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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1326ACL Anthology · ACL 2026

EIFFEL: a novel benchmark to measure bias of English heavy training on French idiomatic expressions

Charlotte Noel Institut de Recherche en Informatique de =Toulouse, Nicholas Asher CNRS, Olivier Gouvert Linagora, Farah Benamara, Julie Hunter LINAGORA

原文摘要

Mainstream multilingual LLMs are generally trained on a much higher proportion of English than multilingual data, raising questions about their ability to capture linguistic features particular to non-English languages or to capture information important to non-anglophone cultures. We add to a growing effort to increase multilingual sensitivity in LLMs by developing a benchmark, EIFFEL, testing mastery of French idiomatic expressions in context. We fully explain the methodology, which exploits input from native French speakers, to make it reproducible for other languages. We compare mainstream multilingual LLMs with French-focused LLMs both on standard LLM benchmarks and EIFFEL; EIFFEL brings out the benefits of higher proportions of French data and shows limitations of standard benchmarks for measuring multilingual competence.

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.1327ACL Anthology · ACL 2026

Latent Attention Denoising: A Training-Free Energy-Based Framework for Mitigating Hallucinations in Vision-Language Models

Zhiwen Luo, Siyu Jiang, Weilong Jiang Huazhong University of Science and Technology, Kun He Huazhong University of Sceince and Technology

原文摘要

Visual hallucination remains a major obstacle to the reliability of Large Vision-Language Models (LVLMs). We argue that this issue originates from a fundamental statistical misspecification: the conventional softmax attention implicitly assumes i.i.d. noise, yet real LVLM attention patterns exhibit structured and competitive biases (e.g., attention sinks) that violate this assumption. To address this mismatch, we introduce Latent Attention Denoising (LAD), a principled and training-free framework that recasts attention calibration as a one-step score-based denoising process. LAD employs an interpretable energy function to derive an analytic score and applies a single Langevin-inspired update to actively steer corrupted attention logits toward more faithful configurations. This intervention imposes negligible computational overhead and operates at a speed comparable to standard greedy decoding. Extensive evaluations across diverse architectures confirm that LAD achieves superior performance on both generative and discriminative tasks, effectively mitigating hallucinations while maintaining efficiency comparable to standard decoding.

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.1328ACL Anthology · ACL 2026

From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent

Yucheng Wang Tsinghua University, Shen Yang Tsinghua University, Jifan Yu, Haoxuan Li Tsinghua University, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu Tsinghua University, Tsinghua University, Lei Hou Tsinghua University 等 10 位作者

原文摘要

Knowing and teaching differ fundamentally: effective instruction requires transforming knowledge into forms learners can grasp. Large language models, when asked to generate lessons (a concrete form of teaching), produce content lacking pedagogical depth. We trace this failure to three decisions that expert teachers make: selecting content by recognizing each source’s instructional role, sequencing topics so foundations precede applications, and synthesizing components into a unified whole. To scaffold these decisions, we introduce TeachCraft, a framework with three agents: Explorer classifies sources by pedagogical intent to guide selection; Planner orders objectives from foundational to advanced; Generator produces lesson materials through a schema that ensures consistency across components. To evaluate this approach, we construct LessonBench, 40 expert-designed lessons paired with two to five heterogeneous source documents, on which TeachCraft achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines, with ablations confirming that each decision contributes independently to overall lesson quality.[Source code is available at https://anonymous.4open.science/r/TeachCraft-1672]

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.1329ACL Anthology · ACL 2026

Learn Like Humans: Use Meta-cognitive Reflection for Efficient Self-Improvement

Xinmeng Hou, Bohao Qu A*STAR, Wuqi Wang Chang 'an University, Peiliang Gong Singapore University of Technology and Design and Nanyang Technological University, Qing Guo Nankai University and National University of Singapore, Yang Liu Nanyang Technological University

原文摘要

While Large Language Models (LLMs) enable complex autonomous behavior, current agents remain constrained by static, human-designed prompts that limit adaptability. Existing self-improving frameworks attempt to bridge this gap but typically rely on inefficient, multi-turn recursive loops that incur high computational costs. To address this, we propose Metacognitive Agent with Reflective Self-improvement (MARS), a framework that achieves efficient self-evolution within a single recurrence cycle. Inspired by educational psychology, MARS mimics human learning by integrating principle-based reflection (abstracting normative rules to avoid errors) and procedural reflection (deriving step-by-step strategies for success). By synthesizing these insights into optimized instructions, MARS allows agents to systematically refine their reasoning logic without continuous online feedback. Extensive experiments on six benchmarks demonstrate that MARS outperforms state-of-the-art self-evolving systems while significantly reducing computational overhead. Code is available at https://github.com/Paparare/MARS/tree/main

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.133ACL Anthology · ACL 2026

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF

Kangwen Zhao University of Science and Technology of China, Jianfeng Cai University of Science and Technology of China, Jinhua Zhu University of Science and Technology of China, Ruopei Sun, Dongyun Xue, Wengang Zhou University of Science and Technology of China, Li Li University of Science and Technology of China, Houqiang Li

原文摘要

Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences. However, RLHF often suffers from reward hacking, wherein policy learning exploits flaws in the trained reward model to maximize reward scores without genuinely aligning with human preferences. A significant example of such reward hacking is length bias, where reward models usually favor longer responses irrespective of actual response quality. Previous works on tackling length bias have notable limitations, these approaches either mitigate bias without characterizing the bias form, or simply assume a linear length-reward relation. To accurately model the intricate nature of length bias and facilitate more effective bias mitigation, we propose FiMi-RM (Bias Fitting to Mitigate Length Bias of Reward Model), a framework that autonomously learns and corrects underlying bias patterns. Our approach consists of three stages: First, we warm up by training a standard reward model which inherently contains length bias. Next, we deploy a lightweight fitting model to capture the non-linear relation between length and reward. Finally, we incorporate this learned relation into the reward model, effectively decoupling length from reward while preserving preference modeling capabilities. Experimental results demonstrate that FiMi-RM achieves a more balanced length-reward distribution. Furthermore, when applied to alignment algorithms such as Direct Preference Optimization (DPO) and Best-of-N (BoN), our debiased reward model improves length-controlled win rate and reduces verbosity without compromising its performance.

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.1330ACL Anthology · ACL 2026

Think Faster Than Words: Efficient LLM Chain-of-Thought Reasoning via Dynamic Shortcut Decoding

Fan Liu, Yanhao Wang East China Normal University, Min Zhang East China Normal University, Zhikang Chen University of Oxford, Zeyuan Li, Lewei He, Jiahui Pan South China Normal University

原文摘要

This paper proposes shortcut decoding, an efficient framework for accelerating Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs). Existing methods that prune or employ early stopping to reduce latency often compromise reasoning reliability. Motivated by the observation that LLMs frequently converge to correct solutions internally before completing explicit textual reasoning, we propose a dual-signal adaptive controller that integrates lightweight probes over internal hidden states with step-level entropy. This controller detects convergence of reasoning during generation and adaptively selects between a fast-exit path and a stability-verified path to remove redundant steps while preserving answer correctness. Experiments across multiple mathematical reasoning benchmarks demonstrate that shortcut decoding reduces token usage by approximately 35%, maintains accuracy comparable to full CoT decoding, and achieves final-answer accuracy comparable to the full CoT baseline, outperforming existing early-stopping methods without updating the base model. Our code is available at https://github.com/kuromi9527/shortcut_decoding.

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.1331ACL Anthology · ACL 2026

AEA: Adaptive Expert Allocation Improves Sentence Embeddings from Mixture-of-Experts LLM

Shufan Yang, Zifeng Cheng Nanjing University, Zhiwei Jiang Nanjing University, Qingfeng Qi, Yafeng Yin Nanjing University, Cong Wang Nanjing University, Ao Zhou, Qing Gu Nanjing University

原文摘要

Extracting embeddings directly from Mixture-of-Experts (MoE) models is a promising yet underexplored direction that requires no additional data or fine-tuning. While previous studies have utilized semantic compression prompts or expert routing information to improve sentence embeddings, they typically allocate a fixed number of experts uniformly across all layers and tokens, ignoring inter-layer and inter-token heterogeneity. In this work, we identify two key observations in MoE models: (1) layer-wise variations in expert homogeneity, suggesting that different layers require different expert budgets, and (2) token-wise contribution imbalance, indicating that different tokens should also be allocated different numbers of experts. To address these issues, we propose an Adaptive Expert Allocation (AEA) framework that dynamically performs both layer-wise and token-wise expert allocation to enhance embedding quality. Specifically, AEA allocates fewer experts to layers with higher homogeneity and to tokens with lower attention importance, where layer-wise homogeneity is determined by the similarity among embeddings produced by the experts in each layer. Notably, our method is plug-and-play, seamlessly integrates with existing prompt engineering methods, and introduces no additional time overhead. Experiments on the STS tasks demonstrate that AEA consistently improves embedding quality across multiple MoE 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.1332ACL Anthology · ACL 2026

It’s High Time: A Survey of Temporal Question Answering

Bhawna Piryani Universität Innsbruck, Abdelrahman Abdallah, Jamshid Mozafari Universität Innsbruck, Avishek Anand Delft University of Technology, Adam Jatowt

原文摘要

Time plays a critical role in how information is generated, retrieved, and interpreted. In this survey, we provide a comprehensive overview of Temporal Question Answering (TQA), a research area that focuses on answering questions involving temporal constraints or context. As time-stamped content from sources like news articles, web archives, and knowledge bases continues to grow, TQA systems must address challenges such as detecting temporal intent, normalizing time expressions, ordering events, and reasoning over evolving or ambiguous facts. We organize existing work through a unified perspective that captures the interaction between corpus temporality, question temporality, and model capabilities, enabling a systematic comparison of datasets, tasks, and approaches. We review recent advances in TQA enabled by neural architectures, especially transformer-based models and Large Language Models (LLMs), highlighting progress in temporal language modeling, retrieval-augmented generation (RAG), and temporal reasoning. We also discuss benchmark datasets and evaluation strategies designed to test temporal robustness, recency awareness, and generalization.

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.1333ACL Anthology · ACL 2026

SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding

Shuang Cheng, Yuhua Jiang, Zineng Zhou, Dawei Liu Shanghai Jiao Tong University and Shanghai Artificial Intelligence Laboratory, Tao Wang Bytedance group, Linfeng Zhang Shanghai Jiaotong University, Biqing Qi University of Hong Kong and Shanghai Artificial Intelligence Laboratory, Bowen Zhou Tsinghua University

原文摘要

Block-wise discrete diffusion offers an attractive balance between parallel generation and causal dependency modeling, making it a promising backbone for vision-language modeling. However, its practical adoption has been limited by high training cost, slow convergence, and instability, which have so far kept it behind strong autoregressive (AR) baselines. We present SDAR-VL, the first systematic application of block-wise discrete diffusion to large-scale vision-language understanding (VLU), together with an integrated framework for efficient and stable training. This framework unifies three components: 1) Asynchronous Block-wise Noise Scheduling to diversify supervision within each batch; 2) Effective Mask Ratio Scaling for unbiased loss normalization under stochastic masking; and 3) a Progressive Beta Noise Curriculum that increases effective mask coverage while preserving corruption diversity. Experiments on 21 single-image, multi-image, and video benchmarks show that SDAR-VL consistently improves training efficiency, convergence stability, and task performance over conventional block diffusion. On this evaluation suite, SDAR-VL sets a new state of the art among diffusion-based vision-language models and, under matched settings, matches or surpasses strong AR baselines such as LLaVA-OneVision as well as the global diffusion baseline LLaDA-V, establishing block-wise diffusion as a practical backbone for VLU.

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.1334ACL Anthology · ACL 2026

N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator

Zheyu Lin, Jirui Yang, Yukui Qiu, Yubing Bao, Hengqi Guo, Yao Guan

原文摘要

Evaluating the safety robustness of LLMs is critical for their deployment. However, mainstream Red Teaming methods rely on online generation and black-box output analysis. These approaches are not only costly but also suffer from feedback latency, making them unsuitable for agile diagnostics after training a new model.To address this, we propose N-GLARE (A Non-Generative, Latent Representation-Efficient LLM Safety Evaluator). N-GLARE operates entirely on the model’s latent representations, bypassing the need for full text generation. It characterizes hidden layer dynamics by analyzing the APT (Angular-Probabilistic Trajectory) of latent representations and introducing the JSS (Jensen-Shannon Separability) metric.Experiments on over 40 models and 20 red teaming strategies demonstrate that the JSS metric exhibits high consistency with Red Teaming safety rankings at less than 1% token and runtime cost.

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.1335ACL Anthology · ACL 2026

Don’t Act Blindly: Robust GUI Automation via Action-Effect Verification and Self-Correction

Yuzhe Zhang Beijing University of Technology, Xianwei Xue, Xingyong Wu, Mengke Chen Baidu, Chen Liu Baidu, Xinran He, Run Shao, Feiran Liu College of Computer Science 等 11 位作者

原文摘要

Autonomous GUI agents based on vision-language models (VLMs) often assume deterministic environment responses, generating actions without verifying whether previous operations succeeded. In real-world settings with network latency, rendering delays, and system interruptions, this assumption leads to undetected action failures, repetitive ineffective behaviors, and catastrophic error accumulation. Moreover, learning robust recovery strategies is challenging due to the high cost of online interaction and the lack of real-time feedback in offline datasets.We propose VeriGUI (Verification-driven GUI Agent), which explicitly models action outcomes and recovery under noisy environments. VeriGUI introduces a Thinking–Verification–Action–Expectation (TVAE) framework to detect failures and guide corrective reasoning, and a two-stage training pipeline that combines Robust SFT with synthetic failure trajectories and GRPO with asymmetric verification rewards. We further construct a Robustness Benchmark based on AndroidControl to evaluate failure recognition and correction. Experiments show that VeriGUI significantly reduces failure loops and improves recovery success while maintaining competitive standard task performance.

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.1336ACL Anthology · ACL 2026

Beyond Static Persona Consistency: Dynamic Persona Coherence in LLM Role-Playing

Yirui QI, Xiaoming Zhang Beijing University of Aeronautics and Astronautics, Ruilin Zeng, Mengyao Liu, Ziyi Zhou, Dezhuang Miao, Bingyu Yan Beijing University of Aeronautics and Astronautics, Zhenyu Guan Beijing University of Aeronautics and Astronautics

原文摘要

Current LLM role-playing systems model persona as a monolithic, static attribute, conflating identity consistency with emotional rigidity. This leads to either robotic repetition or catastrophic persona drift under sustained interaction. We introduce Dynamic Persona Coherence, a framework that decouples Identity-Layer Stability (time-invariant traits) from Adaptive-Layer Appropriateness (history-dependent psychological evolution). We operationalize this through the L/M/S Psychological State Model, which represents persona dynamics across long-term identity, mid-term meaning/stress accumulation, and short-term affect. On top of this state representation, a closed-loop alignment system comprising an automated evaluator (Persona Consistency Critic, PCC), a selective repository (Persona Case Repository, PCR), and a trajectory-adjusting corrector (Persona Drift Suppressor, PDS) enables autonomous coherence repair. Experiments on GPT-4o, Claude-3.5-Sonnet, and DeepSeek-V3.2 demonstrate consistent improvements (+16–84% PCC gains).

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.1337ACL Anthology · ACL 2026

What If Consensus Lies? Selective-Complementary Reinforcement Learning at Test Time

Dong Yan Institute of Automation, Chinese Academy of Sciences, Jian Liang Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Yanbo Wang Institute of automation, Chinese academy of sciences, Shuo Lu Chinese Academy of Sciences, Ran He Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Tieniu Tan Institute of Automation, Chinese Academy of Sciences

原文摘要

Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving pseudo-rewards from majority voting consensus.However, existing TTRL methods rely exclusively on positive pseudo-labeling strategies.Such reliance becomes vulnerable under challenging scenarios where answer distributions are highly dispersed, resulting in weak consensus that inadvertently reinforces incorrect trajectories as supervision signals.In this paper, we propose SCRL (Selective-Complementary Reinforcement Learning), a robust test-time reinforcement learning framework that effectively mitigates label noise amplification.SCRL develops Selective Positive Pseudo-Labeling, which enforces strict consensus criteria to filter unreliable majorities. Complementarily, SCRL introduces Entropy-Gated Negative Pseudo-Labeling, the first negative supervision mechanism in TTRL, to reliably prune incorrect trajectories based on generation uncertainty. Extensive experiments on mathematical reasoning benchmarks demonstrate that SCRL achieves substantial improvements over baselines, while maintaining robust generalization and training stability under constrained rollout budgets.

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.1338ACL Anthology · ACL 2026

Exploring and Distilling Multi-Dimensional Clues for Interpretable Social Bot Detection

Yi Han, Haiqi Lu, Lizi Liao Singapore Management University, Shuhan Zhou, Yuanxing Liu Harbin Institute of Technology, Weinan Zhang Harbin Institute of Technology, Ting Liu Harbin Institute of Technology

原文摘要

Social bot accounts have long been disseminating disinformation and engaging in malicious activities on social media platforms. Detecting these social bots has become a critical and urgent task, essential for maintaining a healthy online ecosystem. Existing social bot detection research usually provides detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy. This is a key concern for online moderation. In this work, we explore the detection interpretation and summarize a four-dimensional clue framework from individual and social perspectives. We propose CDRBot, which primarily employs outcome-reward reinforcement learning to train inspectors to generate faithful, grounded, and readable clues from the User Information, Semantic Features, Interactive Situation, and Behavioral Pattern. These clues are then integrated to make final predictions. Experimental results demonstrate that our approach outperforms other baselines in detection performance. The generated clues are faithful, grounded, and readable, and can significantly enhance the performance of large language models in social bot detection.

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.1339ACL Anthology · ACL 2026

Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution

Zhou Ziheng, Huacong Tang University of California, Los Angeles, Mingjie Bi Beijing Institute for General Artificial Intelligence, Wanying He Wuhan University, Fang Sun, Yizhou Sun University of California, Los Angeles and Amazon, Ying Nian Wu, Demetri Terzopoulos University of California, Los Angeles 等 10 位作者

原文摘要

The evolution of morality presents a puzzle: natural selection should favor self-interest, yet humans developed moral systems promoting altruism. Traditional approaches must abstract away cognitive processes, leaving open how cognitive factors shape moral evolution. We introduce an LLM-based agent simulation framework that brings cognitive realism to this question: agents with varying moral dispositions perceive, remember, reason, and decide in a simulated prehistoric hunter-gatherer society. This enables us to manipulate factors that traditional models cannot represent—such as moral type observability and communication bandwidth—and to discover emergent cognitive mechanisms from agent interactions. Across 20 runs spanning four settings, we find that cooperation and mutual help are the central driver of evolutionary survival, with universal and reciprocal morality exhibiting the most stable outcomes across conditions while selfishness is strongly disfavoured. Beyond cooperation itself, we further identify cognition as a central mediator—most clearly through a cost of moral judgment that shifts the winning moral type across settings, with a self-purging effect among selfish agents as an additional cognitive pattern. We validate robustness across multiple LLM backbones, architecture ablations, and prompt sensitivity analyses. This work establishes LLM-based simulation as a powerful new paradigm to complement traditional research in evolutionary biology and anthropology, opening new avenues for investigating the complexities of moral and social evolution.

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.134ACL Anthology · ACL 2026

Rethinking Sample Polarity in Reinforcement Learning with Verifiable Rewards

Xinyu Tang Renmin University of China, Yuliang Zhan, Zhixun Li, Xin Zhao Renmin University of China, Zhenduo Zhang, Zujie Wen Ant Group, Zhiqiang Zhang Ant Group, Jun Zhou Ant Group

原文摘要

Large reasoning models (LRMs) are typically trained using reinforcement learning with verifiable reward (RLVR) to enhance their reasoning abilities. In this paradigm, policies are updated using both positive and negative self-generated rollouts, which correspond to distinct sample polarities. In this paper, we provide a systematic investigation into how these sample polarities affect RLVR training dynamics and behaviors. We find that positive samples sharpen existing correct reasoning patterns, while negative samples encourage exploration of new reasoning paths. We further explore how adjusting the advantage values of positive and negative samples at both the polarity level and the token level affects RLVR training. Based on these insights, we propose an Adaptive and Asymmetric token-level Advantage shaping method for Policy Optimization, namely A3PO, that more precisely allocates advantage signals to key tokens across different polarities. Experiments across five reasoning benchmarks demonstrate the effectiveness of our approach.

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.1340ACL Anthology · ACL 2026

Systematicity between Forms and Meanings across Languages Supports Efficient Communication

Doreen Osmelak Universität des Saarlandes, Yang Xu Department of Computer Science, University of Toronto, Michael Hahn, Kate McCurdy Universität des Saarlandes and University of Edinburgh, University of Edinburgh

原文摘要

Languages vary widely in how meanings map to word forms. These mappings have been found to support efficient communication; however, this theory does not account for systematic relations within word forms. We examine how a restricted set of grammatical meanings (e.g. person, number) are expressed on verbs and pronouns across typologically diverse languages. Consistent with prior work, we find that verb and pronoun forms are shaped by competing communicative pressures for simplicity (minimizing the inventory of grammatical distinctions) and accuracy (enabling recovery of intended meanings). Crucially, our proposed model uses a novel measure of complexity (inverse of simplicity) based on the learnability of meaning-to-form mappings. This innovation captures fine-grained regularities in linguistic form, allowing better discrimination between attested and unattested systems, and establishes a new connection from efficient communication theory to systematicity in natural language.

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.1341ACL Anthology · ACL 2026

On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference

Zhengyi Li, Yakai Wang, Jingwen Leng Shanghai Jiao Tong University, Kang Yang, Yu Yu, Jiaping Gui Shanghai Jiao Tong University, Yu Feng Shanghai Jiaotong University, Ning Liu 等 9 位作者

原文摘要

For Transformer models, cryptographically secure inference ensures that the client learns only the final output, while the server learns nothing about the client’s input. However, securely computing nonlinear layers remains a major efficiency bottleneck due to the substantial communication rounds and data transmission required. To address this issue, prior works reveal intermediate activations to the client, allowing nonlinear operations to be computed in plaintext. Although this approach significantly improves efficiency, exposing activations enables adversaries to extract model weights. To mitigate this risk, existing works employ a shuffling defense that reveals only randomly permuted activations to the client. In this work, we show that the shuffling defense is not as robust as previously claimed. We propose an attack that aligns differently shuffled activations to a common permutation and subsequently exploits them to extract model weights. Experiments on Pythia-70m and GPT-2 demonstrate that the proposed attack can align shuffled activations with mean squared errors ranging from 10^{-9} to 10^{-6}. With a query cost of approximately $1, the adversary can recover model weights with L1-norm differences ranging from 10^{-4} to 10^{-2} compared to the oracle weights.

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.1342ACL Anthology · ACL 2026

LaCo: Layer-wise Compensation for Pruned Large Language Models

Yingen Liu, Fan Wu, Panxuyan, Ruihui Li Hunan University, Zhuo Tang Hunan University, Kenli Li Hunan University

原文摘要

Pruning is essential for the efficient deployment of Large Language Models (LLMs); however, it causes severe performance degradation due to the structural distortion induced by sparsity.Existing recovery strategies, such as LoRA, predominantly employ global fine-tuning, often overlooking the mechanistic root of this degradation: the layer-wise accumulation and amplification of local errors. To address this limitation, we propose LaCo(Layer-wise Compensation), a framework that reorients the recovery paradigm from global adaptation to hierarchical representation alignment.By sequentially optimizing each layer to reconstruct the model’s hidden states, LaCo effectively intercept the error propagation chain at its source.Extensive experiments demonstrate that LaCo surpasses parameter-efficient baselines in both perplexity reduction and zero-shot reasoning.Notably, it reduces recovery-time memory usage to approximately 1/7 of the baseline and requires only 2,048 unlabeled samples to match a LoRA model trained on 50k examples—achieving a \sim25\times improvement in data efficiency.

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.1343ACL Anthology · ACL 2026

QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization

Changxin Ke Institute of Computing Technology, Chinese Academy of Sciences, Rui Zhang Institute of Computing Technology, CAS, Jiaming Guo Institute of Computing Technology, Chinese Academy of Sciences, Yuanbo Wen Institute of Computing Technology, Chinese Academy of Sciences, Li Ding, Shuo Wang, Xuyuan Zhu, Xiong Peng 等 13 位作者

原文摘要

Large Language Models (LLMs) achieve strong program repair performance but often suffer from over-editing, where excessive modifications overwrite correct code and hinder bug localization. We systematically quantify its impact and introduce precise repair task, which maximizes reuse of correct code while fixing only buggy parts. Building on this insight, we propose PRepair, a framework that mitigates over-editing and improves repair accuracy. PRepair has two components: Self-Breaking, which generates diverse buggy programs via controlled bug injection and min–max sampling, and Self-Repairing, which trains models with Edit-Aware Group Relative Policy Optimization (EA-GRPO) using an edit-aware reward to encourage minimal yet correct edits. Experiments show that PRepair improves repair precision by up to 31.4% under \mathrm{fix}_1@1, a metric that jointly considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing, demonstrating its potential for precise and practical code repair.

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.1344ACL Anthology · ACL 2026

Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs

Guy Mor-Lan, Omer Goldman University of Cambridge, Matan Eyal Google, Adi Mayrav Gilady Research, Google, Sivan Eiger, Idan Szpektor Google, Avinatan Hassidim Bar-Ilan University and Google, Yossi Matias Google and Tel Aviv University 等 9 位作者

原文摘要

Multilingual large language models (LLMs) have minimized the fluency gap between languages. This advancement, however, exposes models to the risk of biased behavior, as knowledge and norms may propagate across languages. In this work we aim to quantify models’ inter- and intra-lingual biases, via their ability to answer locale-ambiguous questions. To this end, we present LocQA, a test set containing 2,156 questions in 12 languages, referring to various locale-dependent facts such as laws, dates, and measurements. The questions do not contain indications of the locales they relate to, other than the querying language itself. LLMs’ responses to LocQA locale-ambiguous questions thus reveal models’ implicit priors. We used LocQA to evaluate 32 models, and detected two types of structural biases. Inter-lingually, we show a global bias towards answers relevant to the US-locale, even when models are asked in languages other than English. Moreover, we discovered that this global bias is exacerbated in models that underwent instruction tuning, compared to their base counterparts. Intra-lingually, we show that when multiple locales are relevant for the same language, models act as demographic probability engines, prioritizing locales with larger populations. Taken together, insights from LocQA may help in shaping LLMs’ desired local behavior, and in quantifying the impact of various training phases on different kinds of biases.

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.1345ACL Anthology · ACL 2026

Incorporating Temporal Coherence to Cross-Document Event Coreference Resolution

Xinyu Chen Soochow University, Peifeng Li, Qiaoming Zhu Soochow University

原文摘要

Previous work on cross-document event coreference resolution (CDECR) primarily focused on enhancing semantic coherence between event mentions, largely overlooking the critical aspect of temporal coherence. To address this issue, we propose CohTP, a novel Temporal Cohorence-driven event coreference framework. CohTP explicitly models and enforces temporal constraints by first constructing a temporal event graph via a fine-tuned natural language inference (NLI) model. The graph is then refined using an Edge-Aware GNN to resolve conflicts and partitioned into ordered time segments, where undirected edges group contemporaneous events. Event coreference resolution is subsequently performed within these temporally coherent segments, where event representations are further augmented with temporally consistent contexts. Experiments on the ECB+, GVC, WEC, and ECB+META datasets show that CohTP outperforms several state-of-the-art 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.1346ACL Anthology · ACL 2026

LLMs in Sarcasm Detection? It’s elementary! (Or is it?)

Priyanshu Mahato, Aniket Santosh Mishra, Kripabandhu Ghosh Indian Institute of Science Education and Research Kolkata

原文摘要

While Large Language Models (LLMs) are frequently cited for their sophisticated pragmatic reasoning (CITATION), recent progress in sarcasm detection increasingly relies on synthetic benchmarks (CITATION). This study exposes a catastrophic generalization gap in this paradigm: we observe that models achieve near-perfect accuracy on synthetic data but collapse to random guessing on organic human speech. By triangulating hidden state geometry, entropy analysis, and causal interventions, we demonstrate that this disparity stems from shortcut learning (CITATION)—models exploit the low-entropy statistical signatures of generated text while remaining “semantically blind” to the pragmatic cues essential for irony. Our findings indicate that high performance on synthetic leaderboards reflects forensic pattern matching rather than the genuine linguistic intelligence assumed in prior work, creating a statistical mirage of competence.

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.1347ACL Anthology · ACL 2026

Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks

Xiang Cheng Renmin University of China, Yulan Hu Alibaba Group, Xiangwen Zhang, Lu Xu, Lide Tan, Zheng Pan Alibaba Group, Xin Li Alibaba Group, Yong Liu Renmin University of China

原文摘要

Travel planning is a natural real-world task to test large language models’ (LLMs) planning and tool-use abilities. Although prior work has studied LLM performance on travel planning, existing settings still differ from real-world needs, mainly due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries. To mitigate these gaps, we propose \mbox{\textbf{TravelBench}}, a benchmark for \textit{truly real-world} travel planning. We collect user queries, user preferences, and tools from real scenarios, and construct three subtasks—\textit{Single-Turn}, \textit{Multi-Turn}, and \textit{Unsolvable}—to evaluate agents’ three core capabilities in real settings: (1) solving problems independently, (2) interacting with users to elicit implicit preferences, and (3) recognizing the capability boundaries. To enable stable tool invocation and reproducible evaluation, we cache real tool-call results and build a sandbox environment which integrates ten travel-related tools, enabling agents to combine these tools to solve most practical travel planning problems. We evaluate multiple LLMs on TravelBench and find that even advanced models exhibit imbalanced performance across different capabilities. Our further systematic verification demonstrates the stability of the proposed benchmark. TravelBench provides a practical and reproducible benchmark to advance research on LLM agents for real-world travel planning.

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.1348ACL Anthology · ACL 2026

TLoRA: Task-aware Low Rank Adaptation of Large Language Models

Weicheng Lin Shenzhen University, Yi Zhang Shenzhen University, Jiawei Dang Shenzhen University, Liang-Jie Zhang Shenzhen University

原文摘要

Low-Rank Adaptation (LoRA) has become a widely adopted parameter-efficient fine-tuning method for large language models, with its effectiveness largely influenced by the allocation of ranks and scaling factors, as well as initialization. Existing LoRA variants typically address only one of these factors, often at the cost of increased training complexity or reduced practical efficiency.In this work, we present Task-aware Low-Rank Adaptation (TLoRA), a unified framework that jointly optimizes initialization and resource allocation at the outset of training. TLoRA introduces a data-driven initialization strategy that aligns the LoRA A matrix with task-relevant subspaces by performing singular value decomposition on the product of pre-trained weights and input activation covariance. After this, the A matrix is frozen, and only the B matrix is trained. Furthermore, TLoRA employs a sensitivity-based importance metric to adaptively allocate ranks and scaling factors across layers under a fixed parameter budget.We conduct extensive experiments that demonstrate TLoRA consistently performs excellently across various tasks, including natural language understanding, commonsense reasoning, math reasoning, code generation, and chat generation, while significantly reducing the number of trainable parameters. Our code are available at https://github.com/Rambo-Yi/TLora/tree/main

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.1349ACL Anthology · ACL 2026

MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation

Dawei Wang, Di Zhao University of Auckland, Xinyuan Liu, Marci Chi Ma Newcastle University, UK, Xiaoyang Liu Newcastle University, UK, Chengming Zhou, Gary Ushaw Newcastle University, UK, Richard Davison University of Newcastle-upon-Tyne

原文摘要

Credit assignment is a fundamental challenge in cooperative multi-agent reinforcement learning, particularly in embodied AI settings characterized by limited and delayed feedback as well as dynamically changing numbers of active agents. We propose MARS-RA, a framework that reformulates credit assignment as a rank aggregation problem using contribution-based pairwise comparisons among agents generated by large multimodal models. This shift from absolute to relative estimation ensures robustness against noise and dynamic agent participation, converting comparison results into contribution scores for potential-based reward shaping. We provide theoretical justification for the convergence and robustness of the proposed framework, and show that Shapley values can be used as an interpretive reference. Experimental results on challenging tasks of different types indicate that MARS-RA can guide agents toward effective cooperation.

cs.CL · cs.MA · 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.135ACL Anthology · ACL 2026

Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models

Jinyang Wu Tsinghua University, Mingkuan Feng Tsinghua University, Shuai Zhang, Feihu Che, Zhengqi Wen Tsinghua University, Chonghua Liao, Ling Yang Princeton University, Haoran Luo Nanyang Technological University 等 10 位作者

原文摘要

In-context learning (ICL) leverages demonstrations to enhance the performance of large language models (LLMs). However, traditional ICL struggles with complex reasoning mainly due to superficial, example-level implicit imitation. To address these limitations, we introduce ThoughtICR, an automated Thought-level In-Context Reasoning paradigm that shifts from surface-level examples to more guidance-oriented thought patterns. Specifically, we first define atomic reasoning actions and construct thought patterns on small-scale seed data using Monte Carlo Tree Search (MCTS). During inference, we dynamically select appropriate thought patterns based on target problem attributes, providing explicit guidance for model reasoning. Thanks to its automated and strategic design, our method enables seamless plug-and-play integration with various post-training techniques. Experimental results demonstrate that our method improves performance across different model sizes and generalizes effectively across reasoning domains. Using only small-scale seed data, we achieve 80.6% accuracy on MATH and 62.5% on AMC, surpassing GPT-4o’s 77.2% and 57.5%, respectively. Moreover, compared to test-time scaling methods, our approach reduces computational costs by over 10. Our code is available at https://github.com/jinyangwu/ThoughtICR.

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.1350ACL Anthology · ACL 2026

Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events

Yuqin Yang, Haowu Zhou South China University of Technology, Haoran Tu, Zhiwen Hui, Shiqi Yan South China University of Technology, HaoYang Li, Dong She South China University of Technology, Xianrong Yao 等 10 位作者

原文摘要

Most affective computing research treats emotion as a static property of text, focusing on the writer’s sentiment while overlooking the reader’s perspective. This approach ignores how individual personalities lead to diverse emotional appraisals of the same event. Although role-playing Large Language Models (LLMs) attempt to simulate such nuanced reactions, they often suffer from "personality illusion”—relying on surface-level stereotypes rather than authentic cognitive logic. A critical bottleneck is the absence of ground-truth human data to link personality traits to emotional shifts. To bridge the gap, we introduce Persona-E² (Persona-Event2Emotion), a large-scale dataset grounded in annotated MBTI and Big Five traits to capture reader-based emotional variations across news, social media, and life narratives. Extensive experiments reveal that state-of-the-art LLMs struggle to capture precise appraisal shifts, particularly in social media domains. Crucially, we find that personality information significantly improves comprehension, with the Big Five traits alleviating "personality illusion.’

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.1351ACL Anthology · ACL 2026

Interleaved Latent Visual Reasoning with Selective Perceptual Modeling

Shuai Dong, Siyuan Wang The Chinese University of Hong Kong, Xingyu Liu, Chenglin Li, Haowen Hou ETHZ - ETH Zurich and Shanghai Jiao Tong University, Zhongyu Wei Fudan University

原文摘要

Interleaved reasoning paradigms enhance Multimodal Large Language Models (MLLMs) with visual feedback but are hindered by the prohibitive computational cost of re-encoding pixel-dense images. A promising alternative, latent visual reasoning, circumvents this bottleneck yet faces limitations: methods either fail to capture intermediate state evolution due to single-step, non-interleaved structures, or sacrifice precise perceptual modeling by over-compressing features. We introduce Interleaved Latent Visual Reasoning (ILVR), a framework that unifies dynamic state evolution with precise perceptual modeling. ILVR interleaves textual generation with latent visual representations that act as specific, evolving cues for subsequent reasoning. Specifically, we employ a self-supervision strategy where a momentum teacher model selectively distills relevant features from ground-truth intermediate images into sparse supervision targets. This adaptive selection mechanism guides the model to autonomously generate context-aware visual signals. Extensive experiments on multimodal reasoning benchmarks demonstrate that ILVR outperforms existing approaches, effectively bridging the gap between fine-grained perception and sequential multimodal reasoning.

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.1352ACL Anthology · ACL 2026

Think before Go: Hierarchical Reasoning for Image-goal Navigation

Pengna Li, Kangyi Wu, Shaoqing Xu, Fang Li, Lin Zhao, Long Chen Xiaomi, Zhi-Xin Yang University of Macau, Nanning Zheng Xi'an Jiaotong University

原文摘要

Image-goal navigation steers an agent to a target location specified by an image in unseen environments. Existing methods primarily handle this task by learning an end-to-end navigation policy, which compares the similarities of target and observation images and directly predicts the actions. However, when the target is distant or lies in another room, such methods fail to extract informative visual cues, leading the agent to wander around. Motivated by the human cognitive principle that deliberate, high-level reasoning guides fast, reactive execution in complex tasks, we propose Hierarchical Reasoning Navigation (HRNav), a framework that decomposes image-goal navigation into high-level planning and low-level execution. In high-level planning, a vision-language model is trained on a self-collected dataset to generate a short-horizon plan, such as whether the agent should walk through the door or down the hallway. This downgrades the difficulty of the long-horizon task, making it more amenable to the execution part. In low-level execution, an online reinforcement learning policy is utilized to decide actions conditioned on the short-horizon plan. We also devise a novel Wandering Suppression Penalty (WSP) to further reduce the wandering problem. Together, these components form a hierarchical framew ork for Image-Goal Navigation. Extensive experiments in both simulation and real-world environments demonstrate the superiority of our method.

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