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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.835ACL Anthology · ACL 2026

Semantic-Aware Logical Reasoning via a Semiotic Framework

Yunyao Zhang, Xinglang Zhang, Junxi Sheng, Wenbing Li, Junqing Yu Huazhong University of Science and Technology, Yi-Ping Phoebe Chen La Trobe University, Wei Yang Huazhong University of Science and Technology, Zikai Song

原文摘要

Logical reasoning is a fundamental capability of large language models (LLMs). However, existing studies largely overlook the interplay between logical complexity and semantic complexity, limiting their robustness under abstract propositions, ambiguous contexts, and conflicting stances, which are central to human reasoning. We propose LogicAgent, a semiotic-square–guided framework that jointly addresses these two axes of difficulty. The semiotic square provides a principled structure for multi-perspective semantic analysis, and LogicAgent integrates automated deduction with reflective verification to manage logical complexity across deeper reasoning chains. To evaluate reasoning under coupled semantic and logical complexity, we introduce RepublicQA, a benchmark that contains abstract propositions with systematically constructed contrary and contradictory forms, providing a semantically rich setting for assessing logical reasoning in LLMs. Experiments show that LogicAgent achieves state-of-the-art performance on RepublicQA with a 6.25% average gain, and generalizes well to four mainstream logical reasoning benchmarks with an additional 7.05% improvement, highlighting the effectiveness of our semiotic-grounded multi-perspective reasoning in boosting LLMs’ logical 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.836ACL Anthology · ACL 2026

Calibration-Aware Policy Optimization for Reasoning LLMs

Ziqi Wang Institute of Automation, Chinese Academy of Sciences, Xingzhou Lou Institute of automation, Chinese academy of science, Chinese Academy of Sciences and University of the Chinese Academy of Sciences, Meiqi Wu, Zhengqi Wen Tsinghua University, Junge Zhang

原文摘要

Group Relative Policy Optimization (GRPO) enhances LLM reasoning but often induces overconfidence, where incorrect responses yield lower perplexity than correct ones, degrading relative calibration as described by the Area Under the Curve (AUC). Existing approaches either yield limited improvements in calibration or sacrifice gains in reasoning accuracy. We first prove that this degradation in GRPO-style algorithms stems from their uncertainty-agnostic advantage estimation, which inevitably misaligns optimization gradients with calibration. This leads to improved accuracy at the expense of degraded calibration. We then propose Calibration-Aware Policy Optimization (CAPO). It adopts a logistic AUC surrogate loss that is theoretically consistent and admits regret bound, enabling uncertainty-aware advantage estimation. By further incorporating a noise masking mechanism, CAPO achieves stable learning dynamics that jointly optimize calibration and accuracy. Experiments on multiple mathematical reasoning benchmarks show that CAPO-1.5B significantly improves calibration by up to 15% while achieving accuracy comparable to or better than GRPO, and further boosts accuracy on downstream inference-time scaling tasks by up to 5%. Moreover, when allowed to abstain under low-confidence conditions, CAPO achieves a Pareto-optimal precision–coverage trade-off, highlighting its practical value for hallucination mitigation.

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.837ACL Anthology · ACL 2026

GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQL

Daojun Chen, Xi Wang University of Sheffield, Shenyuan Ren Beijing Jiaotong University, Qingzhi Ma Huatai Securities, Pengpeng Zhao Soochow University, An Liu Soochow University

原文摘要

While Large Language Models have significantly advanced Text2SQL generation, a critical semantic gap persists: syntactically valid queries can still misinterpret user intent. To mitigate this challenge, we propose GBV-SQL, a multi-agent framework that introduces Guided Generation with SQL2Text Back-translation Validation. In particular, a dedicated validator translates generated SQL back into natural language and checks whether its logic is aligned with the original question. Beyond the method itself, we also conduct a systematic audit of benchmark quality and introduce a typology of “Gold Errors” in Text2SQL datasets. Our analysis shows that benchmark issues can coexist with strong execution accuracy and can substantially affect evaluation outcomes. On the challenging BIRD benchmark, GBV-SQL achieves 63.23% execution accuracy, a 5.8% absolute improvement over the Deepseek-v3-based MAC-SQL setting. Under manually audited benchmark corrections, GBV-SQL reaches 96.5% (dev) and 97.6% (test) on Spider, and 90.42% on repaired BIRD dev, providing a diagnostic view of model behavior under improved gold quality. Our work contributes both a practical framework for semantic validation and an empirical analysis of benchmark integrity in Text2SQL evaluation.

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.838ACL Anthology · ACL 2026

Branch-and-Browse: Efficient and Controllable Web Exploration with Tree-Structured Reasoning and Action Memory

Shiqi He University of Michigan - Ann Arbor, Yue Cui Alibaba Group, Xinyu Ma, Yaliang Li Alibaba Group, Bolin Ding Alibaba Group, Mosharaf Chowdhury

原文摘要

Autonomous web agents powered by large language models (LLMs) show strong potential for performing goal-oriented tasks such as information retrieval, report generation, and online transactions. These agents mark a key step toward practical embodied reasoning in open web environments. However, existing approaches remain limited in reasoning depth and efficiency: vanilla linear methods fail at multi-step reasoning and lack effective backtracking, while other search strategies are coarse-grained and computationally costly. We introduce Branch-and-Browse, a fine-grained web agent framework that unifies structured reasoning-acting, contextual memory, and efficient execution. It (i) employs explicit subtask management with tree-structured exploration for controllable multi-branch reasoning, (ii) bootstraps exploration through efficient web state replay with background reasoning, and (iii) leverages a page action memory to share explored actions within and across sessions. On the WebArena benchmark, Branch-and-Browse achieves a task success rate of 35.8% and reduces execution time by up to 40.4% relative to state-of-the-art methods. These results demonstrate that Branch-and-Browse is a reliable and efficient framework for LLM-based web agents. Code is available at https://anonymous.4open.science/r/Branch_and_Browse/.

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.839ACL Anthology · ACL 2026

Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge

Yebo Wu, Jingguang Li, Chunlin Tian, KaHou Tam, Zhijiang Guo The Hong Kong University of Science and Technology (Guangzhou), Li Li University of Macau

原文摘要

Federated fine-tuning enables privacy-preserving LLM adaptation but faces a critical bottleneck: the disparity between LLMs’ high memory demands and edge devices’ limited capacity. To break the memory barrier, we propose Chain Federated Fine-tuning (ChainFed), an innovative paradigm that forgoes end-to-end updates in favor of a sequential, layer-by-layer manner. It first trains the initial adapter to convergence, freezes its weights, and then proceeds to the next. This iterative train-and-freeze process forms an optimization chain, gradually enhancing the model’s task-specific proficiency. ChainFed further integrates three core techniques: 1) Dynamic Layer Co-Tuning to bridge semantic gaps between sequentially tuned layers and facilitate information flow; 2) Globally Perceptive Optimization to endow each adapter with foresight beyond its local objective; 3) Function-Oriented Adaptive Tuning to automatically identify the optimal fine-tuning starting point. Extensive experiments on multiple benchmarks demonstrate the superiority of ChainFed over existing methods, boosting average accuracy by up to 46.46%.

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.84ACL Anthology · ACL 2026

Using Perspectival Words Is Harder Than Vocabulary Words for Humans —and Even More So for Multimodal Language Models

Dota Tianai Dong Max Planck Institute for Psycholinguistics, Yifan Luo, Po-Ya Angela Wang National Taiwan University, Asli Ozyurek mpi for psycholinguistics, Paula Rubio-Fernandez The Max Planck Institute for Psycholinguistics

原文摘要

Multimodal language models (MLMs) increasingly demonstrate human-like communication, yet their use of everyday perspectival words remains poorly understood. To address this gap, we compare humans and MLMs in their use of three word types, which we predict impose increasing cognitive demands: vocabulary (e.g., ’boat’ or ’cup’), possessives (e.g., ’mine’ vs. ’yours’), and demonstratives (e.g., ’this one’ vs. ’that one’). Testing seven MLMs against human participants, we find that perspectival words are harder than vocabulary words for both groups. The gap is even larger for MLMs: while models approach human-level performance on using vocabulary, they exhibit clear deficits with possessives and even greater difficulties with demonstratives. Ablation analyses point to limitations in perspective-taking and spatial reasoning as key sources of these gaps in MLMs. Instruction-based prompting helps close the gap for possessives but still leaves demonstratives far below human performance. These results show that, unlike vocabulary, perspectival words pose a greater challenge in human communication—and this difficulty is further amplified in MLMs, revealing a crucial shortfall in their pragmatic and social-cognitive abilities.

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.840ACL Anthology · ACL 2026

TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models

Lin Mu Anhui University, Haiyang Wang, Li Ni Anhui University, Lei Sang Anhui University, Zhize Wu, Peiquan Jin University of Science and Technology of China, Yiwen Zhang Anhui University

原文摘要

Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of Large Language Models (LLMs), and recent Mixture-of-Experts (MoE) extensions further enhance flexibility by dynamically combining multiple LoRA experts. However, existing MoE-augmented LoRA methods assume that experts operate independently, often leading to unstable routing, expert dominance. In this paper, we propose TalkLoRA, a communication-aware MoELoRA framework that relaxes this independence assumption by introducing expert-level communication prior to routing. TalkLoRA equips low-rank experts with a lightweight Talking Module that enables controlled information exchange across expert subspaces, producing a more robust global signal for routing. Theoretically, we show that expert communication smooths routing dynamics by mitigating perturbation amplification while strictly generalizing existing MoELoRA architectures. Empirically, TalkLoRA consistently outperforms vanilla LoRA and MoELoRA across diverse language understanding and generation tasks, achieving higher parameter efficiency and more balanced expert routing under comparable parameter budgets. These results highlight structured expert communication as a principled and effective enhancement for MoE-based parameter-efficient adaptation.

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.841ACL Anthology · ACL 2026

TARE: Lightweight Token-Aware Representation Editing for Fine-tuning Transformer-like Models

Yulong Wang Beijing University of Posts and Telecommunications, Siyu Zhao

原文摘要

Parameter-efficient fine-tuning (PEFT) must balance effectiveness and efficiency: low-rank methods can be costly, while global representation edits often underfit token-level contexts. We propose Token-Aware Representation Editing (TARE), a PEFT method that performs fine-grained, token-specific edits with a small additional inference overhead and minimal tuning.After each FFN block in a transformer-like model, we adopt a lightweight selector that scores a small pool of hidden representation editors for each token, activates only the top-k editors, and mixes their element-wise scaling/bias updates. This design achieves superior performance while maintaining computational efficiency, yielding a more favorable Pareto frontier compared to state-of-the-art (SOTA) methods.Across LLaMA-3-8B (eight knowledge reasoning and seven mathematical reasoning tasks) and RoBERTa-base/large (GLUE), TARE outperforms SOTAs (LoRA, DoRA, MiLoRA, LoReFT, and RED), achieving 86.7% (knowledge reasoning), 76.7% (mathematical reasoning), and 88.3% (GLUE) while tuning only 0.0392% of parameters using about 20 GiB peak GPU memory during training.An implementation is available at: https://github.com/PatriciaPulec/tare.

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.842ACL Anthology · ACL 2026

Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual Learning

Yitong Wang China Mobile Jiutian Artificial Intelligence Technology (Beijing) Co., Ltd., Xue Han, WenChun Gao, Qian Hu China Mobile Research Institute, Jiahui Wang China Mobile Jiutian AI Technology (Beijing) Co., Ltd., Ziqing Wang, Lijun Mei China Mobile Communications Company Limited Research Institute, Junlan Feng

原文摘要

When large language models are used in real-world scenarios, continual learning (CL) becomes a non-trivial problem. In particular, continual learning with modern LLMs is challenged both by the substantial computational costs induced by their massive parameter scale, and by the limitations of current CL methods, which are mainly designed to mitigate catastrophic forgetting while neglecting knowledge sharing across tasks. We further observe that models with stronger performance exhibit stronger inter-task connections. In light of the above challenges and findings, we propose Attribution Scores-based Soft Orthogonality Low-Rank Adaptation (ASO-LoRA), an effective and efficient framework that simultaneously facilitates knowledge transfer while mitigating catastrophic forgetting. Specifically, ASO-LoRA initially assigns task-specific parameter subspaces for new tasks utilizing multi-LoRA modules, enabling for efficient training and inference without relying on task labels. Then, ASO-LoRA leverages attribution scores to evaluate task similarity and employs soft orthogonality between task-specific subspaces, guiding gradient updates in directions that promote parameter isolation, achieving a balance between knowledge transfer and preservation. Experiments are carried out on both the T5-large and the LLaMA2-7B, showing ASO-LoRA’s superior performance and suitability as a plug-in CL solution for general Transformer-based LLMs. Code is available at https://github.com/736619821/ASO-LORA.

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.843ACL Anthology · ACL 2026

ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering

Rachneet Kaur J.P. Morgan Chase, Nishan Srishankar J.P. Morgan Chase, Fidelity AI COE, Worcester Polytechnic Institute, Honda Research Institute and NASA/SETI Institute, Zhen Zeng, Sumitra Ganesh J.P. Morgan Chase

原文摘要

Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts—those requiring precise visual interpretation rather than relying on textual shortcuts. To address this, we introduce ChartAgent, a novel agentic framework that explicitly performs visual reasoning directly within the chart’s spatial domain. Unlike textual chain-of-thought reasoning, ChartAgent iteratively decomposes queries into visual subtasks and actively manipulates and interacts with chart images through specialized actions such as drawing annotations, cropping regions (e.g., segmenting pie slices, isolating bars), and localizing axes, using a library of chart-specific vision tools to fulfill each subtask. This iterative reasoning process closely mirrors human cognitive strategies for chart comprehension. ChartAgent achieves state-of-the-art accuracy on the ChartBench and ChartX benchmarks, surpassing prior methods by up to 16.07% absolute gain overall and 17.31% on unannotated, numerically intensive queries. Furthermore, our analyses show that ChartAgent is (a) effective across diverse chart types, (b) achieves the highest scores across varying visual and reasoning complexity levels, and (c) serves as a plug-and-play framework that boosts performance across diverse underlying LLMs. Our work is among the first to demonstrate visually grounded reasoning for chart understanding using tool-augmented multimodal agents.

cs.CL · cs.AI · 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.844ACL Anthology · ACL 2026

Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards

Youliang Yuan The Chinese University of Hong Kong-Shenzhen, Qiuyang Mang, Jingbang Chen The Chinese University of Hong Kong, Hong Wan, Xiaoyuan Liu Xiaohongshu and The Chinese University of Hong Kong, Shenzhen, Junjielong Xu Tencent and The Chinese University of Hong Kong, Shenzhen, Jen-tse Huang, Wenxuan Wang Renmin University of China 等 10 位作者

原文摘要

In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model’s reasoning ability. This is evidenced by a high incidence of “false positives”—solutions that reach the correct answer through an unsound process.Through a systematic analysis with human verification, we establish a taxonomy of these failure modes, identifying patterns like Miracle Steps—abrupt jumps to a correct output without a valid preceding derivation. Probing experiments suggest that these Miracle Steps are linked to answer-recall shortcuts, including memorization from pretraining, where the model accesses the correct answer independently of its reasoning chain.To mitigate this systemic issue, we introduce the Rubric Reward Model (RRM), a process-oriented reward function that evaluates the entire reasoning trajectory against problem-specific rubrics.The RRM explicitly penalizes logical flaws and encourages rigorous deduction.When integrated into an RL pipeline, RRM-based training consistently outperforms outcome-only supervision across four math benchmarks.Notably, it boosts Verified Pass@1024 on AIME2024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%.Our work demonstrates that rewarding the solution process is crucial for building accurate and reliable 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.845ACL Anthology · ACL 2026

Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search

Sangwon Ryu Pohang University of Science and Technology, Heejin Do ETHZ - ETH Zurich, Yunsu Kim Lilt, Gary Lee, Jungseul Ok POSTECH

原文摘要

Controllable summarization moves beyond generic outputs toward human-aligned summaries guided by specified attributes. In practice, the interdependence among attributes makes it challenging for language models to satisfy correlated constraints consistently. Moreover, previous approaches often require per-attribute fine-tuning, limiting flexibility across diverse summary attributes. In this paper, we propose adaptive planning for multi-attribute controllable summarization (PACO), a training-free framework that reframes the task as planning the order of sequential attribute control with a customized Monte Carlo Tree Search (MCTS). In PACO, nodes represent summaries, and actions correspond to single-attribute adjustments, enabling progressive refinement of only the attributes requiring further control. This strategy adaptively discovers optimal control orders, ultimately producing summaries that effectively meet all constraints. Extensive experiments across diverse domains and models demonstrate that PACO achieves robust multi-attribute controllability, surpassing both LLM-based self-planning models and fine-tuned baselines. Remarkably, PACO with Llama-3.2-1B rivals the controllability of the much larger Llama-3.3-70B baselines. With larger models, PACO achieves superior control performance, outperforming all competitors.

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.846ACL Anthology · ACL 2026

CPT-Agent: A Cognitive Process Theory-driven Framework for Student Simulation in Writing Development

Yuhan Chen Capital Normal University, Zizhuo Shen Beijing Language and Culture University and IFLYTEK CO.LTD., Miaomiao Cheng, Xu Han, Jiefu Gong IFLYTEK CO.LTD., Shijin Wang State Key Laboratory of Cognitive Intelligence, Wei Song Capital Normal University

原文摘要

Simulating student writing behavior offers a promising pathway to scalable feedback evaluation and teacher training. However, existing LLM-based approaches tend to model overly capable learners who readily understand and over-apply feedback, resulting in pedagogically implausible behavior. In this work, we introduce pedagogical realism as a guiding principle for student writing simulation, emphasizing bounded cognition, selective feedback comprehension, and developmentally plausible learning processes. To operationalize this idea, we propose CPT-Agent, a cognitively grounded framework that decouples cognitive ability from writing proficiency and models their interaction during writing and revision. CPT-Agent combines probabilistic modeling of cognitive development, proficiency-controlled text generation, and structured memory for skill accumulation. Experiments show that it (1) produces clearly distinguishable proficiency levels, (2) generates cognitively plausible revisions consistent with instructional theories, and (3) achieves strong agreement with expert judgments in evaluating feedback quality. These results highlight the importance of modeling cognitive constraints in LLM-based student simulation and demonstrate the potential of pedagogically realistic agents for automated feedback assessment and teacher development.

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.847ACL Anthology · ACL 2026

Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs

Wu Li Harbin Institute of Technology, Yigeng Zhou, Zesheng Shi Harbin Institute of Technology, Yequan Wang Peking University and Beijing Academy of Artificial Intelligence, Min Zhang Harbin Institute of Technology, Shenzhen and Harbin Institute of Technology, Jing Li Harbin Institute of Technology

原文摘要

While recent self-training approaches have reduced reliance on human-labeled data for aligning LLMs, they still face critical limitations: (i) sensitivity to synthetic data quality, leading to instability and bias amplification in iterative training; (ii) ineffective optimization due to a diminishing gap between positive and negative responses over successive training iterations. In this paper, we propose Team-based self-Play with dual Adaptive Weighting (TPAW), a novel self-play algorithm designed to improve alignment in a fully self-supervised setting. TPAW adopts a team-based framework in which the current policy model both collaborates with and competes against historical checkpoints, promoting more stable and efficient optimization. To further enhance learning, we design two adaptive weighting mechanisms: (i) a response reweighting scheme that adjusts the importance of target responses, and (ii) a player weighting strategy that dynamically modulates each team member’s contribution during training. Initialized from a SFT model, TPAW iteratively refines alignment without requiring additional human supervision. Experimental results demonstrate that TPAW consistently outperforms existing baselines across various base models and LLM benchmarks.

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.848ACL Anthology · ACL 2026

SearchGym: Bootstrapping Real-World Search Agents via Cost-Effective and High-Fidelity Environment Simulation

Xichen Zhang Hong Kong University of Science and Technology, Ziyi He, Yinghao Zhu The University of Hong Kong, Sitong Wu The Chinese University of Hong Kong, Shaozuo Yu, Meng Chu, Wenhu Zhang, Haoru Tan 等 9 位作者

原文摘要

Search agents have emerged as a pivotal paradigm for solving open-ended, knowledge-intensive reasoning tasks. However, training these agents via Reinforcement Learning (RL) faces a critical dilemma: interacting with live commercial Web APIs is prohibitively expensive, while relying on static data snapshots often introduces noise due to data misalignment. This misalignment generates corrupted reward signals that destabilize training by penalizing correct reasoning or rewarding hallucination. To address this, we propose SearchGym, a simulation environment designed to bootstrap robust search agents. SearchGym employs a rigorous generative pipeline to construct a verifiable knowledge graph and an aligned document corpus, ensuring that every reasoning task is factually grounded and strictly solvable. Building on this controllable environment, we introduce SearchGym-RL, a curriculum learning methodology that progressively optimizes agent policies through purified feedback, evolving from basic interactions to complex, long-horizon planning. Extensive experiments across the Llama and Qwen families demonstrate strong Sim-to-Real generalization. Notably, our Qwen2.5-7B-Base model trained within SearchGym surpasses the web-enhanced ASearcher baseline across nine diverse benchmarks by an average relative margin of 10.6%. Our results validate that high-fidelity simulation serves as a scalable and highly cost-effective methodology for developing capable search agents.

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.849ACL Anthology · ACL 2026

Learning to Select: Query-Aware Adaptive Dimension Selection for Dense Retrieval

Zhanyu Wu, Richong Zhang, Zhijie Nie Alibaba Group and Beihang University

原文摘要

Dense retrieval represents queries and documents as high-dimensional embeddings, but these representations can be redundant at the query level: for a given information need, only a subset of dimensions is consistently helpful for ranking. Prior work addresses this via pseudo-relevance feedback (PRF) based dimension importance estimation, which can produce query-aware masks without labeled data but often relies on noisy pseudo signals and heuristic test-time procedures. In contrast, supervised adapter methods leverage relevance labels to improve embedding quality, yet they learn global transformations shared across queries and do not explicitly model query-aware dimension importance. We propose a Query-Aware Adaptive Dimension Selection framework that learns to predict per-dimension importance directly from query embedding. We first construct oracle dimension importance distributions over embedding dimensions using supervised relevance labels, and then train a predictor to map a query embedding to these label-distilled importance scores. At inference, the predictor selects a query-aware subset of dimensions for similarity computation based solely on the query embedding, without pseudo-relevance feedback. Experiments across multiple dense retrievers and benchmarks show that our learned dimension selector improves retrieval effectiveness over the full-dimensional baseline as well as PRF-based masking and supervised adapter 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.85ACL Anthology · ACL 2026

CHIMERA: A Knowledge Base of Scientific Idea Recombinations for Research Analysis and Ideation

Noy Sternlicht Hebrew University of Jerusalem, Tom Hope Hebrew University, Hebrew University of Jerusalem and Allen Institute for Artificial Intelligence

原文摘要

A hallmark of human innovation is recombination—the creation of novel ideas by integrating elements from existing concepts and mechanisms. In this work, we introduce CHIMERA, the first large-scale Knowledge Base (KB) of recombination examples automatically mined from the scientific literature. CHIMERA enables empirical analysis of how scientists recombine concepts and draw inspiration from different areas, and enables training models that propose cross-disciplinary research directions. To construct this KB, we define a new information extraction task: identifying recombination instances in papers. We curate an expert-annotated dataset and use it to fine-tune an LLM-based extraction model, which we apply to a broad corpus of AI papers. We also demonstrate generalization to a biological domain. We showcase the utility of CHIMERA through two applications. First, we analyze patterns of recombination across AI subfields. Second, we train a scientific hypothesis generation model using the KB, showing that it can propose directions that researchers rate as inspiring.

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.850ACL Anthology · ACL 2026

MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences

Zizhen Li Nankai University, Chuanhao Li, Yibin Wang, Jianwen Sun Nankai University, Yukang Feng, Jiaxin Ai Wuhan University, Fanrui Zhang, Mingzhu Sun Nankai University 等 10 位作者

原文摘要

Recent advancements have expanded the role of Large Language Models (LLMs) in board games from playing agents to creative co-designers. However, a critical gap remains: current systems lack the capacity to offer constructive critique grounded in the emergent user experience. Bridging this gap is fundamental for harmonizing Human-AI collaboration, as it empowers designers to refine their creations via external perspectives while steering models away from biased or unpredictable outcomes. Automating this evaluation presents two challenges: inferring the latent dynamics connecting static rules to gameplay without an explicit engine, and modeling the subjective heterogeneity of diverse player groups. To address these, we curate a comprehensive dataset of 1,727 structurally corrected rulebooks and 150K reviews selected via rigorous quality scoring and facet-aware sampling. We augment this data with Mechanics-Dynamics-Aesthetics (MDA) reasoning to explicitly bridge the causal gap between written rules and player experience. We further distill distinct player personas and introduce MeepleLM, a specialized model that internalizes persona-specific reasoning patterns to accurately simulate the subjective feedback of diverse player archetypes. Extensive experiments demonstrate that MeepleLM significantly outperforms latest commercial models (e.g., GPT-5.1, Gemini3-Pro) in community alignment and critique quality, achieving a 70% preference rate in user studies assessing practical utility. MeepleLM serves as a reliable virtual playtester that provides experience-grounded feedback, offering a practical step towards audience-aligned Human-AI collaboration.

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.851ACL Anthology · ACL 2026

MSMO-ABSA: Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis

Chengyan Wu, Bolei Ma, Ningyuan Deng The Hong Kong University of Science and Technology, He Yanqing Institute of Scientific and Technical Information of China, Yun Xue, Liu Xiaoyong

原文摘要

Aspect-based sentiment analysis (ABSA) garnered growing research interest in multilingual contexts in the past. However, the majority of the studies lack more robust feature alignment and finer aspect-level alignment. In this paper, we propose a novel framework, MSMO: Multi-Scale and Multi-Objective optimization for cross-lingual ABSA. During multi-scale alignment, we achieve cross-lingual sentence-level and aspect-level alignment, aligning features of aspect terms in different contextual environments. Specifically, we introduce code-switched bilingual sentences into the language discriminator and consistency training modules to enhance the model’s robustness. During multi-objective optimization, we design two optimization objectives: supervised training and consistency training, aiming to enhance cross-lingual semantic alignment. To further improve model performance, we incorporate distilled knowledge of the target language into the model. Results show that MSMO significantly enhances cross-lingual ABSA by achieving state-of-the-art performance across multiple languages and 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.852ACL Anthology · ACL 2026

FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models

Zixuan Weng Pennsylvania State University, Jinghuai Zhang University of California, Los Angeles, Kunlin Cai University of California, Los Angeles, Ying Li, Peiran Wang, Yuan Tian University of California, Los Angeles

原文摘要

Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations. Although inference-time steering offers a cost-effective way to adjust model behavior without updating its parameters, existing methods often fail to be simultaneously effective, utility-preserving, and training-efficient due to their rigid, one-size-fits-all designs and limited adaptability. In this work, we present FineSteer, a novel steering framework that decomposes inference-time steering into two complementary stages—conditional steering and fine-grained vector synthesis—allowing fine-grained control over when and how to steer internal representations. In the first stage, we introduce a Subspace-guided Conditional Steering (SCS) mechanism that preserves model utility by avoiding unnecessary steering. In the second stage, we propose a Mixture-of-Steering-Experts (MoSE) mechanism that captures the multimodal nature of desired steering behaviors and generates query-specific steering vectors for improved effectiveness. Through tailored designs in both SCS and MoSE, FineSteer maintains robust performance on general queries while adaptively optimizing steering vectors for targeted inputs in a training-efficient manner. Extensive experiments on safety and truthfulness benchmarks show that FineSteer outperforms the state-of-the-art methods in overall performance (e.g., a 7.6% improvement on TruthfulQA over Llama-3), achieving stronger steering performance with minimal utility loss. The code is available at https://github.com/YukinoAsuna/FineSteer

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.853ACL Anthology · ACL 2026

Is the Attention Matrix Really the Key to Self-Attention in Multivariate Long-Term Time Series Forecasting?

Xinyu Li, Kexi Chen, Jiajie Shen Fudan University, Ying Zheng, Hong Lu Fudan University, Jin Zhao Fudan University, Xin Wang Fudan University

原文摘要

In multivariate long-term time series forecasting, it is widely believed that the effectiveness of self-attention arises from its attention matrix. We challenge this assumption with a counterintuitive finding: our experiments, conducted on three classic and three latest Transformer models, show that dot-product attention can be replaced by element-wise operations without token interaction, such as the addition and Hadamard product, while maintaining or even improving accuracy. This leads to our central hypothesis: the effectiveness of self-attention in this task stems not from the dynamic attention matrix, but from the multi-branch feature extraction inherent in the parallel projections to Query, Key, and Value matrices and their fusion. To validate this, we construct a simple multi-branch MLP that isolates the ‘multi-branch mapping with element-wise operation’ structure from the Transformer and show that it achieves competitive performance. Our results indicate that the source of performance in self-attention has been misattributed, suggesting that the true benefit lies in the architectural principle of multi-branch mapping and fusion, not in the attention matrix.

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.854ACL Anthology · ACL 2026

Difficulty-Controllable Cloze Question Distractor Generation

Seokhoon Kang, Yejin Jeon McGill University and Mila - Quebec Artificial Intelligence Institute, Seonjeong Hwang, Gary Lee

原文摘要

Multiple-choice cloze questions are commonly used to assess linguistic proficiency and comprehension. However, generating high-quality distractors remains challenging, as existing methods often lack adaptability and control over difficulty levels, and the absence of difficulty-annotated datasets further hinders progress. To address these issues, we propose a novel framework for generating distractors with controllable difficulty by leveraging both data augmentation and a multitask learning strategy. First, to create a high-quality, difficulty-annotated dataset, we introduce a two-way distractor generation process to produce diverse and plausible distractors. These candidates are filtered and then categorized by difficulty using an ensemble QA system. Second, this newly created dataset is used to train a difficulty-controllable generation model via multitask learning. Experimental results demonstrate that our method generates high-quality distractors across difficulty levels and substantially outperforms GPT-4o in aligning distractor difficulty with human perception.

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.855ACL Anthology · ACL 2026

ToolPRM: Fine-Grained Inference Scaling of Structured Outputs for Function Calling

Jianghao Lin Shanghai Jiaotong University, Yuanyuan Shi Shanghai Jiaotong University, Xin Peng, Renjie Ding LongShine, Hairui Wang, Yuxuan Peng longshine, Bizhe Bai, Weixi Song Westlake University, Zhejiang University and Shanghai Innovation Institute 等 13 位作者

原文摘要

Large language models (LLMs) excel at function calling, but inference scaling has been explored mainly for unstructured generation. We propose an inference-scaling framework for structured outputs that combines fine-grained beam search with ToolPRM, a process reward model scoring each intra-call decision (function name and argument filling). We build the first fine-grained intra-call supervision dataset via function masking, rollout collection, and step-level annotation. ToolPRM outperforms outcome and coarse-grained reward models in predictive accuracy and yields consistent test-time gains on multiple function-calling benchmarks. We further show that structured generation follows “explore more but retain less”, since early JSON errors are unrecoverable.

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.856ACL Anthology · ACL 2026

GTA: Generating Long-horizon Tasks for Web Agents at Scale

Tenghao Huang, Kung-Hsiang Huang SalesForce.com, Prafulla Kumar Choubey SalesForce.com, Yilun Zhou Massachusetts Institute of Technology, Muhao Chen University of California, Davis, Jonathan May USC/ISI, Chien-Sheng Wu Salesforce AI

原文摘要

Web agents, which couple language models with browsing and tool-use capabilities, show promise as open web assistants. Yet progress is increasingly limited by the lack of scalable, process-level supervision. Existing benchmarks are largely manually constructed, providing only coarse start–goal annotations without intermediate trajectories, while recent automatic generation efforts remain expensive, biased, and shallow. These limitations prevent reliable training and evaluation of agents that must generalize to realistic, multi-hop, cross-page tasks. We introduce a scalable framework GTA that integrates crawling, retrieval-based seeding, in-context generation, and automated quality control to produce realistic tasks paired with executable trajectories. This design decouples crawling from generation for greater efficiency, grounds tasks in the site graph to enforce compositionality, and ensures dense supervision through deterministic replays and systematic validation. We instantiate the pipeline on over 50 websites covering e-commerce, government, forums, and news, with multilingual and multi-hop coverage. The resulting benchmark reveals a significant human–agent performance gap and enables detailed diagnostics. Our contributions are three-fold: (i) formalizing multi-hop web-agent task generation, (ii) proposing an efficient and validated pipeline for automatic data creation, and (iii) releasing a dynamic benchmark with reproducible evaluation.

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.857ACL Anthology · ACL 2026

Closing the Modality Reasoning Gap for Speech Large Language Models

Chaoren Wang, Heng Lu, Xueyao Zhang The Chinese University of Hong Kong, Shenzhen, Shujie Liu, Yan Lu Microsoft Research Asia, Jinyu Li Microsoft, Zhizheng Wu Chinese University of Hong Kong, Shenzhen

原文摘要

Although Speech Large Language Models have achieved notable progress, a substantial modality reasoning gap remains: their reasoning performance on speech inputs is markedly weaker than on text. This gap could be associated with representational drift across Transformer layers and behavior deviations in long-chain reasoning. To address this issue, we introduce TARS, a reinforcement-learning framework that aligns text-conditioned and speech-conditioned trajectories through an asymmetric reward design. The framework employs two dense and complementary signals: representation alignment, which measures layer-wise hidden-state similarity between speech- and text-conditioned trajectories, and behavior alignment, which evaluates semantic consistency between generated outputs and reference text completions. Experiments on challenging reasoning benchmarks, including MMSU and OBQA, show that our approach significantly narrows the modality reasoning gap and achieves state-of-the-art performance among 7B-scale Speech LLMs.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.858ACL Anthology · ACL 2026

Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning

Xinglang Zhang, Yunyao Zhang, ZeLiang Chen, Junqing Yu Huazhong University of Science and Technology, Wei Yang Huazhong University of Science and Technology, Zikai Song

原文摘要

Symbolic logical reasoning is a critical yet underexplored capability of large language models (LLMs), providing reliable and verifiable decision-making in high-stakes domains such as mathematical reasoning and legal judgment. In this study, we present a systematic analysis of logical reasoning under controlled increases in logical complexity, and reveal a previously unrecognized phenomenon, which we term Logical Phase Transitions: rather than degrading smoothly, logical reasoning performance remains stable within a regime but collapses abruptly beyond a critical logical depth, mirroring physical phase transitions such as water freezing beyond a critical temperature threshold. Building on this insight, we propose Neuro-Symbolic Curriculum Tuning, a principled framework that adaptively aligns natural language with logical symbols to establish a shared representation, and reshapes training dynamics around phase-transition boundaries to progressively strengthen reasoning at increasing logical depths. Experiments on five benchmarks show that our approach effectively mitigates logical reasoning collapse at high complexity, yielding average accuracy gains of +1.26 in naive prompting and +3.95 in CoT, while improving generalization to unseen logical compositions.

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.859ACL Anthology · ACL 2026

SpecCache: Speculative KV Cache Reuse for Efficient RAG Serving

Zijian Wen, Tao Zhang University of Science and Technology of China, Shuangwu Chen, Shenghao Ye University of Science and Technology of China, Yu Guo University of Science and Technology of China, Qirui Chen University of Science and Technology of China, Jingxian Shuai, Yunpeng Hou 等 10 位作者

原文摘要

Retrieval-Augmented Generation (RAG) significantly enhances LLMs but faces high prefill latency during long-context processing. While KV cache reuse can mitigate this, current methods relying on shallow features or static heuristics often fail to identify critical tokens for recomputation, resulting in generation quality degradation.We have an insight that KV deviations are more pronounced in deep layers.However, directly extracting deep-layer features from the target model is computationally prohibitive. Crucially, we find that the deep-layer features of a lightweight speculative model exhibit strong consistency with the target model in the selection of critical tokens for recomputation.In light of these insights, we propose SpecCache, which employs deep-layer hidden-state norms from a speculative model as a proxy to guide the critical token selection for target large model.Experiments demonstrate that SpecCache outperforms state-of-the-art (SOTA) baselines. Compared to full KV recomputation, it reduces time-to-first-token (TTFT) by 2.17-3.95\times and increases inference throughput by 2.7-5.2\times, with negligible degradation in generation quality relative to full recomputation.

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.86ACL Anthology · ACL 2026

Towards Robust Real-World Spreadsheet Understanding with Multi-Agent Multi-Format Reasoning

Houxing Ren Beijing University of Aeronautics and Astronautics, Mingjie Zhan, Zimu Lu, Ke Wang The Chinese University of Hong Kong, Yunqiao Yang, Haotian Hou, Hongsheng Li The Chinese University of Hong Kong

原文摘要

Spreadsheets are central to real-world applications such as enterprise reporting, auditing, and scientific data management. Despite their ubiquity, existing large language model based approaches typically treat tables as plain text, overlooking critical layout cues and visual semantics. Moreover, real-world spreadsheets are often massive in scale, exceeding the input length that LLMs can efficiently process. To address these challenges, we propose SpreadsheetAgent, a two-stage multi-agent framework for spreadsheet understanding that adopts a step-by-step reading and reasoning paradigm. Instead of loading the entire spreadsheet at once, SpreadsheetAgent incrementally interprets localized regions through multiple modalities, including code execution results, images, and LaTeX tables. The method first constructs a structural sketch and row/column summaries, and then performs task-driven reasoning over this intermediate representation in the Solving Stage. To further enhance reliability, we design a verification module that validates extracted structures via targeted inspections, reducing error propagation and ensuring trustworthy inputs for downstream reasoning. Extensive experiments on two spreadsheet datasets demonstrate the effectiveness of our approach. With GPT-OSS-120B, SpreadsheetAgent achieves 38.16% on Spreadsheet Bench, outperforming the ChatGPT Agent baseline (35.27%) by 2.89 absolute points. These results highlight the potential of SpreadsheetAgent to advance robust and scalable spreadsheet understanding in real-world applications. Code is available at https://github.com/renhouxing/SpreadsheetAgent.

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.860ACL Anthology · ACL 2026

REG: Retrieval via Emotion Similarity for Guiding Empathetic Dialogue Generation

Xu Wang, Bo Wang, Yang Xiang Peng Cheng Laboratory, Yihong Tang, Dongming Zhao, Yuzifei, Yuexian Hou

原文摘要

Empathy relies on the cognitive capacity to relate to similar past experiences. Consequently, retrieval-based approaches utilize analogous exemplars to guide empathetic dialogue generation. However, existing methods prioritize semantic similarity over emotion characteristics, often leading to unempathetic responses. To address this, we propose REG, a framework that integrates four Emotion Attributes into the retrieval process to ensure explicit emotional alignment. Furthermore, to mitigate the noise and limited diversity caused by coarse-grained sentence-level attributes, we incorporate Token-level Retrieval for finer granularity and a Retrieval Candidate Augmentation strategy to enhance diversity. Empirical results on the EmpatheticDialogues dataset demonstrate that REG significantly outperforms baselines, offering a robust solution for empathetic generation.

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.861ACL Anthology · ACL 2026

TellWhisper: Tell Whisper Who Speaks When

Yifan Hu, Peiji Yang, Zhisheng Wang Tencent, Yicheng Zhong, Rui Liu Inner Mongolia University

原文摘要

Multi-speaker automatic speech recognition (MASR) aims to predict ”who spoke when and what” from multi-speaker speech, a key technology for multi-party dialogue understanding. However, most existing approaches decouple temporal modeling and speaker modeling when addressing ”when” and ”who”: some inject speaker cues before encoding (e.g., speaker masking), which can cause irreversible information loss; others fuse identity by mixing speaker posteriors after encoding, which may entangle acoustic content with speaker identity. This separation is brittle under rapid turn-taking and overlapping speech, often leading to degraded performance. To address these limitations, we propose TellWhisper, a unified framework that jointly models speaker identity and temporal within the speech encoder. Specifically, we design TS\text{-}RoPE, a time-speaker rotary positional encoding: time coordinates are derived from frame indices, while speaker coordinates are derived from speaker activity and pause cues. By applying region-specific rotation angles, the model explicitly captures per-speaker continuity, speaker-turn transitions, and state dynamics, enabling the attention mechanism to simultaneously attend to ”when” and ”who”. Moreover, to estimate frame-level speaker activity, we develop Hyper\text{-}SD, which casts speaker classification in hyperbolic space to enhance inter-class separation and refine speaker-activity estimates. Extensive experiments demonstrate the effectiveness of the proposed approach. The project webpage is available at https://walker-hyf.github.io/TellWhisper.

cs.CL

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