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新论文 · v1arXiv:2611.07702

InfMasking: A Unified Variational Framework for Synergistic Multimodal Learning

Liangjian Wen, Qun Dai, Jiang Duan, Yong Dai, Jianzhuang Liu, Dongkai Wang, Jun Wang, Fuyuan Cao 等 10 位作者

原文摘要

Multimodal models are expected to exploit synergistic information, that is, information available only when multiple modalities are observed together. In practice, however, a fusion module can fit the training objective by relying on a dominant modality or on information that modalities share, and the synergy between modalities is often left unused. We propose InfMasking, a unified multimodal learning framework based on Infinite Masking. Before fusion, InfMasking randomly masks the features of each modality; each sampled mask produces a different partial combination of cross-modal evidence and a fused representation of the corresponding modality interactions. All masked fused representations are required to remain consistent with a reference target: the unmasked fused view when training is unsupervised, and the class proxy of the task label when labels are available. Maintaining this consistency across diverse partial combinations discourages reliance on a dominant modality or on shared information alone, and drives the fusion module to learn cross-modal interaction patterns that remain meaningful under partial observations. Since the objective is an expectation over infinitely many masks, we model the masked fused representations as Gaussian and optimize closed-form variational bounds, whose population-moment gaps are controlled by the masking-induced covariance under this model. On the evaluated benchmarks, InfMasking improves predictive performance and yields higher mean PID estimates of task-label synergy in learned modality-specific representations, alongside higher InterSHAP interaction attribution in the trained predictors. Code is available at https://github.com/brightest66/InfMasking.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07377

Designing with Cinematic Language: An Intent-Driven Approach to Human-AI Video Clip Co-Creation

Yi He, Zhengxi Yu, Feilin Han, Chuer Chen, Shengqi Dang, Nan Cao

原文摘要

Generative video models have made video creation more accessible, yet creators still struggle to translate high-level narrative intentions into coherent cinematic expressions. Existing systems primarily rely on prompt refinement or expose isolated camera controls, leaving a gap between what to express and how to film it. We present CineAssist, a human-AI co-creation system that treats cinematic language as an interactive design medium connecting creative intentions with executable video controls. We construct a Cinematic Language Design Space and an annotated dataset from 107 filmmaking sources and 1,436 film clips, linking communicative intentions to cinematic parameter values. CineAssist combines a multi-agent workflow for intent alignment and strategy recommendation with an interactive cinematic editor and a context-sensitive preference learner for user-controlled personalization. We evaluate the system through three studies examining end-to-end usability, cinematic quality, and preference evolution. Results show that CineAssist facilitates efficient, controllable, and personalized intent-driven cinematic video creation.

cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06430

TOP: Transmission-Oriented Remote Sensing Pansharpening

Zi-Han Cao, Jing-Ze Liang, Shao-Jie Wu, Jin-Cheng Di, Liang-Jian Deng, Gemine Vivone

原文摘要

Pansharpening has been extensively studied as a fundamental image fusion task in remote sensing. However, most existing methods focus on improving fusion quality under the implicit assumption that high-resolution observations have already been fully downlinked to the ground. As a result, existing models are highly vulnerable to perturbations introduced by lossy compression used for downlink transmission. To address this limitation, we present, to the best of our knowledge, the first transmission oriented pansharpening (TOP) framework, which unifies compact latent coding, onboard pansharpening, and efficient downlink transmission within a single satellite-toground pipeline. TOP encodes remote-sensing observations into a compact binary latent space, where a lightweight onboard fusion module can efficiently predict the fused binary latent code. Since only the compact fused code is downlinked, the transmission payload is substantially reduced. Overall, TOP enables low-payload downlink and lightweight onboard processing while maintaining high fusion quality under severe bandwidth constraints. Experiments across multispectral and hyperspectral settings show that TOP consistently achieves superior fusion quality at higher compression ratios than representative pansharpening methods paired with standard lossy codecs. For example, on GaoFen-2, TOP attains a fusion PSNR of 31.4 dB at a downlink compression ratio of 50x. This design also points toward transmissionoriented processing for other remote-sensing tasks. The code will be made available at https://github.com/JingZe-Liang/TOP.git.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06422

Unaligned Image Guided Denoising via Cross-modal Conditional Flow Matching

Runmin Zhang, Linshan Li, Si-Yuan Cao, Zhu Yu, Lingyu Zhu, Huaqi Zhang, Hui-Liang Shen

原文摘要

This work presents UGD-FM, a novel unaligned image guided denoising framework based on cross-modal conditional flow matching. Unlike previous approaches that assume spatially aligned inputs or only handle small-baseline rectified pairs, we address a more challenging setting where severe target noise and large-range two-dimensional cross-modal misalignment coexist. Specifically, we formulate guided denoising as a progressive flow matching process, allowing image restoration and aligned guidance aggregation to mutually reinforce each other. To support efficient few-step inference, we introduce a stage-focused and inference-consistent training strategy that focuses on task-critical denoising stages, and mitigates the training-inference mismatch. We further propose adaptive sparse guidance propagation for efficient and reliable large-range matching, regularized by perceptual and geometric auxiliary losses. Experiments demonstrate that UGD-FM achieves state-of-the-art denoising performance with only one or two inference steps, and is compatible with modern single-image restoration backbones.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.16745

VicEdit: Learning to Edit Videos from Visual In-Context Examples

Yuji Wang, Teng Hu, Yuheng Chen, Ran Yi, Han Feng, Weijian Cao, Chengjie Wang, Lizhuang Ma 等 9 位作者

原文摘要

Despite remarkable progress in instruction-based video editing, textual descriptions remain limited in expressing fine-grained appearance, spatial transformations, and temporal dynamics. We introduce Visual In-context Editing, a new paradigm that shifts video editing from text-only instructions to visual demonstrations, where users specify desired edits through heterogeneous visual references including single images, image pairs, and video pairs. To establish this paradigm, we curate VicEdit-400K, the first large-scale dataset for visual in-context video editing, containing 400K high-quality samples across ten editing tasks with comprehensive semantic and visual consistency filtering. Building upon this foundation, we propose VicEdit, a unified framework that enables effective reasoning over diverse visual contexts and textual instructions. Specifically, we introduce Modality-Adaptive Semantic Distillation to extract modality-aware visual editing semantics and Dual-Context Injection to align visual contexts with textual editing intents. Extensive experiments on VicEdit-Bench demonstrate that VicEdit achieves state-of-the-art performance on both conventional instruction-based editing and visual in-context editing. Further controlled studies and human evaluations verify that VicEdit effectively leverages heterogeneous visual references, establishing visual in-context editing as a promising paradigm for controllable video editing.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05897

FWIK: Multi-Waypoint IK Selection via Lookahead Graph Search

Jinglan Cao, Guohui Zhong, Chengwei Zhang

原文摘要

An ordered sequence of end-effector poses can give a redundant robot arm multiple valid joint configurations at each waypoint. Inverse kinematics (IK) computes these configurations. Choosing a configuration only for the next waypoint can leave the arm poorly placed for later waypoints. This may increase planned motion time or cause a later planning failure. This paper presents Future-Window Inverse Kinematics (FWIK), an online method that selects IK solutions by considering a small window of future waypoints. FWIK generates several IK candidates, connects them in a layered joint-space graph, and ranks candidate paths with a simple motion-time estimate. A learned model removes paths whose transitions are likely to cause self-collision. A standard MoveIt 2 planner then checks the next-waypoint IK goals from the remaining paths in ranked order, and the first goal that yields a valid trajectory is selected for execution. We evaluate FWIK in simulation on two 7-DoF manipulators, the Franka Emika Panda and the KUKA LBR iiwa. Each method-planner setting contains five repetitions of 100 sequences with 50 waypoints. Across Pilz, RRT-Connect, CHOMP, and STOMP, FWIK completes 96.8-100% of sequences and gives the shortest planned trajectory duration in all eight robot-planner settings. Its mean duration is 12.8-13.1% lower than STAMPEDE, 22.5-25.6% lower than IKLink, and 27.5-37.2% lower than retry-based TRAC-IK. The results show that a small future window can improve sequence completion and reduce planned motion time while leaving final trajectory validation to the motion planner.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05811

ReTailor: A Residual Feature Tailoring Framework for Real-Time Cross-Sensor Pansharpening Adaptation

Jing-Ze Liang, Wei-Jie Xiao, Zi-Han Cao, Yang-Dong Li, Tian-Yi Hu, Liang-Jian Deng

原文摘要

Recent pansharpening models achieve strong fusion performance on trained sensors, yet often generalize poorly to unseen sensors. Existing cross-sensor adaptation is limited by target-sensor retraining, which requires target-sensor training pairs and a dedicated training stage, and by per-image zero-shot adaptation, which is computationally expensive due to whole-network optimization for each test image. To address these problems, we propose ReTailor, a real-time sequential cross-sensor adaptation framework. Only a lightweight residual Feature Tailor is rapidly optimized under self-supervised, physics-aware constraints to correct cross-sensor shifts without modifying the well-learned fusion prior. For efficient and effective deployment, ReTailor combines content-aware patch selection, which identifies a few informative patches per image for optimization at negligible cost to improve both quality and efficiency, with rolling residual bootstrap, which inherits sensor-related knowledge across the image stream to further improve quality. Extensive experiments on cross-sensor adaptation settings show that ReTailor mitigates cross-sensor degradation both effectively and efficiently. Moreover, we compare ReTailor with Low-Rank Adaptation (LoRA), a widely used method for fine-tuning, to demonstrate the advantages of our approach. We further evaluate the core principle of ReTailor on two additional remote sensing tasks: super-resolution pansharpening and image segmentation, where it consistently improves performance. In general, ReTailor provides a highly effective and efficient strategy for deploying pansharpening models under real-world cross-sensor degradation, while also offering useful insights into managing cross-sensor degradation in other remote sensing tasks. Code will be released at https://github.com/JingZe-Liang/ReTailor .

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05802

GREP: Grounding Generated Room Evaluation in Principles

Mohan Cao, Yongsen Mao, Chucheng Xiang, Zhongyuan Liu, Zhihang Zhong, Haiping Wang, Yuan Liu

原文摘要

Recent LLM-centric advances can generate attractive furnished rooms by selecting and arranging assets. Yet evaluation remains limited to specific perspectives or predefined questions, failing to reflect overall scene quality. To address this pressing need, we introduce GREP. Its core is a focus shift from defining specific questions to establishing a three-tier evaluation philosophy: basic physical validity checks whether a scene obeys physical constraints; visual coherence concerns compatibility in object scale, style, and composition; and, at the highest level, human-use logic requires object arrangements to align with everyday human habits. Guided by these principles, an evaluation agent autonomously and adaptively identifies issues from scene context, gathers evidence, and produces scores and reports. This supports scene-specific issue discovery and reliable judgments. Experiments show that (1) GREP achieves 87% agreement with human majority preferences, substantially outperforming baseline evaluators. (2) GREP reports improve scene quality through refinement, whereas score-only and no-feedback baselines show slight declines. (3) GREP suggests that as planning ability improves, recent models such as GPT-5.6 Sol can move from relying on pipelines and harnesses to challenging their guidance and constraints, but still lack the ability to resolve the resulting conflicts. Stronger models are therefore expected not only to plan well, but also to adapt to external guidance and constraints more flexibly. Project page: https://han-808.github.io/GREP/

cs.CV · cs.AI · cs.GR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03412

DeliverBench: Benchmarking AI Agents on Delivering Realistic Multi-Stage Professional Projects

Yumou Liu, Mingju Chen, Zhoufutu Wen, Qianyu Yang, Zijie Wang, Sihan Chen, Yeqian Liu, Chenghao Yang 等 24 位作者

原文摘要

LLM agents are judged by what they deliver, and in professional work what gets delivered is a project, not a task. A project pays off only if the whole package holds, and one weak part can limit the value of the rest. Yet existing benchmarks mostly test agents on one task at a time, leaving a gap between the capability we measure and the outcome that makes agents useful. To address this gap, we introduce DeliverBench, which tests whether agents can carry a project from brief to final delivery. Its 11 projects, spanning financial due diligence, legal contract drafting, and drug safety reporting, are reconstructed with 13 domain experts from real materials and chain 150 subtasks through 2-15 rounds of new materials and client feedback. Because no reference outputs are injected, agents inherit their own errors from one task to the next. Expert rubrics then score every checkpoint as well as the final package. Across seven model-harness systems, even the best system scores only 65.0 out of 100 on final delivery and passes 50.0% of subtasks at a 90-point threshold. The failures are often failures of carrying work forward: in one audit project, all seven systems addressed the review findings, but only one preserved traceability. Together, these results suggest that task-level capability does not simply compose into delivery, which also depends on keeping the work coherent as it evolves.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04466

From Watching to Steering: Enabling Human-Coding Agent Collaboration through Execution Traces

Xiaoke Yan, Jiaying Lei, Danqing Shi, Zichen Wang, Nan Cao

原文摘要

Recent advances in large language models (LLMs) have enabled coding agents to autonomously perform complex software development tasks, yet their long-running execution remains difficult for users to inspect, understand, and steer. We present Vibetrace, an interactive trace visualization and intervention system for agentic programming built upon OpenCode. Vibetrace introduces a human-agent collaboration paradigm of Observe, Understand, Intervene, and Evolve. It (1) visualizes agent workflows as interactive execution traces for inspecting actions, intermediate states, and failures; (2) enables users to fork execution from prior steps and provide targeted corrections without restarting validated progress; and (3) distills traces and human feedback into reusable skills for continual agent evolution. On SWE-bench Verified, distilled skills reduce input tokens by 23.0% and cost by 15.5%, while improving pass rate from 65.0% to 68.0%. User studies further demonstrate improved accuracy and efficiency over conventional log-based interaction.

cs.HC · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2510.00279

SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion

Trung Hoang Le, Tran Cao Son, Ishtiaq Ahmed, Huiping Cao

原文摘要

Logical rule-based methods offer an interpretable approach to knowledge graph completion (KGC) by capturing compositional relationships in the form of human-readable inference rules. While existing logical rule-based methods learn rule confidence scores, they typically assign a global weight to each rule schema, applied uniformly across the graph. This is a significant limitation, as a rule's importance often varies depending on the specific query instance. To address this, we introduce SLogic (Subgraph-Informed Logical Rule learning), a novel framework that assigns query-dependent scores to logical rules. The core of SLogic is a context-aware scoring function. This framework is in line with the specificity principle in commonsense reasoning. This function determines the importance of a rule by analyzing the subgraph locally defined by the query's head entity, thereby enabling a differentiated weighting of rules specific to their local query contexts. Extensive experiments on benchmark datasets show that SLogic achieves competitive performance among rule-based methods. It also generates query-dependent, human-readable logical rules that serve as explicit explanations for its inferences.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03963

ProteoCraft: Protein Design as Proposal-Verification Search

Fang Wu, Hongbin Lin, Bingchen Duan, Yuchen Wang, Li Erran Li, Yanjie Huang, Mingxuan Cao, Yijun Ma 等 27 位作者

原文摘要

Protein-protein interactions underpin nearly all biological processes. Yet, designing proteins with desired interactions remains challenging due to the structural complexity governing molecular recognition. This study views protein design as a search optimization problem over a combinatorial sequence space under expensive structural evaluation. Toward this goal, we introduce a novel tree-based search framework, ProteoCraft, that employs approximate structural gradients from diffusion-based structure predictors as effective proposal policies to specify where the search should explore. Complete no-gradient inference of folding models separately assigns values to the resulting sequences, determining what is good. ProteoCraft allocates this verification budget through a coarse-to-fine paradigm: multi-site edits first explore distinct sequence basins, then verified promising states undergo strict single-site refinement. Across fourteen de novo binder-design settings spanning eight target proteins, five immune-relevant TCR-facing targets, and four localized CDR redesign tasks, ProteoCraft improves design quality and high-confidence yield. These improvements persist under independent AF2-IG and Rosetta-based interface evaluation. Prospective experimental validation further yielded an 82.9% binding hit rate for PD-L1 miniproteins, with a best measured KD of 0.879 nM, and confirmed binding of designed IFNAR2 VHH. Our code is available at https://github.com/smiles724/ProteoCraft_v1.

cs.CE · cs.AI · q-bio.BM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02710

MicroMouse: Masking Harmful Supervision in Long CoT via Relative Potential

Xiaowei Zhu, Hengzhu Tang, Zhenyu Zhang, Shuohuan Wang, Yubing Ren, Yanan Cao

原文摘要

Even successful long CoTs may contain harmful supervision, motivating the need for methods that can automatically identify and mask such signals during SFT. Inspired by the MicroMouse maze-solving process, we view a long CoT as a search trajectory from the input problem toward the target answer. This perspective motivates maintaining a goal-relative measure, analogous to a MicroMouse's spatial coordinates, to quantify how close the current reasoning state is to the target answer. Building on this perspective, we propose MicroMouse, an automated selective-SFT framework for masking harmful supervision in long CoTs. In MicroMouse we introduce a Relative Potential (REP) to measure how closely each reasoning state supports the gold answer. Unlike the perplexity of the gold answer alone, REP measures relative support by contrasting the gold answer with a matched foil. This contrast cancels likelihood variations shared by the pair, yielding a more stable signal of progress toward the target answer. By tracking the REP trajectory, MicroMouse identifies consecutive potential decreases as erroneous exploration and post-peak reasoning as redundant wandering, and masks these harmful supervision during SFT. Extensive experiments across multiple student model scales and reasoning benchmarks show that MicroMouse consistently outperforms Full-CoT SFT and existing selective-SFT baselines, yielding average relative gains of 5.5% in greedy accuracy and 2.6% in Pass@1 relative to Full-CoT SFT. Meanwhile, MicroMouse improves reasoning efficiency by reducing average output length by 8.8%, while identifying harmful supervision in only 1.7 seconds per long CoT on average.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.22538

STAGE: Stateful Translation to Agentic Graph Execution with Policy-Scoped Context and Deterministic Control

Mengxi Luo, Changjia Chen, An Cao, Wanyi Dai, Zirong Huang, Shixiong Gao, Ruoqi Shu, John Chen 等 9 位作者

原文摘要

Enterprise agents are expected to deliver correct and consistent outcomes while executing complex procedures, adhering to governing policies, and operating within practical resource budgets. We present \textsc{Stage}, an integrated execution framework combining typed task nodes, shared execution memory, document tools, and deterministic orchestration. We evaluate \textsc{Stage} on SOP-Bench Referral Abuse, the Retail and Telecom domains of $τ^2$-bench, and two proprietary banking workflows, Smart Dispute and Paystub Fraud Detection. Using the same underlying model in each comparison, \textsc{Stage} improves single-run success and repeated-run reliability over the baseline in all 18 benchmark--model pairs. Success across all three runs (\(\mathrm{Pass}^{3}\)) increases by 7.5--66.0 percentage points. Fixed-graph ablations demonstrate the contributions of localized policy context and coordinator-owned routing. Further analysis of Smart Dispute shows improved coverage of required operations across procedure lengths. These findings show that integrating local reasoning, persistent state, and procedural control can improve reliable enterprise workflow completion without changing the underlying model.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37179

Irreducibility Testing of Sparse Polynomials over Integers is NP-hard and Probabilistic Polynomial Time

Yichuan Cao, Xiao-Shan Gao, Qiao-Long Huang, Jiawei Nie

原文摘要

In this paper, we prove that irreducibility testing for sparse polynomials in $\mathbb{Z}[X]$ is coNP-hard under deterministic polynomial-time many-one reductions. That is, there exists no polynomial-time irreducibility test algorithm unless $\mathrm P=\mathrm{NP}$. We also provide an upper bound for this problem by demonstrating that the same irreducibility testing problem lies in $\mathsf{PP}\subseteq\mathsf{PSPACE}$; in other words, it can be solved in probabilistic polynomial time and, moreover, within polynomial space. The fact that $\mathsf{SparseIrred}_{\mathbb{Z}}\in\mathsf{PP}$ relies on the quasi-Riemann hypothesis result of Cureton--Thorner and on the uniform effective prime-ideal bound by Frei--Sofos--Widmer just posted recently.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37037HF 社区推荐

Zooming without Zooming v2: From Seeing Details to Connecting Evidence for Generalizable Multimodal Perception

Lai Wei, Yatai Li, Liangbo He, Jun Lan, Renjie Gu, Linhan Cao, Junkai Lin, Huijia Zhu 等 13 位作者

原文摘要

Multimodal perception forms the foundation of multimodal intelligence. In particular, spatial perception and multimodal long-context understanding are two pivotal tasks that underpin embodied AI and multimodal agents. However, synthesizing targeted, high-quality training data for these tasks remains difficult and costly. Our key insight is that these tasks share an underlying capability, which we call connecting evidence: identifying relevant visual facts, binding them to the correct entities, recovering their relations, and composing them into an answer. Crucially, targeted training data are far easier to synthesize for this capability than for these tasks themselves. To strengthen this capability, we propose EvidenceConnect, an easy-to-implement data synthesis framework that uses zoomed views of related regions within a single image to generate questions requiring comparison, aggregation, or chaining of visual evidence. To ensure quality, a reference answer is retained only when independent VLMs agree on it, and region-ablation tests discard questions whose answer survives the removal of selected regions. We then train base VLMs on the resulting data with RLVR, obtaining Zooming without Zooming v2 (ZwZ2). Although the synthesized data are not tailored to any downstream benchmark, ZwZ2 improves fine-grained and spatial perception, with further gains on embodied spatial reasoning, multimodal long-context understanding, multimodal agent tasks, and general visual reasoning. These results establish EvidenceConnect as an effective framework for supervising evidence connection and suggest that strengthening this shared capability leads to more generalizable multimodal perception.

cs.CV · cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35080

Zero-Shot Multi-Class Wafer Map Classification with Rule-Ordered Prompts for Large Multimodal Models

Tianjian Cao, Jun Tao

原文摘要

Wafer-map defect classification often requires labeled data and retraining when production conditions change. We present a training-free six-class framework that combines connected-component highlighting with morphology-, location-, and exclusion-aware prompts for a frozen large multimodal model. On Mixed-WM38 (6,866 maps), multi-region prompting achieves 92.9% macro and 93.8% overall accuracy. On WM811K (9,000 maps), it achieves 79.1% and 81.3%, respectively. Eight alternative rule orders achieve 90.1-91.9% overall accuracy, compared with 93.8% for the selected order.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35036

Mapping the Solution-Set Structure of Grokking on Latin Squares

Zihan Gu, Lulu Zhao, Tianyi Ma, Hua Zhang, Wenbo Su, Yanan Cao, Yue Hu

原文摘要

Grokking is a delayed transition from memorisation to generalisation. A network first fits observed examples while remaining near chance on held-out inputs, then generalises abruptly. Prior work has focused mainly on this delay, finding that the network's main loss becomes weight decay after the fitting phase. We name the solution to this optimization problem as the minimum-norm endpoint, and study which tasks make all minimum-norm endpoints generalise from partial observations. Specifically, we model pairwise input tasks as edge colourings of $K_{q,q}$, with Latin colourings as our structured family, and classify each observed instance as all-good, mixed, or no-good by its endpoint set. Furthermore, we prove two contrasting results: (1) Finite-abelian-group families are asymptotically all-good from partial observations; (2) Under a uniform random Latin square, all-good tasks become asymptotically rare for observation-based learners, leaving mixed or no-good behaviour. Moreover, experiments show that relabellings of addition generalise despite their dense polynomial representations. Finally, on tasks of composite order, finite-training results suggest that predictors learn a coarse part of the output before the full label.

cs.LG

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.3ACL Anthology · ACL 2026

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He 等 59 位作者

原文摘要

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1211ACL Anthology · ACL 2026

Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection

Xiaowei Zhu, Yubing Ren Institute of Information Engineering, Chinese Academy of Sciences, Fang Fang Institute of Information Engineering,Chinese Academy of Sciences, Shi Wang Chinese Academy of Sciences, Yanan Cao Institute of Information Engineering, Chinese Academy of Sciences, Li Guo Institute of Information Engineering, Chinese Academy of Sciences

原文摘要

The rapid advancement of large language models has increasingly blurred the boundary between human-written and AI-generated text, raising societal risks such as misinformation dissemination, authorship ambiguity, and threats to intellectual property rights. These concerns highlight the urgent need for effective and reliable detection methods. While existing training-free approaches often achieve strong performance by aggregating token-level signals into a global score, they typically assume uniform token contributions, making them less robust under short sequences or localized token modifications. To address these limitations, we propose Exons-Detect, a training-free method for AI-generated text detection based on an exon-aware token reweighting perspective. Exons-Detect identifies and amplifies informative exonic tokens by measuring hidden-state discrepancy under a dual-model setting, and computes an interpretable translation score from the resulting importance-weighted token sequence. Empirical evaluations demonstrate that Exons-Detect achieves state-of-the-art detection performance and exhibits strong robustness to adversarial attacks and varying input lengths. In particular, it attains a 2.2% relative improvement in average AUROC over the strongest prior baseline on DetectRL.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1420ACL Anthology · ACL 2026

EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment

Yixuan Nan, Xixun Lin Institute of Information Engineering, Chinese Academy of Sciences, Yanmin Shang University of the Chinese Academy of Sciences, Ge Zhang Donghua University, Zheng Fang, Fang Fang Institute of Information Engineering,Chinese Academy of Sciences, Yanan Cao Institute of Information Engineering, Chinese Academy of Sciences

原文摘要

Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object and plays a critical role in knowledge fusion and integration. Traditional EA methods mainly rely on knowledge representation learning, but their performance is often limited under noisy or sparsely supervised scenarios. Recently, large language models (LLMs) have been introduced to EA and achieved notable improvements by leveraging rich semantic knowledge. However, existing LLM-based EA approaches typically treat LLMs as black-box decision makers, resulting in limited interpretability, and the direct use of large-scale triples substantially increases inference cost. To address these challenges, we propose EA-Agent, a reasoning-driven agent for EA. EA-Agent formulates EA as a structured reasoning process with multi-step planning and execution, enabling interpretable alignment decisions. Within this process, it introduces attribute and relation triple selectors to filter redundant triples before feeding them into the LLM, effectively addressing efficiency challenges. Experimental results on three benchmark datasets demonstrate that EA-Agent consistently outperforms existing EA methods and achieves state-of-the-art performance. The source code is available at https://anonymous.4open.science/r/EA-Agent-5696.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1473ACL Anthology · ACL 2026

Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations

Yilong Liu Institute of Information Engineering, Chinese Academy of Sciences, Xixun Lin Institute of Information Engineering, Chinese Academy of Sciences, Pengfei Cao Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Ge Zhang Donghua University, Fang Fang Institute of Information Engineering,Chinese Academy of Sciences, Yanan Cao Institute of Information Engineering, Chinese Academy of Sciences

原文摘要

Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to tools that are irrelevant to the user’s query, in which case the desired behavior is to refrain from invocations. In this work, we identify a widespread yet overlooked mechanistic flaw in tool refusal, which we term structural alignment bias: Even when a tool fails to serve the user’s goal, LLMs still tend to invoke it whenever query attributes can be validly assigned to tool parameters. To systematically study this bias, we introduce SABEval, a new dataset that decouples structural alignment from semantic relevance. Our analysis shows that structural alignment bias induces severe tool-invocation errors in LLMs, yet remains largely unaccounted for in existing evaluations. To investigate the internal mechanisms underlying this bias, we propose Contrastive Attention Attribution, which reveals two competing pathways for semantic checking and structural matching. The relative strength of these pathways drives LLMs’ tool invocation decisions. Based on these findings, we further introduce a rebalancing strategy that effectively mitigates structural alignment bias, as demonstrated by extensive experiments, without degrading general tool-use capabilities.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1479ACL Anthology · ACL 2026

Generating then Refining for Reliable Knowledge Base Question Answering

Jianqi Gao Shanghai University, Hang Yu, Jian Cao Shanghai Jiaotong University, Ranran Bu, Jinghua Tang Shanghai Jiaotong University, Nengjun Zhu Shanghai University, Yonggang Zhang

原文摘要

Knowledge Base Question Answering (KBQA) aims to retrieve accurate answers to natural language queries by retrieving and reasoning over large-scale structured knowledge bases (KBs). Advanced semantic parsing-based methods promoted by large language models (LLMs) demonstrate superior performance by transforming questions into structured queries, i.e., logical forms (LFs). However, LFs generated by LLMs could be non-executable due to the inherent semantic hallucination issue of LLMs and the complex graph retrieval characteristics of the KBQA task. To address this challenge, we propose a novel “generate-verify-refine” framework, termed Action-Reflection-Integrated KBQA (ARI-KBQA) for reliable LF generation. ARI-KBQA introduces a dual-module cooperative architecture: First, an action generator is trained to produce initial query paths based on a hop-by-hop reasoning strategy. Then a reflection verifier dynamically validates path feasibility by interacting with the KBs. Consequently, ARI-KBQA filters out invalid LFs and provides semantic correction feedback to the action generator for iteratively refining LFs. Evaluations on standard KBQA benchmarks show that the proposed ARI-KBQA significantly enhances model performance with a reduced search space, especially in complex multi-hop query scenarios.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.235ACL Anthology · ACL 2026

Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection

Yang Li, Qiang Sheng Institute of Computing Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Zhengjia Wang, Yehan Yang, Danding Wang Institute of Computing Technology, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Juan Cao Institute of Computing Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences

原文摘要

The misuse of large language models (LLMs) requires precise detection of synthetic text. Existing works mainly follow binary or ternary classification settings, which can only distinguish pure human/LLM text or collaborative text at best. This remains insufficient for the nuanced regulation, as the LLM-polished human text and humanized LLM text often trigger different policy consequences. In this paper, we explore fine-grained LLM-generated text detection under a rigorous four-class setting. To handle such complexities, we propose RACE (Rhetorical Analysis for Creator-Editor Modeling), a fine-grained detection method that characterizes the distinct signatures of creator and editor. Specifically, RACE utilizes Rhetorical Structure Theory (RST) to construct a logic graph for the creator’s foundation while extracting Elementary Discourse Unit (EDU)-level features for the editor’s style. Experiments show that RACE outperforms 12 baselines in identifying fine-grained types with low false alarms, offering a policy-aligned solution for LLM regulation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.286ACL Anthology · ACL 2026

Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments

Hao Mi Institute of Computing Technology, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Qiang Sheng Institute of Computing Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Shaofei Wang, Beizhe Hu Institute of Computing Technology, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Yifan Sun, Zhengjia Wang, Hengqi Zeng, Yang Li 等 10 位作者

原文摘要

Large Language Models (LLMs) are prone to factual hallucinations, risking their reliability in real-world applications. Existing hallucination detectors mainly extract micro-level intrinsic patterns for uncertainty quantification or elicit macro-level self-judgments through verbalized prompts. However, these methods address only a single facet of the hallucination, focusing either on implicit neural uncertainty or explicit symbolic reasoning, thereby treating these inherently coupled behaviors in isolation and failing to exploit their interdependence for a holistic view. In this paper, we propose LaaB (Logical Consistency-as-a-Bridge), a framework that bridges neural features and symbolic judgments for hallucination detection. LaaB introduces a “meta-judgment” process to map symbolic labels back into the feature space. By leveraging the inherent logical bridge where response and meta-judgment labels are either the same or opposite based on the self-judgment’s semantics, LaaB aligns and integrates dual-view signals via mutual learning and enhances the hallucination detection. Extensive experiments on 4 public datasets, across 4 LLMs, against 8 baselines demonstrate the superiority of LaaB.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.30ACL Anthology · ACL 2026

Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety

Can Jin Rutgers University, Rui Wu, Tong Che NVIDIA, Qixin Zhang Nanyang Technological University, Hongwu Peng Adobe Research, Jiahui Zhao University of Connecticut, Zhenting Wang Mohamed bin Zayed University of Artificial Intelligence, Wenqi Wei Fordham University 等 13 位作者

原文摘要

Ensuring that Large Language Models (LLMs) adhere to safety principles without refusing benign requests remains a significant challenge. While OpenAI introduces deliberative alignment (DA) to enhance the safety of its o-series models through reasoning over detailed “code-like” safety rules, the effectiveness of this approach in open-source LLMs, which typically lack advanced reasoning capabilities, is understudied. In this work, we systematically evaluate the impact of explicitly specifying extensive safety codes versus demonstrating them through illustrative cases. We find that referencing explicit codes inconsistently improves harmlessness and systematically degrades helpfulness, whereas training on case-augmented simple codes yields more robust and generalized safety behaviors. By guiding LLMs with case-augmented reasoning instead of extensive code-like safety rules, we avoid rigid adherence to narrowly enumerated rules and enable broader adaptability. Building on these insights, we propose CADA, a case-augmented deliberative alignment method for LLMs utilizing reinforcement learning on self-generated safety reasoning chains. CADA effectively enhances harmlessness, improves robustness against attacks, and reduces over-refusal while preserving utility across diverse benchmarks, offering a practical alternative to rule-only DA for improving safety while maintaining helpfulness.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.578ACL Anthology · ACL 2026

A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement

Shengji Tang, Jianjian Cao, Weihao Lin, Jiale Hong, Bo Zhang AI for Science, Shanghai AI Laboratory, Shuyue Hu, Lei Bai Shanghai AI Laboratory, Tao Chen Fudan University 等 10 位作者

原文摘要

Existing multi-LLM collaboration systems often encounter scalability challenges when integrating new LLMs and tasks, leading to suboptimal performance. To address this, we propose SMCS, a Scalable Multi-LLM Collaboration System designed to effectively coordinate multiple open-source LLMs. The system consists of two core components: a Retrieval-based Prior Selection (RPS) module, which dynamically selects the most suitable LLMs for each input, and an Exploration–Exploitation-Driven Posterior Enhancement (EPE) module, which fosters response diversity and selects high-quality outputs through a hybrid scoring mechanism. Experiments on eight mainstream benchmarks validate the effectiveness of our system: by integrating fifteen open-source LLMs, SMCS outperforms prevailing closed-source LLMs, e.g., GPT-4.1(+5.36%) and GPT-o3-mini(+5.28%) across multiple tasks. Remarkably, it even exceeds the average of best results on different datasets with open-source LLMs (+2.86%), significantly advancing the empirical performance frontier of open-source collaboration. The code is released at https://github.com/magent4aci/SMCS.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.635ACL Anthology · ACL 2026

Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation

Katelyn X. Mei, Yi-Li Hsu, Minjoon Choi University of Maryland, College Park, Zongwan Cao University of Washington, Chenjun Xu University of Washington, Bingbing Wen University of Washington, Su Lin Blodgett Mila - Quebec Artificial Intelligence Institute, Lucy Lu Wang University of Washington and Allen Institute for Artificial Intelligence

原文摘要

Human evaluation plays a critical role in assessing the quality of generated text. However, the reliability and reproducibility of these evaluations depend on transparent and well-documented protocols—details that are frequently missing in current practice. In this work, we conduct a large-scale analysis of human evaluation protocols for evaluating long-form generation tasks in *CL conference publications from 2023–2025, including a full manual review of 284 papers and LLM-assisted analysis for another 1.8k+ papers. We define a set of 20 reportable criteria related to reproducibility of human evaluation studies, and apply these criteria to systematically examine reporting norms and practices within the community. We find widespread under-reporting of important aspects of human evaluation study design, leading to ambiguity about what was measured and how, who contributed judgments, and how judgments should be interpreted. Based on these findings, we outline actionable recommendations to support more transparent and reproducible reporting in future research.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.647ACL Anthology · ACL 2026

DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing

Qian Cao, Yahui Liu, Wei Bi Independent Researcher, Yi Zhao, Ruihua Song, Xiting Wang Renmin University of China, Ruiming Tang, Guorui Zhou Kuaishou- 快手科技 等 9 位作者

原文摘要

Reinforcement learning (RL)-based enhancement of large language models (LLMs) often leads to reduced output diversity, undermining their utility in open-ended tasks like creative writing. Current methods lack explicit mechanisms for guiding diverse exploration and instead prioritize optimization efficiency and performance over diversity. This paper proposes an RL framework structured around a semi-structured long Chain-of-Thought (CoT), in which the generation process is decomposed into explicitly planned intermediate steps. We introduce a Diverse Planning Branching method that strategically introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories. Experimental results on creative writing benchmarks demonstrate that our approach significantly improves output diversity without compromising generation quality, consistently outperforming existing baselines.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.815ACL Anthology · ACL 2026

CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems

Yongxuan Wu, Xixun Lin Institute of Information Engineering, Chinese Academy of Sciences, He Zhang Griffith University, Nan Sun Institute of Information Engineering, Chinese Academy of Sciences, Kun Wang, Chuan Zhou Academy of Mathematics and Systems Science, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences, Shirui Pan Griffith University, Yanan Cao Institute of Information Engineering, Chinese Academy of Sciences

原文摘要

LLM-based Multi-Agent Systems (MAS) have demonstrated remarkable capabilities in solving complex tasks. Central to MAS is the communication topology which governs how agents exchange information internally. Consequently, the security of communication topologies has attracted increasing attention. In this paper, we investigate a critical privacy risk: MAS communication topologies can be inferred under a restrictive black-box setting, exposing system vulnerabilities and posing significant intellectual property threats. To explore this risk, we propose Communication Inference Attack (CIA), a novel attack that constructs new adversarial queries to induce intermediate agents’ reasoning outputs and models their semantic correlations through the proposed global bias disentanglement and LLM-guided weak supervision. Extensive experiments on MAS with optimized communication topologies demonstrate the effectiveness of CIA, achieving an average AUC of 0.87 and a peak AUC of up to 0.99, thereby revealing the substantial privacy risk in MAS. The source code is available at https://github.com/aabbbcd/CIA.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表