whereas the bulk spectral distributions concentrate at zero. Stable rank remains bounded for bidirectional attention but grows logarithmically for causal attention. Within the canonical model, these results explain how attention can preserve a small number of dominant directions while compressing the remaining bulk modes.

cs.LG · cs.AI · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02777

Beyond Policy Patches: Distilling RL Specialists into Vision-Language-Action Policies

Zhennan Jiang, Tingying Xie, Shilong Ji, Shu'ang Yu, Tianxing Zhou, Yuhui Chen, Jiahuan Gao, Kaizhi Xie 等 14 位作者

原文摘要

Vision-language-action (VLA) policies can handle noncritical stages of long-horizon manipulation yet fail during short precision-critical phases. Lightweight reinforcement learning (RL) specialists can address these failures without directly applying RL to the full VLA, but retain auxiliary controllers and switching mechanisms at deployment. We study how to transfer their capabilities into a single flow-matching VLA. Direct imitation of specialist rollouts can reproduce action jitter and leave the VLA unprepared for states encountered during its own execution. We introduce REPAIR, a value-guided distillation framework that combines offline initialization with on-policy refinement. At each critical-phase observation, REPAIR queries the corresponding specialist for multiple action candidates and weights them using its critic to construct a supervision distribution. A shared weighted flow-matching objective trains the VLA first on observations from specialist-assisted rollouts, then on observations collected through its own execution. Specialists provide supervision without taking control during on-policy collection and are removed at deployment. Across six real-robot tasks with 30 evaluation trials per task, REPAIR achieves 100% success on all six tasks, with lower mean end-effector jerk than specialist-assisted execution on every task.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02776

LipLingo: CNN Model for Lip Reading

Mahmoud S. Mayaleh, Liban Ali Achkir, Nijat Mehdiyev, Ahmet Ozdil

原文摘要

Lip reading plays a crucial role in applications such as speech recognition, assistive technologies for the hearing-impaired, and biometric authentication. However, performance often degrades when the speaker varies and when certain speech sounds are visually similar. To address these challenges, we propose LipLingo, a robust lip-reading system based on the LipNet architecture, enhanced with a standardized preprocessing pipeline for consistent mouth-region representation, a rotational validation strategy for improved generalization to unseen speakers, and a flexible similarity evaluation for more reliable sentence-level assessment. The model architecture combines 3D convolutional layers for spatio-temporal feature extraction and bidirectional recurrent layers for sequence modeling, optimized using the Adam optimizer and Connectionist Temporal Classification (CTC) loss. On the GRID dataset, LipLingo achieved 95.0\% character-level accuracy (CER 4.99\%) and 87.2\% sentence-level accuracy. At the corpus level, the system obtained a (WER 13.1\%), outperforming the baseline LipNet and demonstrating competitive performance relative to recent 3D-ResNet + Bi-GRU + MixUp approaches. These results provide insights into strategies that enhance lip-reading robustness and highlight directions for future work toward real-world deployment.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.13217

Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision

Bingsen Chen, Boyan Li, Ping Nie, Yuyu Zhang, Xi Ye, Chen Zhao

原文摘要

Existing benchmarks for Deep Research Agents (DRAs) treat report generation as a single-shot writing task, which fundamentally diverges from how human researchers iteratively draft and revise reports via self-reflection or peer feedback. Whether DRAs can reliably revise reports with user feedback remains unexplored. We introduce Mr Dre, an evaluation suite that establishes multi-turn report revision as a new evaluation axis for DRAs. Mr Dre consists of (1) a unified long-form report evaluation protocol spanning comprehensiveness, factuality, and presentation, and (2) a human-verified feedback simulation pipeline for multi-turn revision. Our analysis of five diverse DRAs reveals a critical limitation: while agents can address most user feedback, they also regress on 16-28% of previously covered content and citation quality. Over multiple revision turns, even the best-performing agents leave significant headroom, as they continue to disrupt content outside the feedback's scope and fail to preserve earlier edits. We further show that these issues are not easily resolvable through inference-time fixes such as prompt engineering and a dedicated subagent for report revision.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02771

Stage-aware Multimodal Brain Graph Contrastive Learning for Neurodegenerative Disorder Diagnosis

Jingjing Zhou, Chenxuan Meng, Ciyuan Peng, Jing Ren, Feng Xia

原文摘要

Neurodegenerative disorders are characterized by their progressive nature, with distinct stages that require precise identification and tracking of progression trajectories for accurate diagnosis. However, existing diagnostic methods often fail to fully capture the unique brain patterns associated with different stages, limiting their ability to effectively target specific stages of the disorders. To address this issue, we propose a Stage-aware Multimodal Brain Graph Contrastive Learning (smBGC) method. Specifically, smBGC introduces a stage-aware feature enhancement strategy to capture stage-specific features across all progression stages, enabling the generation of stage-aware multimodal brain graph representations. In addition, smBGC incorporates a stage-adaptive brain graph augmentation approach within a graph contrastive learning framework, facilitating multimodal fusion and improving the model's capability to distinguish between different disorder stages. Experiments on two real-world datasets demonstrate the superior performance of smBGC in various neurodegenerative disorder diagnosis tasks compared with state-of-the-art methods.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02769

Unlocking the Potential of Vision Foundation Models for Multi-Task Dense Prediction via Mixture of Task Adapters

Yunzhi Zhuge, Xinzhuo Yu, Sitong Gong, Lu Zhang, Pingping Zhang, Huchuan Lu

原文摘要

Multi-task dense prediction seeks a single, coherent representation that supports simultaneously discriminative semantic recognition and regularized geometric/structural inference. For vision foundation models, pretrained semantic invariances conflict with the frequency and smoothness biases of dense objectives; joint fine-tuning and single-route adapters therefore compress competing gradients into one update channel, causing negative transfer and unstable cross-task agreement. We frame this as two-level adaptation: task-generic alignment that preserves priors, and task-aware specialization that resolves late-stage conflicts. Based on this principle, we propose D2MoE , a Mixture of Task Adapters that explicitly factorizes these roles within a frozen DINOv3 backbone. D2MoE introduces Task-Generic Low-Rank Adaptation (TG-LoRA) in attention projections throughout the network to provide a stable, shared adaptation channel, and a Task-Adaptive Mixture of Experts (TA-MoE) injected only at the final attention block of each stage to enact sparse, task-conditioned perturbations of token mixing in a single forward pass. A Semantic Prior Injector further couples multi-level task features with frozen foundation priors to reinforce semantic coherence while preserving boundary and structural fidelity. Experiments across diverse benchmarks substantiate that this structured factorization yields robust multi-task improvements with a small trainable footprint, establishing a principled and scalable route to prior-preserving, foundation-centric multi-task dense perception.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02767

GaussFlaw: Fine-Grained Perceptual Diagnosis for 3D Gaussian Splatting

Shaocheng Shen, Zihan Zheng, Kejin Li, Lu Liu, Xiaoyun Zhang, Qiang Hu, Wenjun Zhang

原文摘要

3D Gaussian Splatting (3DGS) enables photorealistic novel- view synthesis with real-time rendering, yet fine-grained di- agnosis of its rendering artifacts remains challenging. These artifacts are typically spatially localized and exhibit diverse degradation patterns, making them difficult to character- ize using global quality metrics and image-level assessment benchmarks, which provide no explicit indication of where they occur or what types of artifacts they represent. To ad- dress this limitation, we introduce GaussFlaw-80K, the first large-scale dataset for fine-grained perceptual diagnosis in 3DGS. Constructed through a model-assisted human-in-the- loop pipeline, GaussFlaw-80K contains 80,049 artifact re- gions across 14,289 rendered images, each annotated with a pixel-level mask and one of eight flaw labels spanning ge- ometric, blending, and photometric degradations. These an- notations turn 3DGS artifact diagnosis into an explicit and measurable task. Building on this dataset, we propose Ge- oCritic, a geometry-guided multi-task model that integrates RGB appearance with depth-derived cues to jointly localize artifacts and classify their flaw types. Beyond evaluation, we further demonstrate the downstream benefits of fine-grained diagnosis through three complementary targeted operations: Gaussian densification, attribute correction, and outlier prun- ing. Experiments show that GeoCritic achieves an AUC-J of 0.9507 and an Acc@1 of 80.08%, while diagnosis-guided re- finement improves all reported global and crop-level metrics across diverse datasets and 3DGS backbones.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02766

EffComp: Efficient Prompt Compression via Hybrid Reinforcement & Supervised Learning

Romtham Tungsununtham, Piyawat Lertvittayakumjorn, Atiwong Suchato, Peerapon Vateekul

原文摘要

Prompt compression is aimed at reducing input prompt lengths to enable cheaper, faster, and potentially higher-quality LLM predictions. However, existing prompt compression methods are often limited by modest compression gains, a risk of hallucination, and/or high compression latency. We propose EffComp, an Efficient prompt Compression framework for RAG-based question answering (QA). EffComp employs BERT-style document reranker and sentence selector models to allow fast extractive prompt compression at the sentence level. Its extractive nature prevents hallucinations in the compressed prompts. Additionally, the training process using a hybrid reinforcement and supervised learning approach is designed to optimize the compression ratio while preserving LLM accuracy. Experiments on various QA datasets demonstrate that EffComp outperforms state-of-the-art prompt compression methods achieving the best trade-off between compression ratio and accuracy with minimal latency. This makes it practical for real-world applications.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03752

LoRA-Patch: Defending Against Backdoored LoRA Adapters via Directional Steering

Shengyan Huo, Peihai Jiang, Xixiang Lyu, Yige Li

原文摘要

Third-party LoRA adapters introduce serious backdoor risks: malicious adapters can maintain normal task performance while embedding hidden, trigger-activated behaviors, making them difficult to detect through standard utility evaluations. Existing removal-based defenses typically sanitize backdoored models by updating, pruning, distilling, or mixing model parameters. However, these approaches are misaligned with adapter-based deployment, where the trusted base model is expected to remain frozen. In this work, we propose LoRA-Patch, a lightweight backdoor defense tailored to LoRA adapters. Our key insight is that backdoor injection distorts the directional geometry of a poisoned adapter relative to its trusted base model. Building on this insight, LoRA-Patch suppresses trigger-activated behaviors by realigning adapter directions through layer-wise steering, without modifying the base model weights. Extensive experiments across five backdoor attacks and three models show that LoRA-Patch effectively mitigates trigger-activated backdoors, outperforming existing baselines while preserving benign task performance. Moreover, LoRA-Patch is computationally efficient, constructing patches within 20 seconds across all evaluated models and adapters. Our code is publicly available at https://github.com/FOXVEDA/LoRA-Patch.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.17437

EV9V: An Echocardiographic Video Benchmark with Uncertainty Guided View Classification

Bo Gou, Jicheng Zhang, Jianlong Xiong, Tao He, Hai Wu, Bentian Liu, Yijiao Wang, Yu Zhang 等 13 位作者

原文摘要

Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main challenges. First, publicly available datasets are scarce and limited in scale and view coverage. Second, the performance of some modern video-level architectures for echocardiographic view classification remains underexplored. Third, some view categories exhibit highly similar spatial appearances, making single-frame features insufficient for discrimination, while heterogeneous frame quality complicates robust temporal information fusion. To address these challenges, we release the Echocardiographic Videos of Nine Views (EV9V) dataset, comprising 5,138 videos, 910,579 frames, and 9 standard views, which is, to the best of our knowledge, the largest openly released echocardiography video dataset. Furthermore, we propose a Spatio-Temporal Fusion Model (STFM), an efficient dual-stream framework that jointly captures spatial anatomical structures and temporal cardiac dynamics. The STFM leverages uncertainty-aware learning to preferentially sample representative video segments during training and evidence-based fusion during inference, improving robustness to variations in frame quality across echocardiographic videos. Extensive experiments demonstrate that STFM achieves competitive performance and effectiveness compared with diverse video classification architectures, including two-dimensional and three-dimensional convolutional neural networks, recurrent neural networks, and Transformers. The code is released at https://huggingface.co/datasets/bgx666/EV9V and the EV9V dataset is available at https://github.com/bgx666/stfm.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05422

CAST-Bench: Tracing Every Sound to Its Source in Multi-Shot Audio-Video Generation

Yucong Liu, Qi Tian, Shuyuan Tu, Kaihang Pan, Weijie Kong, Chao Pan

原文摘要

Existing benchmarks for joint text-to-audio-video (T2AV) generation evaluate perceptual quality and audiovisual alignment, but balanced sound-source sam?pling and separate reporting by shot structure remain limited. This makes it dif?ficult to determine whether a specific sound is missing, attributed to the wrong source, or lost across shot transitions. To address this, we introduce CAST?BENCH, a benchmark designed to trace every generated sound back to its in?tended source in multi-shot audio-video generation. The core idea is to formu?late T2AV correctness as an audiovisual contract in which each requested sound must be emitted by the right source, synchronized with the corresponding visual action, and carry the right semantic content. Concretely, the benchmark orga?nizes 900 prompts into 9 sound-source categories, with 100 prompts per cate?gory, spanning speech, animals, instruments, ambient sounds, and other emitter types. The prompts include 439 single-shot and 461 multi-shot narratives, each derived from a structured specification that links every audio event to its emit?ter, subject, and temporal slot. The benchmark provides an 18-metric evaluation suite covering three dimensions: Visual Performance, Content Consistency, and Audio & Semantic Alignment. We further benchmark 12 representative gen?erators and find that visual quality is nearly saturated across leading systems, yet audio semantic scores still differ markedly. Across the nine shared mod?els, multi-shot prompts receive lower aggregate scores under the subset-specific scoring rules and yield different model rankings. Multi-person speech, con?sistent speaker identity across shots, and cross-shot event retention remain the main unresolved challenges. All prompts and evaluation code are available at https://github.com/yucong-liu/CAST-Bench-public.

cs.SD · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02759

Trace4D: Grounded Spatiotemporal Reasoning via 4D State Sequences

Wooseok Jang, Seungryong Kim, Yan Lu, Lei Ji

原文摘要

Understanding how objects and their spatial relationships evolve in 3D space over time remains challenging for vision-language models (VLMs), particularly under changing viewpoints. Recent approaches incorporate geometric information into reasoning, but explicit supervision for temporally ordered 4D state evolution and its use as intermediate evidence remains limited. We introduce \textbf{Trace4D}, which trains VLMs to predict 4D state sequences and reason over their inter-timestep changes alongside temporal visual observations. Its 4D-grounded chain-of-thought (CoT) explicitly represents 4D states, analyzes temporal visual context, and connects both as evidence for the answer. We construct Trace4D-Train from in-the-wild videos with 600K 4D perception examples and approximately 70K grounded QA examples, and train with joint supervised fine-tuning followed by geometry-aware GRPO. At inference, Trace4D predicts 4D states and reasoning directly from video without reference annotations or an external geometric model. Experiments on Trace4D-Bench and VLM4D show improved QA accuracy over the base VLMs and benefits from reasoning over 4D state changes and temporal visual observations.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02758

Learning to Adjudicate: Verifier-Guided Multi-Agent Reasoning for E-Commerce Relevance

Shi Shu, Hao Chen, Baopu Qiu, Chao Wei, Weiru Zhang, Xiu Li, Xiaoyi Zeng

原文摘要

Relevance modeling is fundamental to e-commerce search, determining how well retrieved products satisfy the needs expressed in a user's query. Accurate judgments require interpreting user intent, comparing product attributes, and applying business-specific matching criteria. Current methods based on large language models (LLMs) leverage contextual understanding and reasoning, focusing on incorporating domain-specific context and enhancing reasoning performance. Despite these advances, difficult query-product pairs remain challenging when evidence from different perspectives supports conflicting conclusions. A single reasoning pass may fail to reconcile these conflicts and ensure that decisive evidence guides the final judgment. To address this challenge, we propose Collaborative Adjudication with Debate and Replay Experience (CADRE), a framework for evidence-based adjudication in e-commerce relevance assessment. CADRE organizes three expert agents sharing a frozen backbone to analyze product attributes, user intent, and business rules in separate contexts, while a trainable Verifier assesses their supporting evidence against the applicable matching criteria to determine relevance. Under expert disagreement, a Verifier-guided debate elicits targeted clarification and revision of disputed interpretations before final adjudication. We optimize the Verifier through multi-turn group relative policy optimization and adapt successful-experience replay so that trajectories yielding correct terminal labels can be reused alongside fresh rollouts in subsequent updates. For efficient online serving, we use CADRE as an offline teacher to distill its relevance judgments into a lightweight student model. Offline evaluations and online A/B tests on an e-commerce search advertising platform demonstrate that CADRE improves relevance classification and key business and relevance satisfaction metrics.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02757

Is your LLM an Agressive Narcissist with Depression? Analyzing Psychological Portraits of Large Language Models

Polina Fedorinina, Anna Fattakhova, Nadezhda Romanova, Ekaterina Lezina, Irina Nikishina

原文摘要

Nowadays, interaction with AI requires caution not only regarding the factual reliability of generated content but also regarding personality markers that models may implicitly transmit during dialogue that can subtly influence users' perception of information. In this paper, we conduct a large-scale experimental study in which 22 large language models complete 11 psychological inventories. Furthermore, we investigate correlations between human and model responses to track the adjustments made for the users who interact with them on a daily basis. The results show that LLMs exhibit coherent and relatively stable psychological patterns. Personality traits remain largely consistent, whereas emotional and symptomatic dimensions are more variable, with anxiety and neuroticism emerging as dominant tendencies. Model explanations further suggest that LLMs generate internally consistent and safety-oriented profiles while also reflecting correlations with the users they regularly interact with.

cs.AI

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新论文 · v1arXiv:2611.02756

DINOFAS: Bonafide-Centric Representation Learning for Face Anti-Spoofing

Shakeel Muhammad Ibrahim, Muhammad Sohail Ibrahim, Muhammad Shahid Jabbar, Shujaat Khan

原文摘要

Face anti-spoofing (FAS) models often learn attack- and dataset-specific cues that degrade performance under unseen acquisition conditions and presentation attacks. We present DINOFAS, a DINOv3-based framework that formulates FAS as bonafide-centric representation learning. The proposed framework combines parameter-efficient DINOv3 adaptation using low-rank adaptation (LoRA), an attention style-content disentanglement (ASCD) module for reducing domain-specific appearance variations, and a dual-branch architecture that separates prototype-based liveness learning from attack-semantic supervision. The model is jointly optimized using a bonafide-centric prototype objective, an attack-semantic classification loss applied to detached features, and orthogonality regularization. DINOFAS achieves an average HTER of 9.09\% under the standard OCIM leave-one-domain-out protocol, where training is performed on three source datasets and evaluation is conducted on the held-out unseen domain. We further evaluate synthetic-to-real generalization by training exclusively on SynthASpoof and testing on four unseen real-world datasets, where DINOFAS achieves the lowest average HTER of 21.41\% and the highest average AUC of 86.14\% among the compared methods. Evaluations on OULU-NPU and SiW-Mv2 additionally demonstrate robustness to variations in acquisition conditions, devices, and unseen presentation attacks.

cs.CV · cs.AI · eess.IV

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新论文 · v1arXiv:2611.02751

Pair-Relative Partial Equivariance for Rotation-Robust Keypoint Descriptor Matching

Jongmin Lee

原文摘要

Learned keypoint descriptors trained on upright imagery often fail under large in-plane rotation. Steerers address this by applying a single exact SO(2) representation to the descriptor space, but this global group constraint can be too rigid for keypoint descriptors, whose angular responses need not follow a single group representation. We propose pair-relative partial equivariance, which measures equivariance under the empirical distribution of relative rotations in image pairs, and PaSteer, a relaxed descriptor-steering framework that allows the steering map to leave the exact representation manifold while retaining the lightweight, backbone-free design of steering. PaSteer instantiates this relaxation with two variants: an identity-preserving residual steerer (PaSteer-soft) and a free-form Fourier steerer (PaSteer-free). We show that the per-angle optimal linear steerer for a fixed non-equivariant descriptor generally does not obey the group composition law, explaining why exact representations can be suboptimal in the frozen-descriptor regime. On Roto-360, MegaDepth-1500, AIMS, and IMC-PT, PaSteer consistently improves rotation-robust matching over an exact steerer on frozen DeDoDe descriptors, especially under large relative rotations, with minimal upright degradation.

cs.CV · cs.CG

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新论文 · v1arXiv:2611.02750

Investigating the effects of the patch size in Time Series Foundation Models

Jad Yehya, Andrei-Tiberiu Pantea, Aymeric Dieuleveut, Matthieu Kowalski, Thomas Moreau

原文摘要

Patch size is a central design choice in patch-based time-series foundation models, controlling temporal resolution, sequence length, and computational cost. We study how this choice interacts with transformer architecture through a controlled comparison of encoder-only and decoder-only forecasting models trained with the same data mixture and optimization budget. Across 97 GIFT-Eval tasks, encoder-only models consistently benefit from smaller patches, whereas decoder-only performance follows a U-shaped trend, with an intermediate patch size performing best in aggregate. Dataset-level results further show that the preferred decoder patch size increases with the forecasting horizon. For encoder-only models, increasing context length improves performance across patch sizes, although the gains diminish beyond a context length of 2048. Finally, training-compute and CPU-latency Pareto analyses reveal complementary regimes: decoder-only models provide efficient low-cost alternatives, while encoder-only models attain the highest overall accuracy. These results show that the choice of patch size and architecture should be made depending on the forecasting horizon and deployment constraints.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.02747

What Drives the Zero-to-One Jump? Diagnosing and Steering Hard-Prompt Learning in RLVR

Rui Zhu, Yumou Liu, Zhenzhe Zheng, Fan Wu

原文摘要

Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved mathematical reasoning, yet progress on hard prompts often stalls: a prompt can remain unsolved for many training steps before abruptly transitioning to solved--a discrete "zero-to-one" jump whose underlying dynamics and steerability remain poorly understood. We investigate two questions: what drives this transition, and can it be steered? Through event-based analysis measuring the time-to-first-positive (TTFP), we find that negative rollouts preceding successful transitions consistently retain higher early-stage generation entropy, distinguishing near-solvable failures from dead ends. This signal motivates NAPO (Negative-Augmented Policy Optimization), a method integrated with Group Relative Policy Optimization (GRPO) that ranks negatives in unsolved groups via an early-branching driver and assigns graded advantages, injecting informative gradients precisely where standard group-relative updates vanish. Under fixed rollout budgets, NAPO accelerates early solution discovery and improves Pass@256 by 10.0 points on AIME24 and 6.6 points on AIME2025, demonstrating that zero-to-one transitions are diagnosable and steerable through driver-guided negative learning. Our code is available at https://github.com/jameszhu-pixel-time/Z2O.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.11296

Spatial-Frequency-Aware Implicit Neural Representation of Multidimensional Signals via MLP-KAN Fusion

Wen Yan, Ligen Shi, Jun Qiu, Haimiao Zhang, Lina Wu, Chang Liu

原文摘要

Implicit Neural Representations (INRs) have emerged as a compelling paradigm for modeling multidimensional signals by mapping continuous coordinates to signal values. However, Multi-Layer Perceptrons (MLP)-based INRs inherently suffer from spectral bias, which favors low-frequency components and suppresses the reconstruction of essential high-frequency details. While existing techniques, such as Fourier feature mappings, mitigate this issue, they often rely on sensitive manual tuning and are prone to spectral artifacts. In this paper, we propose a spatial-frequency-aware INR framework that combines an MLP branch with a Kolmogorov-Arnold network (KAN) branch for complementary frequency-oriented modeling. The MLP branch provides a low-frequency-oriented representation of smooth structures, whereas the KAN branch complements localized variations and fine details. To coordinate the two branches, we integrate the discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) into the output fusion stage. The outputs of the two branches are decomposed into wavelet coefficients, and the corresponding coefficients are additively fused before inverse wavelet reconstruction. A wavelet-domain band-separation regularization further penalizes high-frequency responses in the MLP branch and low-frequency responses in the KAN branch, thereby encouraging complementary frequency-oriented behavior. Experiments on 1D signals, 2D images, 3D volumes and signed distance functions, videos, and 4D light-fields demonstrate the applicability of the proposed representation across the evaluated signal modalities. Results demonstrate improved reconstruction fidelity across the evaluated signal modalities.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.18850

MOPDA: Mixed-Trajectory On-Policy Distillation for Language-Guided Industrial Anomaly Detection

Shuimu Chen, Jing Jin, Nan Su, Hongbo Xu, Zebang Cheng, Wenming Yang, Fei Ma, Guijin Wang

原文摘要

Large vision-language models (LVLMs) have shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable reasoning. However, reliably translating generated judgments into precise pixel-level localization remains challenging. We propose \textbf{M}ixed-Trajectory \textbf{O}n-\textbf{P}olicy \textbf{D}istillation for Language-Guided Industrial \textbf{A}nomaly Detection (MOPDA), the first framework to introduce on-policy self-distillation into LVLM-based IAD. For judgment learning, \method introduces \textbf{Mixed-Trajectory Supervision}, combining student-generated on-policy trajectories with evidence-conditioned teacher trajectories under a shared token-level distillation objective. Student trajectories preserve supervision on deployment-relevant response paths, while teacher trajectories provide complementary evidence-conditioned supervision. For dense localization, \textbf{Language-guided Visual Anchoring} uses the final judgment as a compact semantic condition to construct image-specific normal and abnormal anchors, which are contrasted with dense visual features to produce anomaly maps. This keeps language as semantic guidance while grounding pixel-level responses in visual evidence. Under a strict cross-dataset zero-shot protocol on five IAD benchmarks, \method outperforms the evaluated LVLM-based baselines on most detection, localization, and judgment metrics while remaining competitive with CLIP-based methods. Ablations further validate both mixed-trajectory supervision and final-judgment conditioning.

cs.CV · cs.AI

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新论文 · v1arXiv:2611.02744

Subspace Representations for Synthetic-to-Real Transfer in 3-D Surface Inspection

Abdulla Nasir Chowdhury, Naish Ibrahim, Gulpi Qorik Oktagalu Pratamasunu, Kazuhiro Fukui

原文摘要

This paper studies what subspace representations contribute to synthetic-to-real transfer in 3-D surface inspection, where models learn from synthetic defects and the only real data before deployment are scans of good parts. We use three classical constructions as instruments: the nearest-subspace classifier, one-class template subspaces and the generalized difference subspace (GDS). On 1,197 MVTec 3D-AD scans without target annotation we find that every decision rule ranks real defects at a similar window AUC, that image-level detection stays at chance, and that no rule keeps its in-domain threshold. We show that the margin of a nearest-subspace rule moves by at most the part of the feature shift outside its class subspaces, and that this leakage, measured from unlabeled good scans, bounds the displacement observed on unseen scans. On 597 Real3D-AD objects, a one-class subspace of registered templates evaluated at every location on frozen features reaches point AUROC 0.957 on pre-registered scans and a category-mean object AUROC of 78 to 81 with no training on the benchmark. Our results suggest that a subspace helps under shift in proportion to the shift it spans, and that in reference-based inspection the reference and its registration matter more than the subspace.

cs.CV · cs.LG

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新论文 · v1arXiv:2611.02739

Beyond Monotonicity: Stochastic Online Optimization under Semi-Bandit Feedback

Tsubasa Harada, Yasushi Kawase, Hanna Sumita

原文摘要

We study stochastic online optimization (SOO) under semi-bandit feedback: in each of $T$ rounds, a policy adaptively observes $n$ independent random variables with unknown distributions, learning only from observed values. First-order stochastic dominance (FSD) monotonicity enables general reductions for learning SOO, but can fail when observations change future feasibility, resource use, or reference points. Our algorithm runs the dynamic program for the optimal policy, replacing each expectation by its most optimistic value over a confidence set of distributions. Its regret depends on the level-set complexity $s_i$ of the computed values as functions of the $i$th variable: the number of intervals needed to describe their superlevel sets. For rewards in $[0,1]$, the regret is $\tilde{O}(\sqrt{ST})$ without this monotonicity, where $S=\sum_{i=1}^n\min\{s_i,k_i\}$ and $k_i$ is the support size of the $i$th distribution, which may be infinite when $s_i$ is finite. For constant level-set complexities, regret is $\tilde{O}(\sqrt{nT})$, even for continuous distributions. The algorithm requires no knowledge of $s_i$, $k_i$, or the supports, and it also achieves regret $\tilde{O}(\sqrt{nkT})$ for supports of size at most $k$ without any level-set condition, which improves the previous bound $\tilde{O}(n(\sqrt{kT}+k))$ for FSD-monotone problems. We also prove a lower bound of $Ω(\min\{T,\sqrt{ST}\})$ for all positive integers $s_1,\ldots,s_n$ with $k_i=2s_i+1$, which holds even under full feedback and matches the upper bound up to logarithmic factors. We obtain $\tilde{O}(\sqrt{nT})$ regret for these problems, all nonmonotone under FSD: stochastic Boolean function evaluation, the full-information secretary problem, optimal stopping with loss aversion, scheduling with deadlines and correlated stochastic knapsack with unimodal rewards, and overbooking with group reservations.

cs.LG

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新论文 · v1arXiv:2611.02738

Multi-Modal Multi-Agent System for Bridge Operation and Maintenance based on Large Language Model

Xiangyun Luo, Yunkai Zhang, Yong Huang, Stephen Wu, Hui Li

原文摘要

Bridges are critical transportation infrastructure, and reliable operation and maintenance (O&M) is essential for traffic safety and long-term structural performance. However, bridge O&M faces key bottlenecks: no unified architecture coordinates heterogeneous resources; model inferences lack clear links to physical-mechanical laws; and experts' tacit knowledge remains difficult to formalize. This study proposes a knowledge-enhanced autonomy formalization methodology and apply it to construct the Bridge O&M Multi-Modal Multi-Agent System (BOMMA), which integrates heterogeneous diagnostic actions, physics-mechanics-informed representations, long-term memory, and structured experiential knowledge within a coordinated multi-agent workflow, enabling interpretable and traceable scheduling, analysis, and decision-making. On 3,492 multi-source instructions, the proposed method achieves scheduling recalls of 85.37% for simple and 99.40% for composite tasks, with 95.35% BERT F1-score for O&M action reasoning. Ablation studies confirm the contributions of the components in the autonomy. This study bridges general LLM reasoning and specialized bridge O&M requirements.

cs.MA

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