跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+2471新增论文
+2025其中 AI 相关
+163已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-11 03:54 · 全站最近入库：2026-11-10 15:22各信源进度 →

统计口径与数据新鲜度

统计 2026-11-10 04:53 — 2026-11-11 04:53（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-11 03:54最近发现新增版本：2026-11-10 15:22库内论文最新更新时间：2026-11-10 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

20,643 已收录论文17,531 AI 相关5,643 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 20,643 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-11 03:53（北京时间）
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.13ACL Anthology · ACL 2026

CLEAR: Cross-Lingual Enhancement in Retrieval via Reverse-training

Seungyoon Lee Korea University, Minhyuk Kim, Seongtae Hong Korea University, Youngjoon Jang Korea University, Dongsuk Oh Kyungpook National University, Heuiseok Lim

原文摘要

Existing multilingual embedding models often encounter challenges in cross-lingual scenarios due to imbalanced linguistic resources and less consideration of cross-lingual alignment during training. Although standardized contrastive learning approaches for cross-lingual adaptation are widely adopted, they may struggle to capture fundamental alignment between languages and degrade performance in well-aligned languages such as English. To address these challenges, we propose Cross-Lingual Enhancement in RetrievAl via Reverse-training (CLEAR), a novel loss function utilizing a reverse training scheme to improve retrieval performance across diverse cross-lingual retrieval scenarios. CLEAR leverages an English passage as a bridge to strengthen alignments between the target language and English, ensuring robust performance in the cross-lingual retrieval task. Our extensive experiments demonstrate that CLEAR achieves notable improvements in cross-lingual scenarios, with gains up to 15%, particularly in low-resource languages, while minimizing performance degradation in English. Furthermore, our findings highlight that CLEAR offers promising effectiveness even in multilingual training, suggesting its potential for broad application and scalability. We release the code at https://github.com/dltmddbs100/CLEAR.

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.130ACL Anthology · ACL 2026

ALIGN: Word Association Learning for Cultural Alignment in Large Language Models

Chunhua Liu University of Melbourne, Kabir Manandhar Shrestha University of Melbourne, Sukai Huang Monash University

原文摘要

Large language models (LLMs) exhibit cultural bias from over-represented viewpoints in training data, yet cultural alignment remains a challenge due to limited cultural knowledge and a lack of exploration into effective learning approaches. We introduce a cost-efficient and cognitively grounded method: fine-tuning LLMs on native speakers’ word-association norms, leveraging cognitive psychology findings that such associations capture cultural knowledge. Using word association datasets from native speakers in the US (English) and China (Mandarin), we train Llama-3.1-8B and Qwen-2.5-7B via supervised fine-tuning and preference optimization. We evaluate models’ cultural alignment through a two-tier evaluation framework that spans low-level lexical associations and high-level cultural value alignment using the World Values Survey. Results show significant improvements in lexical alignment (16–20% English, 43–165% Mandarin on Precision@5) and high-level cultural value shifts. On a subset of 50 questions where US and Chinese respondents diverge most, fine-tuned Qwen nearly doubles its response alignment with Chinese values (13 to 25). Remarkably, our trained 7–8B models match or exceed vanilla 70B baselines, demonstrating that a few million of culture-grounded associations achieve value alignment without expensive retraining. Our work highlights both the promise and the need for future research grounded in human cognition in improving cultural alignment in AI models.

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.1300ACL Anthology · ACL 2026

VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation Agents

Xunyi Zhao University of Adelaide, Gengze Zhou University of Adelaide, Qi Wu The University of Adelaide

原文摘要

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across a wide range of vision-language tasks. However, their performance as embodied agents, which requires multi-round interaction with spatial reasoning and sequential action prediction, needs further exploration. Our work investigates this potential in the context of Vision-and-Language Navigation (VLN) by introducing a unified and extensible simulation-free evaluation framework to probe MLLMs as zero-shot agents, named VLN-MME. Simplifying the evaluation with a highly modular and accessible design streamlines experiments, enabling structured comparisons and component-level ablations across diverse MLLM architectures, agent designs, and navigation tasks. Crucially, enabled by VLN-MME, we observe that enhancing prevalent agents with Chain-of-Thought (CoT) reasoning and self-reflection leads to an unexpected performance decrease. This suggests MLLMs exhibit poor context awareness in embodied navigation tasks; although they can follow instructions and structure their output, their 3D spatial reasoning fidelity is low. Furthermore, we demonstrate that agent performance could be largely improved with simple failure cases in context learning. VLN-MME lays the groundwork for systematic evaluation of general-purpose MLLMs in embodied navigation settings and reveals limitations in their sequential decision-making capabilities. We believe these findings offer crucial guidance for MLLM post-training as embodied agents.

cs.CL · cs.CV

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.1301ACL Anthology · ACL 2026

ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models

Chonghan Qin, Xiachong Feng University of Hong Kong, Weitao Ma Harbin Institute of Technology, Xiaocheng Feng, Lingpeng Kong Department of Computer Science, The University of Hong Kong

原文摘要

Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval. This gap is critical: effective assistants must automatically apply learned procedures or avoid failed actions without explicit reminders. We introduce ImplicitMemBench, the first systematic benchmark evaluating implicit memory through three cognitively grounded constructs drawn from standard cognitive-science accounts of non-declarative memory: Procedural Memory (one-shot skill acquisition after interference), Priming (theme-driven bias via paired experimental/control instances), and Classical Conditioning (Conditioned Stimulus–Unconditioned Stimulus (CS–US) associations shaping first decisions). Our 300-item suite employs a unified Learning/Priming-Interfere-Test protocol with first-attempt scoring. Evaluation of 17 models reveals severe limitations: no model exceeds 66% overall, with top performers DeepSeek-R1 (65.3%), Qwen3-32B (64.1%), and GPT-5 (63.0%) far below human baselines. Analysis uncovers dramatic asymmetries (inhibition 17.6% vs. preference 75.0%) and universal bottlenecks requiring architectural innovations beyond parameter scaling. ImplicitMemBench reframes evaluation from “what agents recall” to “what they automatically enact”.

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.1302ACL Anthology · ACL 2026

Semantic Hardness Is Not Visual Hardness: Sign-Aware Hard Negative Mining for Sign Language Retrieval

Junmyeong Lee, Chan Hur Electronics and Telecommunications Research Institute, ChangSu Choi, Sukmin Cho Korea Advanced Institute of Science and Technology, Fitsum Gaim, Eui Jun Hwang Korea Advanced Institute of Science & Technology, Hoyun Song Korea Advanced Institute of Science & Technology, KyungTae Lim Korea Advanced Institute of Science & Technology

原文摘要

Sign Language Retrieval (SLRet) enables efficient access to sign language content but remains fragile in fine-grained scenarios where visually similar signs must be distinguished. We show that this limitation does not stem from model capacity, but from ineffective hard negative supervision. Specifically, we formulate fine-grained retrieval failures as a negative distribution mismatch: semantically distinct yet visually confusable signs are rarely treated as hard negatives, while existing text-based mining strategies fail to capture such visual ambiguity. To address this issue, we propose Sign-Aware Hard Negative Mining (SAN), which constructs hard negatives based on visual confusability in the sign embedding space rather than linguistic similarity. Experiments on PHOENIX-2014T demonstrate that SAN substantially improves fine-grained retrieval performance while preserving coarse-grained accuracy, highlighting the importance of aligning negative supervision with visual ambiguity in sign language retrieval.

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.1303ACL Anthology · ACL 2026

Difference in Task Performance on Sparse Speech Representations

Wenjie Peng, Chen Chen University of Sheffield, Thomas Hain University of Sheffield

原文摘要

Learning speech representations that are useful for a variety of downstream tasks has received considerable attention, due to the outstanding properties of Self-Supervised Learning (SSL) trained models. Despite advancements in modelling methods, understanding the difference in task performance on representations is limited. Mainly motivated by the no-free-lunch theorem and speech production, this work investigates changes in task performance in sparse speech representations, providing interpretability analysis under the Information Bottleneck (IB) framework. Autoencoders with varying sparsity levels were trained using three SSL features, and evaluated on six tasks of SUPERB: Speech Enhancement (SE), Speaker Identification (SID), Speech Emotion Recognition (SER), Phone Recognition (PR), Automatic Speech Recognition (ASR) and Slot Filling (SF). Experiments show that: 1) different tasks manifest different degrees of sensitivity to the sparsity levels; 2) the optimal sparsity level for task performance varies; 3) the choice of SSL features has a limited impact on most tasks but with an exception of PR; 4) overall PR and ASR require more preservation of relevant information about the labels, while SID and SER demand more compression of irrelevant information, where the input quality can shift this trade-off to some degree. These findings can contribute to the design of a universal sparse speech representation learner.

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.1304ACL Anthology · ACL 2026

VerilogLAVD: LLM-Aided Pattern Generation for Verilog CWE Detection

Xiang Long Hangzhou Dianzi University, Yingjie Xia Hangzhou Dianzi University, Li Kuang Central South University, Yao Wan Huazhong University of Science and Technology, ZiHao Liu

原文摘要

LLMs often fail in hardware vulnerability detection due to the intrinsic semantic concurrency of HDLs (Hardware Description Language), where vulnerabilities arise from the interaction of multiple concurrent execution statements rather than a single sequential execution path. To address the problem, we propose VerilogLAVD, a LLM-Aided Vulnerability Detection framework by generating executable Traversal Detection Patterns (TDPs), i.e. the rules describing how to find the evidence of vulnerabilities in Verilog HDL. We first introduce a Unified Verilog Property Graph (VeriPG) that explicitly models parallel semantics by combining AST, CFG, and DDG. Furthermore, a semantic validation mechanism is designed to constrain and filter the LLM-generated TDPs. By executing these validated TDPs on VeriPG, our method produces stable and deterministic detection results. Experiments demonstrate that VerilogLAVD improves the F1 score by 133% compared to LLM-based methods. Furthermore, the framework successfully identifies real-world hardware vulnerabilities in open-source hardware design repositories.

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.1305ACL Anthology · ACL 2026

Temporal Sampling for Forgotten Reasoning in LLMs

Yuetai Li, Zhangchen Xu, Fengqing Jiang Apple and University of Washington, Bhaskar Ramasubramanian Western Washington University, Luyao Niu University of Washington, Bill Yuchen Lin xAI and University of Washington, Xiang Yue Carnegie Mellon University, Radha Poovendran University of Washington, Seattle

原文摘要

Fine-tuning large language models (LLMs) is intended to improve their reasoning capabilities, yet we uncover a counterintuitive effect: models often forget how to solve problems they previously answered correctly during training. We term this phenomenon Temporal Forgetting and show that it is widespread across model sizes, fine-tuning methods (both Reinforcement Learning and Supervised Fine-Tuning), and multiple reasoning benchmarks. Our analysis reveals on average more than 20% of final errors were once solved correctly at an earlier checkpoint. Inspired by the phenomenon of Temporal Forgetting, we proposed Temporal Sampling, a simple decoding strategy that draws outputs from multiple checkpoints along the training trajectory. This approach recovers forgotten solutions and leads to significant improvements in reasoning performance than final-ckpt-sampling only, gains from 4 to 19 points in Pass@k and consistent gains for majority-voting and Best-of-N across several benchmarks. Temporal sampling also outperforms strong baselines such as model merging. By leveraging the temporal diversity inherent in training, Temporal Sampling offers a practical, compute-efficient way to surface hidden reasoning ability and rethink how we evaluate LLMs.

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.1306ACL Anthology · ACL 2026

Conflict-Aware Memory for Embodied Agents: Enhancing Vector Data Quality via Detection Rules

Kexin Ma, Haotian Wang, Shenglin Chen, Yishuai Cai Peking University and National University of Defense Technology, Huangyuyu, Ruochun Jin National University of Defense Technology

原文摘要

Embodied agents have successfully leveraged large language models (LLMs) to better transform human instructions and images into executable task plans. Furthermore, memories of agents can be leveraged to achieve continual self-learning and optimization. However, vector data quality problems emerge in memories when they are projected into vector space, especially in discerning contextually similar but semantically conflicting sentences and highly similar images. This is particularly detrimental to embodied AI as it potentially distorts the robot’s actions. To address this challenge, we propose Conflict Detection Rules (CDRs) to identify and manage data quality issues in vector knowledge bases, which assist in correcting the index structure and further improving the answer quality. Experimental results show that planners with CDRs exceed the basic LLM planner by 15.25% and 14.25% in grammatical accuracy (GA) and interpretation accuracy (IA) on average, respectively. Moreover, the entire workflow has been successfully integrated into various scenarios, demonstrating its practical applicability and robustness in the real world.

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.1307ACL Anthology · ACL 2026

Benchmarking Deflection and Hallucination in Large Vision-Language Models

Nicholas Moratelli, Christopher Davis Amazon, Leonardo F. R. Ribeiro, Bill Byrne Amazon and University of Cambridge, Gonzalo Iglesias Amazon

原文摘要

Large Vision–Language Models (LVLMs) increasingly rely on retrieval to answer knowledge-intensive multimodal questions. Existing benchmarks overlook conflicts between visual and textual evidence and the importance of generating deflections (e.g., “Sorry, I cannot answer...”) when retrieved knowledge is incomplete. These benchmarks also suffer from rapid obsolescence, as growing LVLM training sets allow models to answer many questions without retrieval. We address these gaps with three contributions. First, we propose a dynamic data curation pipeline that preserves benchmark difficulty over time by filtering for genuinely retrieval-dependent samples. Second, we introduce VLM-DeflectionBench, a benchmark of 2,775 samples spanning diverse multimodal retrieval settings, designed to probe model behaviour under conflicting or insufficient evidence. Third, we define a fine-grained evaluation protocol with four scenarios that disentangle parametric memorization from retrieval robustness. Experiments across 20 state-of-the-art LVLMs indicate that models usually fail to deflect in the presence of noisy or misleading evidence. Our results highlight the need to evaluate not only what models know, but how they behave when they do not, and serve as a reusable and extensible benchmark for reliable KB-VQA evaluation. All resources will be publicly available upon publication.

cs.CL · cs.CV

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.1308ACL Anthology · ACL 2026

STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training

Qiuyi Qi Zhejiang University, Tian Liang Zhejiang University, Mutian Bao Zhejiang University, Jinjian Zhang Ant Group, Dongnan Liu Ant Group, Wei Zhou, Linjian Mo Ant Group, Ming Kong Zhejiang University 等 11 位作者

原文摘要

Reinforcement Learning (RL) is the dominant paradigm for training Large Language Model (LLM) agents on long-horizon tasks. However, sparse and delayed rewards often lead to trajectory neglect, in which agents lose focus on the task goal and interaction history at intermediate steps. Prior work has explored step-level supervision using Shannon-entropy–based uncertainty signals, which conflate inherent state complexity with agent confidence and therefore provide unreliable estimates of decision reliability. To address this issue, we propose normalized entropy, which measures confidence deviations relative to an agent’s average behavior under a given state, thereby strengthening the association between low-quality actions and trajectory neglect. Building on this insight, we introduce Selective Trajectory-Aware Policy Optimization (STAPO), a hierarchical group-based RL framework. STAPO leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a joint mechanism of trajectory-aware reward and trajectory-independent penalty, enhancing trajectory awareness while preserving training stability. Extensive experiments on ALFWorld, WebShop, and Search-Augmented QA demonstrate that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect, validating its effectiveness and robustness for agentic tasks.

cs.CL · cs.AI

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.1309ACL Anthology · ACL 2026

Read As Human: Compressing Context via Parallelizable Close Reading and Skimming

Jiwei Tang, Shilei Liu, Zhicheng Zhang, Qingsong Lv, Runsong Zhao, Tingwei Lu, Langming Liu Alibaba Group, Haibin Chen 等 12 位作者

原文摘要

Large Language Models (LLMs) demonstrate exceptional capability across diverse tasks. However, their deployment in long-context scenarios is hindered by two challenges: computational inefficiency and redundant information. We propose RAM (Read As HuMan), a context compression framework that adopts an adaptive hybrid reading strategy, to address these challenges. Inspired by human reading behavior (i.e., close reading important content while skimming less relevant content), RAM partitions the context into segments and encodes them with the input query in parallel. High-relevance segments are fully retained (close reading), while low-relevance ones are query-guided compressed into compact summary vectors (skimming). Both explicit textual segments and implicit summary vectors are concatenated and fed into decoder to achieve both superior performance and natural language format interpretability. To refine the decision boundary between close reading and skimming, we further introduce a contrastive learning objective based on positive and negative query–segment pairs. Experiments demonstrate that RAM outperforms existing baselines on multiple question answering and summarization benchmarks across two backbones, while delivering up to a 12x end-to-end speedup on long inputs (average length 16K; maximum length 32K).

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.131ACL Anthology · ACL 2026

RealTalk-CN: A Realistic Chinese Speech Task-Oriented Dialogue Benchmark with Cross-Modal Analysis

Enzhi Wang, Jiaming Zhou, Yuhang Jia, Aobo Kong, Qicheng Li Nankai University, Yong Qin Nankai University

原文摘要

Recent advances in speech large language models (e.g., GPT-4o) have enabled end-to-end spoken interactions, yet their robustness in real-world applications remains unclear, where systems must assist users in completing specific tasks under complex conditions such as multi-turn, ambiguous, and often spontaneous speech, as well as natural alternation between speech and text. Task-oriented dialogue (TOD) offers a realistic scenario to evaluate whether models can effectively help users accomplish such task-oriented goals, but existing benchmarks are mainly text-based, and the few speech datasets are limited to English and often neglect spontaneous disfluencies and speaker diversity. To address this gap, we introduce RealTalk-CN, the first Chinese multi-turn, multi-domain speech–text TOD dataset, containing 5.4k dialogues (60K turns, ~150 hours) of real human-to-human recordings with detailed annotations for dialogue states, disfluency types, and speaker characteristics. Based on this dataset, we propose a cross-modal interaction task supporting dynamic speech-text switching and a comprehensive evaluation protocol assessing robustness to disfluencies, sensitivity to speaker variation, and cross-domain generalization. Experiments on state-of-the-art models demonstrate the challenges posed by RealTalk-CN and establish its value as a benchmark for developing reliable and fair Speech LLMs in real-world deployments. The dataset and evaluation framework are available to encourage further 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.1310ACL Anthology · ACL 2026

OASIS: Mitigating Harmful Fine-tuning Attacks on LLMs via Orthogonal and Adaptive Safety Alignment Strategy

Jiayu Tang, Guowei Peng, Qiuhao Xie, Yuning Yang, Xiurui Xie University of Electronic Science and Technology of China, Guisong Liu

原文摘要

The “Fine-Tuning-as-a-Service” paradigm exposes large language models to catastrophic safety degradation from less harmful samples. Alignment-stage defenses address this by proactively injecting adversarial perturbations to bolster the model’s inherent robustness against harmful drift. However, existing methods rely on perturbation directions that often conflict with harmful gradients, inadvertently facilitating the acquisition of malicious features rather than suppressing them. To address this issue, we propose Orthogonal and Adaptive Safety Alignment Strategy (OASIS) to mathematically decouple safety enforcement from harmful feature acquisition. By projecting perturbations orthogonal to harmful gradients and concentrating optimization on adaptively selected safety-critical layers, OASIS effectively resolves directional conflicts while maximizing parameter efficiency. Extensive experiments on four LLMs across three datasets (SST2, GSM8K, and AGNews) demonstrate that OASIS reduces the Harmful Score by approximately 60% compared to competitive baselines, while maintaining stable downstream task utility.

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.1311ACL Anthology · ACL 2026

KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development?

Xue Jiang, Ge Li Peking University, Jiaru Qian, Xianjie Shi, Chenjie Li Peking University, Hao Zhu, Ziyu Wang Peking University, Jielun Zhang Peking University 等 14 位作者

原文摘要

Large language models (LLMs) excel at general programming but struggle with domain-specific software development. This gap motivates research into domain specialization methods that enable LLMs to learn and utilize domain knowledge and data. However, existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge, lacking explicit knowledge corpora for developing domain specialization methods. To this end, we present KOCO-bench, a novel benchmark designed for evaluating domain specialization methods in real-world software development. KOCO-bench contains 6 emerging domains with 11 software frameworks and 25 projects, featuring curated knowledge corpora alongside multi-granularity evaluation tasks including domain code generation (from function-level to project-level with rigorous test suites) and domain knowledge understanding (via multiple-choice Q A). Unlike previous benchmarks that only provide test sets for direct evaluation, KOCO-bench requires acquiring and applying diverse domain knowledge (APIs, rules, constraints, etc.) from the corpora to solve evaluation tasks. Our evaluations reveal that KOCO-bench poses significant challenges to state-of-the-art LLMs. Even with domain specialization methods (e.g., SFT, RAG, kNN-LM) applied, improvements remain marginal. Best-performing coding agent, Claude Code, achieves only 34.2%, highlighting the urgent need for more effective domain specialization methods. We release KOCO-bench, evaluation code, and baselines to advance further research at https://github.com/jiangxxxue/KOCO-bench.

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.1312ACL Anthology · ACL 2026

Enhancing Two Steps Textual Anomaly Detection through Anisotropy Mitigation

Pierre Fihey Télécom Paris, Matthieu Labeau Télécom ParisTech, Pavlo Mozharovskyi Télécom Paris

原文摘要

Anomaly detection aims at distinguishing between in-distribution samples, which belong to the same distribution as the training set, and out-of-distribution samples, which lie outside of it. In textual anomaly detection, recent approaches routinely apply anomaly detection algorithms directly to embeddings extracted from pre-trained embedding models (two-stage approaches). However, the geometric properties of pre-trained embeddings can hinder the effectiveness of detection algorithms, which often rely on distance-based measures. In this work, we first highlight the relevance of similarity-trained models for textual anomaly detection. Beyond being trained to capture semantic similarities, these models also exhibit geometric properties that appear better suited to detection algorithms. We further demonstrate that, besides model choice, a simple post-processing step can significantly improve anomaly detection by adapting embeddings to the assumptions made by classical detection algorithms. The bulk of our experiments is done on a reformulation of the classification tasks from the MTEB benchmark into anomaly detection tasks.

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.1313ACL Anthology · ACL 2026

InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training

Ziyun Zhang, Zezhou Wang, Xiaoyi Zhang Microsoft, Zongyu Guo Microsoft Research, Jiahao Li Microsoft Research Asia, Bin Li Microsoft, Yan Lu Microsoft Research Asia

原文摘要

GUI agents that interact with graphical interfaces on behalf of users are a promising direction for practical AI assistants, yet training them is hindered by scarce suitable environments. We present InfiniteWeb, a system that automatically generates functional web environments at scale for GUI agent training. While LLMs perform well on generating a single webpage, building a realistic and functional website with many interconnected pages faces challenges. We address these challenges through unified specification, task-centric test-driven development, and combining website seed variation with reference design images. Our system also generates verifiable task evaluators enabling dense reward signals for reinforcement learning. Experiments show that our system surpasses commercial coding agents at realistic website construction, and GUI agents trained on our generated environments achieve significant performance improvements on OSWorld and Online-Mind2Web, demonstrating the effectiveness of the proposed system.

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.1314ACL Anthology · ACL 2026

MagicBench: Diagnosing Visual Agency Loss and Semantic Dependency in Multimodal LLMs

Tang Da Huang, Weidong Tang, Wen Qi Xu, Xianpeng Guo

原文摘要

Multimodal Large Language Models typically assume linguistic context invariably enhances visual understanding. We study this assumption in semantic adversarial scenarios, specifically magic tricks, where narration deliberately diverges from physical reality. We introduce MagicBench, a diagnostic benchmark of 402 videos for evaluating MLLMs under hierarchical linguistic interference, together with a Physical Constraint Set (PCS) protocol for assessing adherence to physical laws. Evaluation uncovers a Semantic Dependency Paradox: (1) Semantic anchoring: Entity nouns act as anchors aiding localization, paradoxically boosting performance despite false predicates. (2) Visual Agency Loss: In semantic vacuums, multimodal performance collapses 12.4% (p < 0.01) below the vision-only “capability probe”. This gap persists under symmetric prompting, suggesting a form of functional perception suppression in which autonomous visual search may be under-utilized in multimodal settings without linguistic triggers. Causal interventions via spatial prompting and signal magnification provide evidence that internal reasoning remains functional, supporting the interpretation of a perceptual access bottleneck. Our findings suggest MLLMs function as “language-guided passive observers”, advocating for perceptually-independent architectures that decouple sensory agency from linguistic dominance. Code and dataset are available at https://github.com/Ink-Dawn/MagicBench

cs.CL · cs.CV

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.1315ACL Anthology · ACL 2026

Bloom-Eval: A Hierarchical Evaluation Benchmark for Automatic Survey Generation Based on Bloom’s Taxonomy

Fei Zhang, Zhe Zhao, HaiBin Wen, Tianshuo Wei, Zaixi Zhang Princeton University, Chao Yang Shanghai Jiaotong University, Ye Wei City University of Hong Kong

原文摘要

The rapid advance of automatic survey generation (ASG) has created a critical evaluation challenge. Existing evaluation methods suffer from both cognitive dimensional simplification and methodological unreliability, primarily due to the over-reliance on the ”LLM-as-a-Judge” approach. To bridge this gap, we establish Bloom-Eval, a six-tiered benchmark based on Bloom’s Taxonomy that reliably evaluates ASG systems by prioritizing deterministic algorithms and introducing our GRADE approach for abstract abilities. Furthermore, we construct a large-scale, cross-disciplinary dataset of over 3,000 high-quality papers. Our empirical study on this benchmark reveals that while leading ASG systems are proficient format organizers, they remain unqualified knowledge integrators. This work aims to redefine ASG evaluation standards, shifting the research focus from the formal mimicry of surface structure to the cognitive deepening of intellectual content. Our method provides the ASG field with a systematic, reproducible, and theoretically grounded benchmark to guide 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.1316ACL Anthology · ACL 2026

Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection

Yuchen Zhang Xi'an Jiaotong University, Yaxiong Wang Hefei University of Technology, Kecheng Han, Yujiao Wu CSIRO, Lianwei Wu, Li Zhu Xi'an Jiaotong University, Zhedong Zheng University of Macau

原文摘要

Recent advances in generative AI have significantly enhanced the realism of multimodal media manipulation, thereby posing substantial challenges to manipulation detection. Existing manipulation detection and grounding approaches predominantly focus on manipulation type classification under result-oriented supervision, which not only lacks interpretability but also tends to overfit superficial artifacts. In this paper, we argue that generalizable detection requires incorporating explicit forensic reasoning, rather than merely classifying a limited set of manipulation types, which fails to generalize to unseen manipulation patterns. To this end, we propose REFORM, a reasoning-driven framework that shifts learning from outcome fitting to process modeling. REFORM adopts a three-stage curriculum that first induces forensic rationales, then aligns reasoning with final judgments, and finally refines logical consistency via reinforcement learning. To support this paradigm, we introduce ROM, a large-scale dataset with rich reasoning annotations. Extensive experiments show that REFORM establishes new state-of-the-art performance with superior generalization, achieving 81.52% ACC on ROM, 76.65% ACC on DGM4, and 74.9 F1 on MMFakeBench.

cs.CL · cs.CV

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.1317ACL Anthology · ACL 2026

Fair-CCD: Mitigating Bias in Large Language Models for Tabular Classification Through Context-Contrastive Decoding

Donghan Liu, Han Sun, Zhaohui Wang, Qin Li East China Normal University, Min Zhang

原文摘要

While recent studies show the effectiveness of in-context learning (ICL) for tabular data prediction, they also reveal significant fairness issues in large language models (LLMs). Prior work to mitigate fairness issues often employs interventions relying on subjective demonstration selection. Its effectiveness varies significantly with the specific demonstration content, leading to low controllability. Moreover, the improvement of fairness is highly unstable across different models and tasks. To address the challenges of low controllability and limited stability in fairness interventions, we propose Fairness-Aware Context-Contrastive Decoding (Fair-CCD). Fair-CCD first constructs Structural Bias Templates (SBTs), motivated by behavioral patterns observed in demonstrations, to encode the relationship between sensitive attributes and predicted labels in a structured and controllable form. During inference, Fair-CCD injects multiple SBTs and contrasts the model’s responses, generating two differential signals that guide fairness adjustment and preserve task performance. By leveraging attention signals to scale decoding adjustments guided by the difference signals, Fair-CCD achieves stable and adaptive bias mitigation across models and tasks. Extensive experimental results demonstrate that Fair-CCD consistently improves fairness metrics without degrading task accuracy.

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.1318ACL Anthology · ACL 2026

DVI-DTM: Dual-View Representation Learning for Interpretable Short Text Dynamic Topic Modeling

Di Liu Beijing University of Posts and Telecommunications, Zheng Fang University of the Chinese Academy of Sciences, Bin Wu Beijing University of Posts and Telecommunications

原文摘要

Dynamic topic modeling aims to capture topic evolution from temporal text corpora. However, existing methods face two major challenges when applied to short texts: semantic ambiguity and interpretation ambiguity. Semantic ambiguity arises from the sparsity of short texts and the neglect of temporal semantic shifts. Interpretation ambiguity refers to the latent topics that lack human-understandable descriptions. In this work, we propose a novel Dual-View representation learning-based Interpretable short text Dynamic Topic Model (DVI-DTM). To address semantic ambiguity, the Dual-View Representation Learning module is presented to learn robust document-topic distributions by aligning temporal-aware term view and sentence view representations of short texts. To tackle interpretation ambiguity, we introduce a GEA Topic Refiner that leverages LLM agents to generate topic descriptions and refine document-topic distributions through collaborative semantic reasoning. Furthermore, a Dual-Factor Ranking module is designed to capture the topic evolution through semantic relevance and temporal uniqueness. Comprehensive experiments demonstrate that DVI-DTM outperforms the state-of-the-art baselines in topic alignment and dynamic topic quality metrics while producing highly interpretable topic descriptions.

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.1319ACL Anthology · ACL 2026

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling

Hao-Xiang Xu, Chong Deng, Jiaqing Liu Alibaba Group, Wen Wang Alibaba Group, Qian Chen Alibaba Group, Lujia Bao, Xiangang Li Alibaba Group, Zhen-Hua Ling University of Science and Technology of China

原文摘要

Large Language Models (LLMs) extend their capabilities through function-calling (FC), which relies on training data with high quality, diversity, and broad coverage of scenario. However, obtaining and annotating real function-calling data is challenging, while synthetic data from existing pipelines often suffers from unreliable APIs, limited tool scalability, insufficient diversity, and weak quality control. To address these, we present GenesisFunc, an automated pipeline for generating FC training data. Starting from reliable tools in widely used public benchmarks, our GenesisFunc employs a multi-agent framework to support a dialogue generation system that produces conversations spanning diverse scenarios, while maintaining both diversity and quality throughout the process. The accuracy of the data is further reinforced through a multi-stage evaluation system. We fine-tune an 8B LLM on the synthetic dataset and show through extensive experiments that it outperforms similarly sized open-source models in in-domain FC performance and out-of-domain generalization, while reaching FC capabilities comparable to some of the latest API-based models. In addition, our method demonstrates strong potential to scale effectively across downstream tools, underscoring its real-world applicability.

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.132ACL Anthology · ACL 2026

HOPE: Hybrid Optimized Parallel Encoding with Supervised and Unsupervised Semantic Fusion for Depression Symptom Detection

Tu-Phuong Mai Vietnam National University Hanoi, Minh-Ha H. Le, Duc-Luong Tran, Phuong-Anh Chu, Duy-Cat Can Université de Lausanne, Centre hospitalier universitaire vaudois (CHUV) and VNU University of Engineering and Technology, Hoang-Quynh Le

原文摘要

Timely detection of depression symptoms is essential for early intervention, and the continuous stream of user-generated content on social media provides an ideal source for this purpose. To address this challenge, we propose HOPE, a Hybrid Optimized Parallel Encoding framework that combines supervised symptom relevance signals with unsupervised intrinsic semantic clustering. This parallel design enables robust symptom detection under limited labeled data and introduces a distinctive semantic-similarity perspective with automatic class-anchor adjustment. We also propose an optimized hybrid semantic fusion mechanism to combine supervised and unsupervised scores through a learnable module. We evaluate our system on multiple benchmark datasets and surpass previous approaches, demonstrating its effectiveness in detecting fine-grained symptoms and early warning of mental health risk. Source code is available at https://github.com/candleMind/hope.

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.1320ACL Anthology · ACL 2026

Selective Test-Time Debiasing for CLIP via Reward Gating

Jaeho Han Chung-Ang University, Jisoo Yang, Hyeondong Woo Chung-Ang University, Mingyu Jeon, Sunjae Yoon Chung-Ang University, Junyeong Kim

原文摘要

Vision language models (VLMs) demonstrate strong zero-shot performance, but often perpetuate social stereotypes in person-centric queries, yielding skewed demographic distributions. Current debiasing methods apply uniform bias corrections across all input queries regardless of their bias sensitivity, creating a fundamental fairness–utility trade-off. Strong debiasing distorts semantically meaningful information in bias-insensitive queries, while weak debiasing fails to mitigate stereotypes in bias-sensitive ones. This one-size-fits-all approach hampers simultaneously achieving high utility on bias-insensitive queries and fairness on bias-sensitive queries. We introduce Reward-Gated Test-Time Adaptation (RG-TTA), a reinforcement learning-based test-time adaptation framework that selectively applies debiasing based on input sensitivity. RG-TTA adaptively triggers fairness regularization based on the bias sensitivity of each input during test-time policy adaptation, while focusing exclusively on optimizing cross-modal alignment for bias-insensitive inputs. Experiments on fairness benchmarks (e.g., FairFace, UTKFace) demonstrate substantial bias reduction while simultaneously improving zero-shot utility, resolving the trade-off of uniform debiasing.

cs.CL · cs.CV

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.1321ACL Anthology · ACL 2026

CURE: Critique-Driven Unified Reinforcement Learning for Test-Time Self-Improvement

Guirong Chen Renmin University of China, Shuqi Ye, Wenkai Yang Renmin University of China, Shiqi Shen Wechat, Tencent, Guangyao Shen Tencent, Yankai Lin Renmin University of China

原文摘要

The evolution paradigm of Large Language Models (LLMs) is shifting from scaling training compute to scaling inference-time compute. While Reinforcement Learning with Verifiable Rewards (RLVR) has become a key engine for this transition, standard approaches often fail to equip models with the autonomous improvement capabilities required for test-time scaling. Existing critique-guided methods attempt to mitigate this by leveraging external feedback or ground-truth signals; however, these dependencies are unavailable at test time, fundamentally limiting the model’s capacity for continuous self-improvement. To bridge this gap, we propose CURE (Critique-driven Unified REinforcement Learning), a framework that jointly optimizes a single policy for standard solving, critiquing, and guided re-exploration. Uniquely, CURE facilitates re-exploration by generating strategic hints while discarding initial incorrect solutions to mitigate anchoring bias.Empirical results across diverse mathematical reasoning and code generation benchmarks demonstrate that CURE not only maintains competitive single-turn performance but, more importantly, unlocks effective inference-time scaling, enabling the model to significantly boost accuracy through iterative self-improvement.

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.1322ACL Anthology · ACL 2026

LGSA: Label Geometry Structuring and Aligning for Hierarchical Text Classification

Shuai Zhang Zhejiang University of Finance and Economics, Weibo Xu, Jiahao Nie Zhejiang University of Finance and Economics, Kecheng Huang

原文摘要

Existing hierarchical text classification (HTC) methods typically use prompt tuning or contrastive learning to inject the label hierarchy into a model as prior knowledge to implicitly learn label embeddings for classification. However, such implicit learning fails to accurately reflect label geometry (i.e., feature spatial distribution of label embeddings), as it does not model hierarchy-aware geometric relations among labels. To address this issue, we propose a novel two-stage label geometry structuring and aligning framework, termed LGSA, which transforms the label hierarchy from an implicit prior into an explicit embedding. First, we propose a hierarchical geometric structuring (HGS) module that leverages a general orthogonal frame (GOF) to reconstruct an explicit label geometry conforming to the label hierarchy. The label geometry is then treated as a label prototype to guide model training. To facilitate the guidance, we thereby propose a hierarchical geometric aligning (HGA) module as a regularization term to align label geometry learned by the model with the explicit label prototype. Experiments on three real-world HTC datasets confirm that LGSA consistently outperforms existing state-of-the-art methods. The code and models are available at https://anonymous.4open.science/r/LGSA-1E0C.

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.1323ACL Anthology · ACL 2026

FactVerse: A Benchmark for Factual Consistency in Interleaved Image–Text Generation

Yubo Shan, Kun Zhang Tencent, Qiming Xu Beijing Technology and Business University, Liping Cao, Yingying Cao, Jian Zhang, Yu Wang, Jingyuan Li 等 9 位作者

原文摘要

Interleaved multimodal understanding and generation—where models can interactively comprehend and produce images and text in arbitrary orders—has emerged as a key research direction in generative Multimodal Large Language Models(MLLMs). Such interleaved image–text content plays an increasingly important role in information dissemination. However, the compounded persuasive power of multimodal narratives also raises the risk of factual misinformation. Despite this, existing benchmarks lack effective mechanisms to evaluate factual consistency in interleaved image–text content. To bridge this gap, we introduce FactVerse, a benchmark dedicated to evaluating factual consistency in interleaved image-text generation. FactVerse comprises 3,000 human-verified instances across four categories and 50 domains, supporting both English and Chinese. We also establish a multi-dimensional evaluation framework designed to rigorously assess factual consistency. Experiments demonstrate that our framework achieves high alignment with human judgments, significantly outperforming existing evaluation methods. Furthermore, our analysis reveals systematic deficiencies in current models, offering critical insights for future design.

cs.CL · cs.CV

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.1324ACL Anthology · ACL 2026

GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment

Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye Northeastern University, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai Tsinghua University, Tsinghua University 等 11 位作者

原文摘要

Large Language Models (LLMs) have achieved remarkable performance across a wide range of Natural Language Processing (NLP) tasks. However, in long-context scenarios, they face two challenges: high computational cost and information redundancy. To address these challenges, we propose GMSA, an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks. GMSA introduces Group Merging to achieve more uniform aggregation, mitigating semantic dominance during autoencoder pretraining, and Layer Semantic Alignment (LSA) to bridge the semantic gap between high-level abstract semantics and low-level input semantics. We first pretrain GMSA as an autoencoder and then fine-tune it for downstream tasks. Experiments demonstrate that GMSA improves context reconstruction compared to existing soft prompt compression paradigm and outperforms baselines on multiple long-context question answering and summarization benchmarks across two backbone models, while maintaining low end-to-end latency.

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.1325ACL Anthology · ACL 2026

SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation

Mahi Luthra Facebook, Jiayi Shen Facebook, Maxime Poli Ecole Normale Supérieure – PSL, Angelo Ortiz Tandazo Ecole Normale Supérieure – PSL, Yosuke Higuchi Meta, Youssef Benchekroun Facebook, Martin Gleize Facebook, Charles-Eric Saint-James 等 17 位作者

原文摘要

Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compared to the data-hungry self-supervised speech models. To address this gap, this paper introduces SpidR-Adapt for rapid adaptation of speech units to new languages using minimal unlabeled data. We cast such low-resource speech representation learning as a meta-learning problem and construct a multi-task adaptive pre-training (MAdaPT) protocol which formulates the adaptation process as a bi-level optimization framework. To enable scalable meta-training under this framework, we propose a novel heuristic solution, first-order bi-level optimization (FOBLO), avoiding heavy computation costs. Finally, we stabilize meta-training by using a robust initialization through interleaved supervision which alternates self-supervised and supervised objectives. Empirically, SpidR-Adapt achieves rapid gains in phonemic discriminability (ABX) and downstream spoken language modeling scores (sWUGGY, sBLIMP, tSC), surpassing in-domain toplines after training on less than 1h of target-language audio and delivering 100\times greater data efficiency than standard multi-task training.. These findings highlight a practical, architecture-agnostic path toward biologically inspired, data-efficient representations. We open-source the training code and model checkpoints at https://github.com/facebookresearch/spidr-adapt.

cs.CL

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