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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1802ACL Anthology · ACL 2026

S^4: Operationalizing Speech Act Theory for Strategic Semi-Structured Psychiatric Interview

Guanqun Bi Tsinghua University, Zhoufu Liu, Zhuang Chen Central South University, Dazhen Wan Beijing Lingxin Intelligent Technology Co., Ltd, Xiyao Xiao, Minlie Huang

原文摘要

Psychiatric interviewing is a strategic, goal-oriented interaction that requires proactively steering the conversation to elicit latent information. However, existing methods often degenerate into rigid interrogation or aimless chitchat due to a lack of strategic planning. In this work, we introduce S4, a comprehensive framework grounded in Speech Act Theory, modeling the interview as a unified process of internal strategy (Illocution and Perlocution) and external realization (Locution). We synthesize a large-scale dataset with fine-grained psychiatric speech act annotations. Trained on this data, S4Dial employs reinforcement learning driven by long-term therapeutic effects to optimize the strategic chaining of atomic acts, aiming to maximally elicit information and maintain patient engagement. Experiments demonstrate that S4 significantly outperforms baselines, validating the effectiveness of our effect-driven strategic modeling.

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.1803ACL Anthology · ACL 2026

More Aligned, Less Diverse? Analyzing the Grammar and Lexicon of Two Generations of LLMs

Adrián Gude Universidad de La Coruña, Roi Santos-Rios, Francis Bond Palacký University Olomouc, Dan Flickinger, Carlos Gómez-Rodríguez Universidade da Coruña, Olga Zamaraeva Universidad de La Coruña

原文摘要

This study contributes to a growing line of research in comparing LLM-generated texts with human-authored text, in this case, English news text. We focus in particular on the evaluation of syntactic properties through formal grammar frameworks. Our analysis compares two generations of LLMs in the context of two human-authored English news datasets from two different years. Employing the Head-Driven Phrase Structure Grammar (HPSG) formalism, we investigate the distributions of syntactic structures and lexical types of AI-generated texts and contrast them with the corresponding distributions in the human-authored New Your Times (NYT) articles. We use diversity metrics from ecology and information theory to quantify variation in grammatical constructions and lexical types. Our results show that, while English news text has changed little in the given time frame, newer, instruction-tuned LLMs display reduced syntactic and, especially, lexical diversity compared to older, non-instruction-tuned models. These findings point to future work in studying effects of instruction tuning, which, while enhancing coherence and adherence to prompts, may narrow the expressive range of model output.

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.1804ACL Anthology · ACL 2026

MemCoRL: Alternating Co-Optimization of Memory Retrieval and Utilization via Collaborative Reinforcement Learning

Yuewen Liu, Peng Xu, Muxi Diao, Anyi Zhang Beijing University of Posts and Telecommunications, Yang Li, Yutong Zhang

原文摘要

Large Language Models (LLMs) are inherently constrained by their fixed-length context windows, which limits LLMs’ ability to retain and utilize information across long-term interactions. To address this limitation, recent work has proposed external memory modules for LLMs. Using memory modules typically involves two stages: evidence retrieval and memory utilization. While prior work focuses on the architecture of memory modules and the retrieval stage, the equally critical memory utilization stage remains underexplored. Building on this, we propose MemCoRL, a two-stage alternating co-optimization reinforcement learning method. Stage 1 optimizes evidence retrieval using citation feedback and semantic accuracy from utilization as rewards. Stage 2 optimizes utilization with rewards combining semantic similarity and lexical overlap. Iterative co-optimization establishes a positive feedback loop: better retrieval improves memory utilization, which in turn refines retrieval rewards. Experimental results show our approach outperforms the leading baselines on both lexical overlap and semantic similarity metrics, confirming the co-optimization in memory retrieval and memory utilization.

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.1805ACL Anthology · ACL 2026

Fast and Accurate Fisher-Guided Quantization via Efficient Kronecker Factorization

Viktoriia A. Chekalina FusionBrain Lab, Gerasin Timofey Independent, Andrey Kuznetsov Innopolis University and FusionBrain Lab, Evgeny Frolov Higher School of Economics, Higher School of Economics and AXXX

原文摘要

Quantization has shown strong results in preserving model quality under compression. However, under aggressive bit-width reductions, even quantization may require additional information to prevent performance degradation. A natural source of it is second-order curvature information, captured by the Hessian. Since the Hessian of the model layers is prohibitively large, direct computation is infeasible, making structured parameterizations and approximations crucial in practice.In this work, we propose efficient Kronecker-factored approximation yielding state-of-the-art performance when integrated into existing quantization schemes. Evaluations on the LLaMA and Qwen model families show near-baseline quality at 4-bit compression and only a 5–6% degradation at 2-bit. Moreover, our method substantially accelerates the most expensive component in second-order quantization – Hessian parameterization – achieving up to a 10× speedup over prior approaches.

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.1806ACL Anthology · ACL 2026

Discovering a Shared Logical Subspace: Steering LLM Logical Reasoning via Alignment of Natural-Language and Symbolic Views

Feihao Fang, My T. Thai, Yuanyuan Lei University of Florida

原文摘要

Large Language Models (LLMs) still struggle with multi-step logical reasoning. Existing approaches either purely refine the reasoning chain in natural language form or attach a symbolic solver as an external module. In this work, we instead ask whether LLMs contain a shared internal logical subspace that simultaneously aligns natural-language and symbolic-language views of the reasoning process. Our hypothesis is that this logical subspace captures logical reasoning capabilities in LLMs that are shared across views while remaining independent of surface forms. To verify this, we employ Canonical Correlation Analysis on the paired residual activations from natural-language and symbolic-language reasoning chains, learning a low-dimensional subspace with maximum cross-view correlation. Furthermore, we design a training-free approach that steers LLMs reasoning chain along this logical subspace, thereby leveraging the complementary reasoning signals from both views. Experiments on four logical reasoning benchmarks demonstrate the effectiveness of our approach, improving accuracy by up to 11 percentage points and generalizing well on out-of-domain problems.

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.1807ACL Anthology · ACL 2026

One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement

Yixiao Zhou, Dongzhou Cheng, Zhiliang wu Nanyang Technological University, Yi Yang Zhejiang University, Yu Cheng Skywork AI and The Chinese University of Hong Kong, Hehe Fan Zhejiang University

原文摘要

Large Language Models (LLMs) often fail to utilize their latent reasoning capabilities due to a distributional mismatch between ambiguous human inquiries and the structured logic required for machine activation. Existing alignment methods either incur prohibitive O(N) costs by fine-tuning each model individually or rely on static prompts that fail to resolve query-level structural complexity. In this paper, we propose ReQueR (Reinforcement Query Refinement), a modular framework that treats reasoning elicitation as an inference-time alignment task. We train a specialized Refiner policy via Reinforcement Learning to rewrite raw queries into explicit logical decompositions, treating frozen LLMs as the environment. Rooted in the classical Zone of Proximal Development from educational psychology, we introduce the Adaptive Solver Hierarchy, a curriculum mechanism that stabilizes training by dynamically aligning environmental difficulty with the Refiner’s evolving competence. ReQueR yields consistent absolute gains of 1.3%–7.2% across diverse architectures and benchmarks, outperforming strong baselines by 2.1% on average. Crucially, it provides a promising paradigm for one-to-many inference-time reasoning elicitation, enabling a single Refiner trained on a small set of models to effectively unlock reasoning in diverse unseen Solvers. Code is available at https://github.com/newera-xiao/ReQueR.

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.1808ACL Anthology · ACL 2026

Why Does Reinforcement Learning Generalize? A Feature-Level Mechanistic Study of Post-Training in Large Language Models

Dan Shi, Zhuowen Han, Simon Ostermann German Research Center for AI and Universität des Saarlandes, Renren Jin, Josef van Genabith German Research Center for AI and Universität des Saarlandes, Deyi Xiong Tianjin University

原文摘要

Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models (LLMs) beyond the training domain, while supervised fine-tuning (SFT) frequently leads to general capabilities forgetting. However, the mechanisms underlying this contrast remain unclear.To bridge this gap, we present a feature-level mechanistic analysis methodology to probe RL generalization using a controlled experimental setup, where RL- and SFT-tuned models are trained from the same base model on identical data. Leveraging our interpretability framework, we align internal activations across models within a shared feature space and analyze how features evolve during post-training.We find that SFT rapidly introduces many highly specialized features that stabilize early in training, whereas RL induces more restrained and continually evolving feature changes that largely preserve base models’ representations. Focusing on samples where RL succeeds but the base model fails, we identify a compact, task-agnostic set of features that directly mediate generalization across diverse tasks. Feature-level interventions confirm their causal role: disabling these features significantly degrades RL models’ generalization performance, while amplifying them improves base models’ performance. The code is available at https://github.com/danshi777/RL-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.1809ACL Anthology · ACL 2026

Polymorphic Universal Transformer

Yilong Chen, Zitian Gao Ubiquant, Yihao Xiao, Jason Klein Liu, Xinyu Yang Carnegie Mellon University, Yifan Luo Peking University, Haoming Luo, Zhengmao Ye 等 11 位作者

原文摘要

Although the Universal Transformer (UT) mitigates the diminishing returns of standard LLM scaling by decoupling parameter count from depth, it remains constrained by linear computational costs and rigid weight-sharing mechanisms. These limitations lead to severe functional homogeneity, which subsequently induces over-smoothing, representation rank collapse, and degraded reasoning performance. In this work, we present the first systematic study of Compute Distribution Skew, identifying it as the primary driver of extrapolation failure. This is a pathological phenomenon in ultra-deep recurrent Transformers characterized by a disproportionate distribution of contributions across recurrent steps, resulting in distinct functional states during prefix and suffix processing phases. To address this challenge, we propose the Polymorphic Transformer, which aims to achieve functional polymorphism and depth sparsity within a shared-parameter framework. By integrating conditional sparse subspaces, SiLU Attention, and an uncertainty-aware depth scheduler, our architecture mitigates power-method collapse and effectively decouples logical depth from computational cost. Experiments demonstrate that our model significantly enhances representation rank and robustness, achieving complex reasoning performance comparable to baseline while reducing computation by 64.7%.

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.181ACL Anthology · ACL 2026

Dynamic Emotion and Personality Profiling for Multimodal Deception Detection

Li Zheng, Yanyi Luo, Hao Fei National University of Singapore and University of Oxford, Yuzhe Ding Wuhan University, Yujie Huang Wuhan University, Fei Li Wuhan University, Chong Teng Wuhan University, Donghong Ji Wuhan University

原文摘要

Deception detection is of great significance for ensuring information security and conducting public opinion analysis, with personality factors and emotion cues playing a critical role. However, existing methods lack sample-level dynamic annotations for emotions and personality. In this paper, we propose an innovative multi-model multi-prompt annotation scheme and a strict label quality evaluation standard, and establish a multimodal joint detection dataset DDEP for deception, emotion, and personality. Meanwhile, we propose Rel-DDEP, an adaptive reliability-weighted fusion framework. Our framework quantifies uncertainty by mapping modal features to a high-dimensional Gaussian distribution space. It then performs reliability-weighted fusion and incorporates an alignment module and a sorting constraint module to achieve joint detection of deception, emotion, and personality. Experimental results on the MDPE and DDEP datasets show that our Rel-DDEP significantly outperforms the existing state-of-the-art baseline models in three tasks. The F1 score of the deception detection increases by 2.53%, that of the emotion detection increases by 2.66%, and that of the personality detection increases by 9.30%. The experiments fully verify the necessity of annotating dynamic emotion and personality labels for each sample and the effectiveness of reliability-weighted fusion.

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.1810ACL Anthology · ACL 2026

Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression

Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang City University of Hong Kong, Chao Shen Xi’an Jiaotong University

原文摘要

Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance. Existing works focus on compressing generated CoT in reasoning, which impairs the necessary information for deriving the correct answer. In this work, we propose post-reasoning, a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for LLMs. We find that post-reasoning significantly reduces the generation length of LLMs, but its effectiveness hinges on the efficiency and the reliability of the contextual CoT generation.Therefore, we propose Upfront CoT (UCoT), an efficient post-reasoning framework for CoT compression. UCoT trains a lightweight model (compressor) to provide contextual CoT in form of soft tokens and trains the LLM (executor) to leverage this contextual CoT for producing the final answer. Extensive experiments show that UCoT maintains the powerful reasoning ability of executor while significantly reducing the length of CoT. It is worth mentioning that when applying UCoT to the Qwen2.5-7B-Instruct model, the usage of tokens on GSM8K dataset is reduced by 50%, while the performance is 3.08% higher than that of the state-of-the-art (SOTA) method. The code is available at: https://github.com/czx-li/UCoT.

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.1811ACL Anthology · ACL 2026

REAL: REtrieval-reAsoning and Logic-constructed Attention Behaviors for Long-Context KV Cache Compression

Mengjie Li Yonsei University, Yuan Feng, Xike Xie, William J. Song Yonsei University

原文摘要

The growing sequence length of large language models poses significant challenges for key-value (KV) caches. Existing state-of-the-art cache eviction methods primarily analyze the inference behavior of attention heads in successful retrieval-reasoning cases, often overlooking diverse behaviors in failure cases, such as bias and distraction. This oversight limits the potential to leverage heterogeneous head behaviors for improved eviction performance. Inspired by the confusion matrix, we introduce an Attention Behavior Matrix to comprehensively analyze attention head behaviors in both success and failure scenarios. By maximizing the signal-to-noise ratio — strengthening valid reasoning pathways in success cases while inhibiting noise from bias and distraction in failure cases — we propose REtrieval-reAsoning and Logic-constructed (REAL) KV cache eviction, the first method to leverage multi-behavior analysis. Comprehensive evaluations show that REAL achieves remarkable performance across various models and benchmarks; notably, on LongBench v2, it achieves comparable accuracy to the strongest baseline, HeadKV-R2, while requiring 32x less space. By offering a novel perspective on behavior analysis, we pave the way for a shift from success-only to comprehensive, failure-aware methods in long-context modeling. Our code is available at https://github.com/yonseicasl/REAL.

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.1812ACL Anthology · ACL 2026

From Where Words Come: Efficient Regularization of Code Tokenizers Through Source Attribution

Pavel Chizhov Technical University of Applied Sciences Würzburg-Schweinfurt and PleIAs, Egor Bogomolov Delft University of Technology and JetBrains Research, Ivan P. Yamshchikov Technical University of Applied Sciences Würzburg-Schweinfurt

原文摘要

Efficiency and safety of Large Language Models (LLMs), among other factors, rely on the quality of tokenization. A good tokenizer not only improves inference speed and language understanding but also provides extra defense against jailbreak attacks and lowers the risk of hallucinations. In this work, we investigate the efficiency of code tokenization, in particular from the perspective of data source diversity. We demonstrate that code tokenizers are prone to producing unused, and thus under-trained, tokens due to the imbalance in repository and language diversity in the training data, as well as the dominance of source-specific, repetitive tokens that are often unusable in future inference. By modifying the BPE objective and introducing merge skipping, we implement different techniques under the name Source-Attributed BPE (SA-BPE) to regularize BPE training and minimize overfitting, thereby substantially reducing the number of under-trained tokens while maintaining the same inference procedure as with regular BPE. This provides an effective tool suitable for production use.

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.1813ACL Anthology · ACL 2026

EmoS: A High-Fidelity Multimodal Benchmark for Fine-grained Streaming Emotional Understanding

Pengze Guo, Jingxi Liang University of Macau, Zhiwen Xie Central China Normal University, Qifeng Wang, Derek F. Wong University of Macau

原文摘要

In the context of today’s high-pressure, aging society, the demand for large-scale emotional models capable of providing empathetic support is more critical than ever. However, existing benchmarks fail to simultaneously achieve ecological validity, signal clarity, and reliable fine-grained labeling. We introduce EmoS, a high-fidelity bilingual benchmark designed to resolve the limitations of ecological validity and noise in existing datasets by combining strictly filtered static slices with a dynamic Streaming Monologue subset. Supported by a rigorous dual-layer human annotation pipeline, EmoS provides trusted ground truth that captures continuous emotional evolution. Empirical results show that fine-tuning MLLMs (multimodal large language models) on EmoS yields significant gains over zero-shot baselines, laying the foundation for the training and evaluation of future emotion recognition models and empathy models. The dataset and code are publicly available at https://github.com/NLP2CT/EmoS.

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.1814ACL Anthology · ACL 2026

Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue

Thomas P. Utting University of Aberdeen, Mario Giulianelli University College London, University of London, Arabella Sinclair

原文摘要

We model utterance production as probabilistic cost-sensitive choice over contextual alternatives, using information-theoretic notions of cost. We distinguish between goal-directed alternatives that realise a fixed communicative intent and goal-agnostic alternatives defined only by contextual plausibility, allowing us to derive speaker- and listener-oriented interpretations of different cost measures. We present a procedure to generate both types of alternative sets using language models. Analysing production choices in open-ended dialogue under both deterministic and noisy cost minimisation, we find that surprisal minimisation relative to goal-directed alternatives provides the strongest explanation of production choices. Uniformity-based costs show weaker overall predictive power, but their influence increases markedly when evaluated relative to goal-agnostic alternatives, consistent with listener-oriented accounts. More broadly, our study suggests that alternative-conditioned optimisation with LM-generated alternatives provides a principled framework for studying speaker and listener pressures in naturalistic language production.

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.1815ACL Anthology · ACL 2026

HowToNarrate: A General-Domain Benchmark for Synchronized Video Narration with External Knowledge

Xueyan Wang, Dingyi Yang, Qin Jin Renmin University of China

原文摘要

We present HowToNarrate, the first general-domain benchmark for Synchronized Video Narration. The benchmark contains 3.2K videos across seven domains, segmented into 37.5K clips with aligned narrations and associated external knowledge. Effective narration requires models to understand visual scenes, incorporate relevant knowledge, and produce coherent, length-appropriate descriptions. We systematically benchmark current Multimodal LLMs (MLLMs) on these abilities. Our analysis shows that existing MLLMs overemphasize knowledge retrieval while largely neglecting prior context (receiving less than 10% attention). Moreover, they often conflate narration context with external knowledge, leading to redundancy and incoherence. To mitigate these issues, we propose VideoNarrationAgent, a multi-agent framework that combines context compression, knowledge retrieval, and narration generation. Experiments demonstrate that our method significantly improves MLLM performance. Furthermore, instruction tuning on HowToNarrate enhances both context-awareness and length control, boosting Qwen2.5-VL’s score from 25 to 84. We will release all data and code to support future research in synchronized video narration.

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.1816ACL Anthology · ACL 2026

Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment

Woody Haosheng Gan, William Barr Held OpenAthena, Diyi Yang Stanford University

原文摘要

The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are costly. To fill this gap, we investigate whether minimal subsets can reliably evaluate LAMs while reducing costs and data redundancy. Analyzing 10 subset selection methods with 18 audio models across 40 tasks covering major LAM evaluation dimensions, we show that subsets of just 50 examples (0.3% of data) can achieve over 0.93 Pearson correlation with full benchmark scores. To understand how well these scores align with what practitioners ultimately care about—user satisfaction—we collect 776 human preference ratings from realistic voice assistant conversations, finding that both subsets and full benchmark achieve only 0.85 correlation with human. To better predict preferences, we trained regression models on these selected subsets, achieving 0.98 correlation—outperforming regression models trained on both random subsets and the full benchmark. This demonstrates that in regression modeling, well-curated subsets outpredict the full benchmark, showing quality over quantity. We open-source these regression-weighted subsets as the HUMANS benchmark, an efficient proxy for LAM evaluation that captures both benchmark performance and user preferences.

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.1817ACL Anthology · ACL 2026

Towards Fast and Accurate Modeling for Cross-Lingual Label Projection

Thang Le Mohamed bin Zayed University of Artificial Intelligence, Huy Huu Nguyen, Anh Tuan Luu Nanyang Technological University, Thamar Solorio Mohamed bin Zayed University of Artificial Intelligence and University of Houston, Thien Huu Nguyen University of Oregon

原文摘要

Information extraction (IE) systems rely on structured data for training, but such annotated data is highly imbalanced across languages, with low-resource languages receiving little attention. Label projection techniques aim to bridge this gap by transferring structured annotations from high-resource to low-resource languages. However, existing methods are either inaccurate or too slow for large-scale use. This work aims to address this problem by developing a more effective method that remains sufficiently efficient for large-scale projection. In particular, we propose to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective, while controlling data influence during training. Experimental results across 50+ languages show that our framework consistently outperforms previous state-of-the-art methods while maintaining fast inference speed. In addition, we introduce EXP - the first benchmark for explicit evaluation of label projection, thereby reducing confounders and non-determinism in method assessment.

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.1818ACL Anthology · ACL 2026

False Friends or Cognates? A Cross-lingual Semantic Ambiguity Evaluation for Galician, Portuguese and Spanish

Marta Vázquez Abuín, Jose Camacho-Collados Cardiff University, Marcos Garcia Universidade de Santiago de Compostela

原文摘要

The linguistic proximity between Galician, Portuguese, and Spanish results in a lexical overlap that often conceals semantic interference. This is particularly evident in false friends, posing a challenge for NLP systems.In this work, we assess whether state-of-the-art language models can identify and process false friends among these languages. We introduce six cross-lingual datasets –created manually or using semi-automatic methods, with all instances being carefully verified– covering cognates and false friends. We evaluate a broad range of encoder and decoder models of varying sizes via zero-shot and few-shot settings. Our results highlight the challenging nature of the task, but also show the clear progress made by LLMs in recent years, particularly those of a larger size, with smaller language models struggling on the task. Notably, unlike other tasks where language distance poses additional challenges, we find that linguistic proximity itself introduces errors: closely related language pairs tend to perform worse, reflecting the challenge of semantic discrimination due to lexical overlap.

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.1819ACL Anthology · ACL 2026

BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks

Rui Miao, Yixin Liu Griffith University, Yili Wang, Xu Shen, Yue Tan Griffith University, Yiwei Dai, Shirui Pan Griffith University, Xin Wang

原文摘要

The security of LLM-based multi-agent systems (MAS) is critically threatened by propagation vulnerability, where malicious agents can distort collective decision-making through inter-agent interactions. While existing supervised defense methods demonstrate promising performance, they may be impractical in real-world scenarios due to their heavy reliance on labeled malicious agents to train a supervised malicious detection model. To enable practical and generalizable MAS defenses, in this paper, we propose BlindGuard, an unsupervised defense method that learns without requiring any attack-specific labels or prior knowledge of malicious behaviors. To this end, we establish a hierarchical agent encoder to capture individual, neighborhood, and global interaction patterns of each agent, providing a comprehensive understanding for malicious agent detection. Meanwhile, we design a corruption-guided detector that consists of directional noise injection and contrastive learning, allowing effective detection model training solely on normal agent behaviors. Extensive experiments show that BlindGuard effectively detects diverse attack types across MAS with various communication patterns while maintaining superior generalizability compared to supervised baselines.

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.182ACL Anthology · ACL 2026

Can We Predict Before Executing Machine Learning Agents?

Jingsheng Zheng, Jintian Zhang, Yujie Luo, Yuren Mao, Yunjun Gao Zhejiang University, Lun Du Ant Group, Huajun Chen Zhejiang University and ZJU-Hangzhou Global Scientific and Technological Innovation Center, Ningyu Zhang Zhejiang University

原文摘要

Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffers from a severe Execution Bottleneck, as hypothesis evaluation relies strictly on expensive physical execution. To bypass these physical constraints, we internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models. In this work, we formalize the task of Data-centric Solution Preference and construct a comprehensive corpus of 18,438 pairwise comparisons. We demonstrate that LLMs exhibit significant predictive capabilities when primed with a Verified Data Analysis Report, achieving 61.5% accuracy and robust confidence calibration. Finally, we instantiate this framework in ForeAgent, an agent that employs a Predict-then-Verify loop, achieving a 6x acceleration in convergence while surpassing execution-based baselines by +6%.

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.1820ACL Anthology · ACL 2026

Black-Box Membership Inference Attacks for Video Training Data in Multimodal Large Language Models

Jinrui Wang Beijing University of Posts and Telecommunications, Zhenfeng Gao, Wendan Wang, Huili Wang, Zichen Qin, Linjie Zhu, Hongke Fu Sangfor Technologies Inc., Shangguang Wang Xiong'an Aerospace Information Research Institute and Beijing University of Posts and Telecommunications 等 9 位作者

原文摘要

The increasing use of video data in training multimodal large language models (MLLMs) raises significant concerns on privacy leakage and copyright violations, highlighting the need for detecting improperly used training videos through membership inference attacks (MIAs). Most existing video MIA methods assess model memorization of key semantic concepts within a video (e.g., the name of a well-known movie character). However, such concepts usually appear repeatedly throughout the training corpus, and memorization of them does not constitute reliable evidence that a specific video was used during training. Besides, while some methods mitigate this limitation by capturing relationships between frames, they require a model logit-accessible setting and are impractical in realistic black-box scenarios. To address these challenges, we propose a black-box MIA framework, named VideoMIA, that can provide reliable evidence of specific video data usage for training MLLMs. The key of our method is to leverage temporal dependencies across video frames to evaluate the model’s memorization of sequential dynamics within the video data, which cannot be inferred solely from general world knowledge or individual image data. The results across ten MLLMs and four benchmarks demonstrate that our method consistently achieves superior performance over all baselines in black-box evaluation settings. Code is available in https://github.com/jinruiwang258/VideoMIA.

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.1821ACL Anthology · ACL 2026

Mitigating Safety Context Amnesia in Multimodal Reasoning Models via Intent-Guided Safety Reasoning

Xiyao Dong, Guangsheng Cheng, YiLong Chen, Xiaojin Zhang, Kun He Huazhong University of Sceince and Technology

原文摘要

Recent advances in Multimodal Large Reasoning Models (MLRMs) have enabled explicit chain-of-thought inference across vision and language, substantially improving performance on complex reasoning tasks. Despite these gains, the reasoning process introduces a subtle yet critical vulnerability. We identify an underexplored multimodal safety failure mode in which harmful objectives are embedded within ostensibly benign contexts, leading models to over-prioritize narrative coherence during reasoning. We term this phenomenon Safety Context Amnesia (SCA), wherein models correctly perceive risk-relevant visual cues but fail to enforce safety constraints as the reasoning process becomes dominated by contextual alignment. To mitigate SCA, we propose Intent-Guided Safety Reasoning (IGSR), an inference-time defense that operates without modifying target model parameters. IGSR employs a Perception Decoupler to extract objective visual evidence into a structured intent output, followed by a Cognitive Arbiter that enforces explicit safety constraints prior to generation. Extensive experiments across multiple multimodal safety benchmarks demonstrate that IGSR improves defense success rates by over 62% compared to baselines, while largely preserving task utility. These results highlight the critical role of structured, intent-aware reasoning in achieving robust safety reasoning for multimodal reasoning models.

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.1822ACL Anthology · ACL 2026

On-policy Reinforcement Fine-tuning with Offline reward for Multi-step Embodied Planning

Di Wu, Jiaxin Fan tongji university, Chloe Gu, Guanbo Wang, Wei Yin, Wenhao Li Tongji University, Bo Jin Tongji University

原文摘要

Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and verbal goals. While recent vision-language models (VLMs) excel at static perception tasks, they struggle in interactive environments. Reinforcement learning (RL) offers a natural way to address this limitation, yet online RL approaches suffer from costly interaction and sparse rewards in embodied settings. This paper introduces ORBIT, an On-policy Reinforcement fine-tuning (RFT) framework with offline rewards for EmBodIed Task Planning, that preserves the generalization benefits of RFT while addressing the challenges of costly interaction and sparse rewards, supported by solid theoretical guarantees. Our approach is evaluated on EmbodiedBench, a recent benchmark for interactive embodied tasks, covering both in-domain and out-of-domain scenarios. Experimental results show that ORBIT achieves SOTA performance on EB-ALFRED, outperforming all closed-source and online-RL-based methods, while being substantially more efficient in training speed and computational cost, remaining robust to sub-optimal expert trajectories, and exhibiting strong generalization to unseen environments. We released all code and data at https://github.com/mail-taii/Reinforced-Reasoning-for-Embodied-Planning.

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.1823ACL Anthology · ACL 2026

Please refuse to answer me! Mitigating Over-Refusal in Large Language Models via Adaptive Contrastive Decoding

Yupeng Qi, Ziyu Lyu Sun Yat-Sen University, Lixin Cui, Lu Bai, Feng Xia Royal Melbourne Institute of Technology

原文摘要

Safety-aligned large language models (LLMs) often generate refusal responses to harmless queries due to the over-refusal problem. However, existing methods for mitigating over-refusal cannot maintain a low refusal ratio for harmless queries while keeping a high refusal ratio for malicious ones. In this paper, we analyze how system prompts with varying safety levels affect LLM refusal behaviors when facing over-refusal queries. A key observation is that, when LLMs suffer from the over-refusal issue, non-refusal tokens remain present in the next-token candidate list, but the model systematically fails to select them, despite the generation of refusal tokens. Based on this observation, we propose a training-free and model-agnostic approach, Adaptive Contrastive Decoding (AdaCD), to mitigate over-refusal while maintaining LLM safety. First, AdaCD compares the output distributions of the LLM with or without an extreme safety system prompt to refine the refusal token distribution. Second, we introduce an adaptive contrastive decoding strategy that dynamically incorporates or removes the refusal token distribution, adaptively boosting the probability of selecting refusal or non-refusal tokens. Experimental results on five benchmark datasets show that, on average, AdaCD reduces the refusal ratio for over-refusal queries by 10.35%, yet still increases the refusal ratio for malicious queries by 0.13%. Code is available at https://shorturl.at/Z31Oe.

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.1824ACL Anthology · ACL 2026

Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs’ Hallucinations

Xinyue Fang, Zhiliang Tian National University of Defense Technology, Zhen Huang National University of Defense Technology, Ziyi Pan, Zhihua Wen, Xi Wang, Quntian Fang, Dongsheng Li National University of Defense Technology

原文摘要

Existing LLM hallucination mitigation methods, including prompt engineering and model optimization, either hardly alter models’ internal knowledge or have poor cross-domain generalization. Contrastive decoding mitigates hallucinations by using layer-wise differences in LLMs. However, prior studies only explore transformer-based models (e.g., GPT), ignoring other effective frameworks like mixture-of-experts (MoE) models. Since MoE alters the traditional transformer architecture, we conduct empirical studies to investigate whether similar layer-wise differences exist in MoEs. Our results show that they do not exist in MoE with shared experts; nevertheless, across different MoEs, higher layers exhibit distinct expert activation patterns between factual and non-factual outputs. Building on these, we propose EAACD, an expert-aware adaptive contrast decoding that uses expert differences in MoE’s higher layers to mitigate hallucinations on QA tasks. EAACD splits high-layer experts into a higher-reliability group and several lower-reliability groups based on their confidence and consistency. It contrasts the higher-reliability group’s prediction with each lower-reliability group’s prediction to calibrate the model’s original predictions. To strengthen this contrast, EAACD amplifies hallucinations from lower-reliability experts via attention and masking to provide stronger negative references. EAACD outperforms all baselines on four datasets

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.1825ACL Anthology · ACL 2026

ERCThinker: Fast-Slow Thinking for Emotion Recognition in Conversation

Yumeng Fu, Weitao Huang Harbin Institute of Technology, Junjie Wu Suzhou University, Hao Teng Harbin Institute of Technology, Shouduo Shang, Meishan Zhang Harbin Institute of Technology (Shenzhen), China, Bingquan Liu Harbin Institute of Technology

原文摘要

Emotion Recognition in Conversation (ERC) aims to identify the emotional states of speakers in conversations. Existing ERC methods perform either fast thinking or slow thinking for emotion predictions. The former lacks interpretability of emotion predictions, and the latter focuses on emotion analysis at shallow semantics. Such insufficient reasoning chains fall short in capturing deep semantics within conversations. To address these limitations, we propose ERCThinker, a Fast-Slow thinking framework for the task of ERC. First, we design different thinking strategies with fine-grained emotion reasoning chains to capture deep semantics that contain topic, discourse structure, speaker characteristic, scene, and emotion shift. Second, we develop an adaptive thinking mechanism in both strategy-level and utterance-level, guiding the model to dynamically perform thinking switching across various scenarios. Furthermore, we utilize Agent-as-Judge to score reasoning chains as reward signals for more accurate emotion predictions. To support training, we construct EmotionCueCoT, the emotion reasoning dataset with supervision in both explanation and judgment. Extensive experiments on various ERC benchmark datasets demonstrate that ERCThinker achieves state-of-the-art performance in both explanation and judgment, making progress in the realm of ERC.

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.1826ACL Anthology · ACL 2026

REaR : Retrieve, Expand and Refine for Effective Multitable Retrieval

Rishita Agarwal, Himanshu Singhal Indian Institute of Technology, Guwahati, Peter Baile Chen Massachusetts Institute of Technology, Manan Roy Choudhury Arizona State University, Dan Roth, Vivek Gupta Arizona State University

原文摘要

Answering natural language queries over relational data often requires retrieving and reasoning over multiple tables, yet most retrievers optimize only for query–table relevance and ignore table–table compatibility. We introduce REaR (Retrieve, Expand and Refine), a three-stage, LLM-free framework that separates semantic relevance from structural joinability for efficient, high-fidelity multi-table retrieval. REaR (i) retrieves query-aligned tables, (ii) expands these with structurally joinable tables via fast, precomputed column-embedding comparisons, and (iii) refines them by pruning noisy or weakly related candidates. Empirically, REaR is retriever-agnostic and consistently improves dense/ sparse retrievers on complex table QA datasets (BIRD, MMQA, and Spider) by improving both multi-table retrieval quality and downstream SQL execution. Despite being LLM-free, it delivers performance competitive with state-of-the-art LLM-augmented retrieval systems (e.g., ARM) while achieving much lower latency and cost. Ablations confirm complementary gains from expansion and refinement, underscoring REaR as a practical, scalable building block for table-based downstream tasks (e.g., Text-to-SQL).

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.1827ACL Anthology · ACL 2026

Figure It Out: Improve the Frontier of Reasoning with Executable Visual States

Meiqi Chen, Fandong Meng WeChat AI, Tencent Inc., Jie Zhou

原文摘要

Complex reasoning problems often involve implicit spatial and geometric relationships that are not explicitly encoded in text. While recent reasoning models perform well across many domains, purely text-based reasoning struggles to capture structural constraints in complex settings. In this paper, we introduce FIGR, which integrates executable visual construction into multi-turn reasoning via end-to-end reinforcement learning. Rather than relying solely on textual chains of thought, FIGR externalizes intermediate hypotheses by generating executable code that constructs diagrams within the reasoning loop. An adaptive reward mechanism selectively regulates when visual construction is invoked, enabling more consistent reasoning over latent global properties that are difficult to infer from text alone. Experiments on seven challenging mathematical benchmarks demonstrate that FIGR outperforms strong text-only chain-of-thought baselines, improving the base model by 13.12% on AIME 2025 and 11.00% on BeyondAIME. These results highlight the effectiveness of precise, controllable figure construction of FIGR in enhancing complex reasoning ability.

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.1828ACL Anthology · ACL 2026

MARCH: Multi-Agent Reinforced Check for Hallucination

Zhuo Li The Chinese University of Hong Kong, Shenzhen, Yupeng Zhang Alibaba Group, Pengyu Cheng Alibaba Group, Jiajun Song Beijing Institute for General Artificial Intelligence, Mengyu Zhou Alibaba Group, Hao Li, Shujie Hu, Yu Qin 等 11 位作者

原文摘要

Hallucination remains a critical bottleneck for large language models (LLMs), undermining their reliability in real-world applications, especially in Retrieval-Augmented Generation (RAG) systems. While existing hallucination detection methods employ LLM-as-a-judge to verify LLM outputs against retrieved evidence, they suffer from inherent confirmation bias, where the verifier inadvertently reproduces the errors of the original generation. To address this, we introduce Multi-Agent Reinforced self-Check for Hallucination (MARCH), a framework that enforces rigorous factual alignment by leveraging deliberate information asymmetry. MARCH orchestrates a collaborative pipeline of three specialized agents: a Solver, a Proposer, and a Checker. The Solver generates an initial RAG response, which the Proposer decomposes into claim-level verifiable atomic propositions. Crucially, the Checker validates these propositions against retrieved evidence in isolation, deprived of the Solver’s original output. This well-crafted information asymmetry scheme breaks the cycle of self-confirmation bias. By training this pipeline with multi-agent reinforcement learning (MARL), we enable the agents to co-evolve and optimize factual adherence. Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucination rates. Notably, an 8B-parameter LLM equipped with MARCH achieves performance competitive with powerful closed-source models. MARCH paves a scalable path for factual self-improvement of LLMs through co-evolution. The code is at https://github.com/Qwen-Applications/MARCH.

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.1829ACL Anthology · ACL 2026

A Survey of Deep Learning for Geometry Problem Solving

Jianzhe Ma Renmin University of China, Wenxuan Wang Renmin University of China, Qin Jin Renmin University of China

原文摘要

Geometry problem solving, a crucial aspect of mathematical reasoning, is vital across various domains, including education, the assessment of AI’s mathematical abilities, and multimodal capability evaluation. The recent surge in deep learning technologies, particularly the emergence of multimodal large language models, has significantly accelerated research in this area. This paper presents a survey of the applications of deep learning in geometry problem solving, including (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; (iii) a detailed analysis of evaluation metrics and methods; and (iv) a critical discussion of state-of-the-art performance, existing challenges, and promising future directions. Our objective is to offer a comprehensive and practical reference of deep learning for geometry problem solving, thereby fostering further advancements in this field. We maintain a list of relevant papers: https://github.com/majianz/dl4gps.

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