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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.21ACL Anthology · ACL 2026

WebSTAR: Scalable Data Synthesis for Computer Use Agents with Step-Level Filtering

Yifei He University of Illinois Urbana-Champaign, Pranit Chawla Microsoft, Yaser Souri Microsoft, Subhojit Som, Xia Song Microsoft

原文摘要

Computer use agents (CUAs) can operate real-world digital interfaces but remain difficult to train due to the high cost of graphical user interface (GUI) interaction and the scarcity of high-quality trajectory data. Existing datasets rely on human demonstrations, limiting scalability. A natural alternative is to synthesize data from strong CUAs, yet their rollouts are highly noisy, with incorrect or suboptimal actions consisting a large proportion of the steps, making naive imitation ineffective. To tackle this challenge, we introduce a scalable data synthesis pipeline that transforms noisy rollouts into reliable supervision without human annotation. The core idea is step-level filtering, which evaluates actions individually to retain only correct steps, complemented by reasoning augmentation for improved planning. Using this pipeline, we construct WebSTAR, a dataset of 13.3K trajectories and 267K graded, reasoning-rich steps synthesized from OpenAI’s computer-use-preview model. We train Qwen-2.5-VL-Instruct models (7B and 32B) on WebSTAR. On WebVoyager, our 7B model surpasses SoTA open-source CUA model UI-TARS-1.5-7B by more than 15% with only supervised finetuning. Building on step-level grading, we further create WebSCORE, a dataset of graded step-level actions, and train StepRM, a 7B multimodal reward model distilled from o4-mini, which matches its grading quality while being far more efficient to deploy at scale. Our results establish step-level filtering as a key principle for scalable CUA training and construct two new datasets (WebSTAR, WebSCORE) and a lightweight reward model (StepRM) as practical tools to advance robust and efficient CUAs.

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.210ACL Anthology · ACL 2026

Revisiting a Pain in the Neck: A Semantic Reasoning Benchmark for Language Models

Yang Liu Beijing Institute for General Artificial Intelligence, Hongming Li, Melissa Xiaohui Qin, Chao Huang University of Science and Technology Beijing, Qiankun Liu University of Science and Technology Beijing

原文摘要

We present SemanticQA, an evaluation suite designed to assess language models (LMs) in semantic phrase processing tasks. The benchmark consolidates existing multiword expression (MwE) resources and reorganizes them into a unified testbed. It covers both general lexical phenomena, such as lexical collocations, and three fine-grained categories: idiomatic expressions, noun compounds, and verbal constructions. Through SemanticQA, we assess LMs of diverse architectures and scales in extraction, classification, and interpretation tasks, as well as sequential task compositions. We reveal substantial performance variation, particularly on tasks requiring semantic reasoning, highlighting differences in reasoning efficacy and semantic understanding of LMs, providing insights for pushing LMs with stronger comprehension on non-trivial semantic phrases. The evaluation harness and data of SemanticQA are available at https://github.com/jacklanda/SemanticQA.

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.2100ACL Anthology · ACL 2026

SHARP: Self-adaptive Harmful Category-aware Prompt Generation for Black-box Jailbreaking

Yingjie Xue, Xingyou Xia, Jun Zhang National University of Defense Technology, Yunbo Cao Information Center, Academy of Military Science, Dengpan Ye Guangzhou University, Guotong Geng AMS, Fei Li Wuhan University

原文摘要

Large Language Models (LLMs) have been widely applied in various domains such as education and healthcare, making safety assurance crucial. Jailbreak attacks, a method used in red-teaming, can help evaluate and improve the defensive strategies of LLMs. However, existing jailbreak methods often overlook the semantic differences across categories of harmful questions, leading to inconsistent success rates and reduced overall attack effectiveness. We propose the first category-aware jailbreak framework, SHARP, which incorporates the semantic category of harmful questions into prompt generation. Trained on a verified jailbreak dataset, SHARP enables the model to learn category-specific semantic features and adaptively generate prompts that bypass safety mechanisms. The method combines two-stage LoRA fine-tuning, and DPO-based reinforcement learning to optimize both attack success and category alignment. Experiments show that SHARP significantly improves attack success rates and achieves better cross-category robustness compared to the state-of-the-art (SOTA) baselines, providing an efficient and scalable tool for evaluating LLM safety.

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.2101ACL Anthology · ACL 2026

Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responses

Sugyeong Eo Yonsei University, Heuiseok Lim

原文摘要

Although large language models (LLMs) have shown considerable progress in pragmatic language understanding, prior research has focused mainly on their comprehension of verbal behavior. Nonetheless, non-verbal behavior remains a fundamental component of human communication, especially when deliberately utilized in isolation to convey indirect meanings. In this work, we present the first systematic evaluation of LLMs’ ability to infer pragmatic meaning in dialogue consisting solely of non-verbal responses. We explore three research questions: (1) Can LLMs recognize indirect intent conveyed through non-verbal responses? (2) When and how do LLMs fail to capture non-verbal intent? (3) How can we improve LLMs’ ability to interpret non-verbal intent?. Through the evaluation, we observe that LLMs struggle to infer underlying meaning from non-verbal responses, with accuracy dropping by up to 60% points compared to verbal ones. Further extensive analysis reveals a behavioral pattern in LLMs’ interpretations of non-verbal behavior and demonstrates that in-context learning facilitates pragmatic inference.

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.2102ACL Anthology · ACL 2026

Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models

Minghe Shen, Zhuo Zhi, Chonghan Liu, Shuo Xing Google and Texas A&M University - College Station, Zhengzhong Tu Texas A&M University - College Station, Che Liu

原文摘要

Recent studies posit that Reinforcement Learning with Verifiable Rewards (RLVR) primarily amplifies behaviors inherent to the pre-training distribution rather than inducing new capabilities, but these insights are predominantly limited to language-only domains, leaving the dynamics of visual-centric spatial reasoning under-explored. To examine the impact of RLVR on the capability boundaries of Vision-Language Models (VLMs), we introduce Ariadne, a controlled framework based on synthetic maze navigation where the reasoning difficulty is precisely regulated by path length and the number of turns. We demonstrate that applying RLVR extends the spatial reasoning boundary, achieving success on problems where the base policy VLM consistently attains 0% accuracy despite increasing pass@k sampling budgets, indicating that the optimized policy successfully navigates search spaces that were effectively unreachable by the base distribution. Furthermore, despite being trained exclusively on synthetic mazes, we evaluate the model on two real-world navigation benchmarks (MapBench and ReasonMap) in a zero-shot setting. The observed improvements in these out-of-domain tasks suggest genuine spatial reasoning capability expansion rather than mere sampling efficiency. Our code is available at: https://github.com/MingheShen/Ariadne

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.2103ACL Anthology · ACL 2026

Perception, Understanding and Reasoning: A Multimodal Benchmark for Video Fake News Detection

Cui Yakun The Hong Kong University of Science and Technology, Peng Qi Centre for Trusted Internet and Community, National University of Singapore, Fushuo Huo The Hong Kong University of Science and Technology, Hang Du Beijing University of Posts and Telecommunications, Weijie Shi Hong Kong University of Science and Technology, Juntao Dai Peking University, Zhenghao Zhu, Sirui Han The Hong Kong University of Science and Technology

原文摘要

The advent of multi-modal large language models (MLLMs) has greatly advanced research on video fake news detection (VFND) tasks. Existing benchmarks typically focus on the detection accuracy, while failing to provide fine-grained assessments for the entire detection process. To address these limitations, we introduce POVFNDB (Process-oriented Video Fake News Detection Benchmark), a process-oriented benchmark comprising 10 tasks designed to systematically evaluate MLLMs’ perception, understanding, and reasoning capabilities in VFND. This benchmark contains 36,240 human-annotated question-answer (QA) in structured or open-ended formats, spanning 15 distinct evaluation dimensions that characterize different aspects of the video fake news detection process.Using POVFNDB, we conduct comprehensive evaluations on both proprietary and open-source MLLMs. Moreover, We fine-tune Qwen2.5VL-7B-Instruct on a reasoning dataset generated by our proposed POVFND-CoT, a chain-of-thought method that utilizes rationales from evaluation results and rationale validation. The resulting model achieves sota performance on VFND.

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.2104ACL Anthology · ACL 2026

RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection

Yejin Lee Yonsei University, Hyeseon An, Yo-Sub Han Yonsei University

原文摘要

Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in the internet and online anonymity accelerate its rapid spread and complicate its detection. However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each reflecting different linguistic styles and social contexts. Despite this diversity, prior studies on hate speech detection often rely on fixed methodologies without adapting to data-specific features. We introduce RV-HATE, a detection framework designed to account for the dataset-specific characteristics of each hate speech dataset. RV-HATE consists of multiple specialized modules, where each module focuses on distinct linguistic or contextual features of hate speech. The framework employs reinforcement learning to optimize weights that determine the contribution of each module for a given dataset. A voting mechanism then aggregates the module outputs to produce the final decision. RV-HATE offers two primary advantages: (1) it improves detection accuracy by tailoring the detection process to dataset-specific attributes, and (2) it also provides interpretable insights into the distinctive features of each dataset. Consequently, our approach effectively addresses implicit hate speech and achieves superior performance compared to conventional static methods.

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.2105ACL Anthology · ACL 2026

Cross-Prompt Automated Essay Scoring of Multiple Traits: Making Sense of the State of the Art

Shengjie Li Google, Vincent Ng

原文摘要

Despite the recent progress made in cross-prompt essay scoring, there is little analysis of what makes a state-of-the-art cross-prompt scorer work well. To this end, we present an empirical analysis of how the key components of a cross-prompt scorer interact with each other and impact its overall performance. In addition, we examine for the first time the application of transductive learning to cross-prompt scoring, which represents an important starting point for providing a practical way to improve cross-prompt scorers for use in the rarely-studied classroom setting without the need for additional labeled training data.

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.2106ACL Anthology · ACL 2026

JARVIS or Ultron? A Survey on the Safety and Security Threats of Computer-Using Agents

Ada Chen CMU, Carnegie Mellon University, Yongjiang Wu, Junyuan Zhang, Jingyu Xiao The Chinese University of Hong Kong, Shu Yang, Jen-tse Huang, Kun Wang, Wenxuan Wang Renmin University of China 等 9 位作者

原文摘要

Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emulate human-like operations in graphical user interfaces. We are now witnessing the emergence of Computer-Using Agents (CUAs), capable of autonomously performing tasks such as navigating desktop applications, web pages, and mobile apps. However, as these agents grow in capability, they also introduce novel safety and security risks. Vulnerabilities in LLM-driven reasoning, with the added complexity of integrating multiple software components and multimodal inputs, further complicate the security landscape. In this paper, we present a systematization of knowledge on the safety and security threats of CUAs. We conduct a comprehensive literature review and distill our findings along four research objectives: (i) define the CUA that suits safety analysis; (ii) categorize current safety threats among CUAs; (iii) propose a comprehensive taxonomy of existing defensive strategies; (iv) summarize prevailing benchmarks, datasets, and evaluation metrics used to assess the safety and performance of CUAs. Building on these insights, our work provides future researchers with a structured foundation for exploring unexplored vulnerabilities and offers practitioners actionable guidance in designing and deploying secure Computer-Using Agents.

cs.CL · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2107ACL Anthology · ACL 2026

Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL

Le Zhou, Feng Yao National University of Defense Technology, Fengcai Qiao, Bo Xu, Fangyuan Wang Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Boyan Xu

原文摘要

Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought have demonstrated remarkable capabilities in code generation and mathematical reasoning. However, their potential in multi-turn Text-to-SQL tasks remains largely underexplored. Existing approaches typically rely on unstable API-based inference or require expensive fine-tuning on small-scale models. In this work, we present Rose-SQL, a training-free framework that leverages small-scale LRMs through in-context learning to enable accurate context-dependent parsing. We introduce the Role-State, a fine-grained representation that bridges the structural gap between schema linking and SQL generation by serving as a structural blueprint. To handle conversational dependencies, Rose-SQL traces the evolution of Role-State through historical context via structural isomorphism checks, guiding the model to infer the possible SQL composition for the current question through verified interaction trajectories. Experiments on the SParC and CoSQL benchmarks show that, within the Qwen3 series, Rose-SQL outperforms in-context learning baselines at the 4B scale and substantially surpasses state-of-the-art fine-tuned models at the 8B and 14B scales, while showing consistent gains on additional reasoning backbones.

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.2108ACL Anthology · ACL 2026

Why Do More Experts Fail? A Theoretical Analysis of Model Merging

Zijing Wang, Xingle Xu Northeastern University, YongKang Liu Northeast University, Yiqun Zhang Shanghai Artificial Intelligence Laboratory and Northeastern University, Peiqin Lin, Shi Feng Northeastern University, China, Daling Wang, Xiaocui Yang Northeastern University 等 9 位作者

原文摘要

Model merging dramatically reduces storage and computational resources by combining multiple expert models into a single multi-task model. However, existing methods struggle to maintain performance gains as the number of merged models increases. In this paper, we investigate the key obstacles that limit the scalability of model merging. We prove that the limited effective parameter space imposes a strict constraint on the number of models that can be successfully merged. Through Gaussian Width analysis, we show that marginal benefits diminish according to a strictly concave function as more models are merged. Using Approximate Kinematics Theory, we further prove the existence of a unique optimal threshold beyond which additional models yield negligible improvements. To address this limitation, we propose a straightforward Reparameterized Heavy-Tailed method to extend the merged model’s coverage and enhance performance. Empirical results on 19 benchmarks, including both knowledge-intensive and general-purpose tasks, validate our theoretical analysis. We believe that these results spark further research beyond the current scope of model merging.

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.2109ACL Anthology · ACL 2026

Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction

Vipul Kumar Rathore, Malik Hammad Faisal, Parag Singla Indian Institute of Technology, Delhi, Mausam

原文摘要

Distantly Supervised Relation Extraction (DSRE) remains a long-standing challenge in NLP, where models must learn from noisy bag-level annotations while making sentence-level predictions. While existing state-of-the-art (SoTA) DSRE models rely on task-specific training, their integration with in-context learning (ICL) using large language models (LLMs) remains underexplored. A key challenge is that the LLM may not learn relation semantics correctly, due to noisy annotation.In response, we propose HYDRE – HYbrid Distantly Supervised Relation Extraction framework. It first uses a trained DSRE model to identify the top-k candidate relations for a given test sentence, then uses a novel dynamic exemplar retrieval strategy that extracts reliable, sentence-level exemplars from training data, which are then provided in LLM prompt for outputting the final relation(s).We further extend HYDRE to cross-lingual settings for RE in low-resource languages. Using available English DSRE training data, we evaluate all methods on English as well as a newly curated benchmark covering four diverse low-resource Indic languages - Oriya, Santali, Manipuri, and Tulu. HYDRE achieves up to 20 F1 point gains in English and, on average, 17 F1 points on Indic languages over prior SoTA DSRE models and naive prompting baselines. Detailed ablations exhibit HYDRE’s efficacy compared to other prompting strategies.

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.211ACL Anthology · ACL 2026

Timely Machine: Awareness of Time Makes Test-Time Scaling Agentic

Yichuan Ma Fudan University, Linyang Li Shanghai Artificial Intelligence Laboratory, Yongkang Chen, Peiji Li, Xiaozhe Li, Qipeng Guo Shanghai AI Laboratory, Dahua Lin The Chinese University of Hong Kong, Kai Chen Shanghai AI Laboratory

原文摘要

As large language models (LLMs) increasingly tackle complex reasoning tasks, test-time scaling has become critical for enhancing capabilities. However, in agentic scenarios with frequent tool calls, the traditional generation-length-based definition breaks down: tool latency decouples inference time from generation length. We propose Timely Machine, redefining test-time as wall-clock time, where models dynamically adjust strategies based on time budgets. We introduce Timely-Eval, a benchmark spanning high-frequency tool calls, low-frequency tool calls, and time-constrained reasoning. By varying tool latency, we find smaller models excel with fast feedback through more interactions, while larger models dominate high-latency settings via superior interaction quality. Moreover, existing models fail to adapt reasoning to time budgets. We propose Timely-RL to address this gap. After cold-start supervised fine-tuning, we use reinforcement learning to enhance temporal planning. Timely-RL improves time budget awareness and consistently boosts performance across Timely-Eval. We hope our work offers a new perspective on test-time scaling for the agentic era.

cs.CL · cs.AI

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2110ACL Anthology · ACL 2026

Explaining Sources of Uncertainty in Automated Fact-Checking

Jingyi Sun University of Copenhagen, Greta Warren University of Copenhagen, Irina Shklovski University of Copenhagen, Isabelle Augenstein University of Copenhagen

原文摘要

Human–AI collaboration in knowledge-intensive tasks such as fact-checking requires understanding model uncertainty in multi-document reasoning amid conflicting/agreeing evidence. Yet, existing methods only express uncertainty as numbers or hedges without revealing which evidence conflicts cause the uncertainty, leaving users unable to resolve disagreements. We present CLUE (Conflict- Agreement-aware Language-model Uncertainty Explanations), a plug-and-play white-box framework that, to our knowledge, is the first to generate natural-language explanations of model uncertainty grounded in conflicting/agreeing evidence. CLUE (i) identifies span-level claim–evidence and inter-evidence relations that signal conflict or agreement without supervision, and (ii) uses these relations to steer explanation generation, articulating how they drive the model’s uncertainty. Across three language models and two fact-checking datasets, CLUE produces explanations that more faithfully track model uncertainty and better align with the model’s fact-checking decisions than span-agnostic explanation prompting; human raters also judge them more helpful, more informative, less redundant, and more logically consistent with the input. By explicitly tying uncertainty to evidence conflicts and agreements, CLUE supports practical fact-checking and other tasks that require reasoning over complex, conflicting information.

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.2111ACL Anthology · ACL 2026

TRACE: A Corpus of Team Creative Discussions

Yixuan Jiang Alibaba Group, Tiancheng Hu University of Cambridge, Jose Hernandez-Orallo Universitat Politecnica de Valencia and University of Cambridge, David Stillwell University of Cambridge, Luning Sun University of Cambridge

原文摘要

Understanding how discussion dynamics shape team creativity has been limited by the difficulty of measuring process at scale. We introduce Trace, a corpus of 309 group discussions from 103 teams (460 participants) across six creative problem-solving tasks. The dataset follows an input-process-output framework, integrating team composition (demographics, personalities), full discussion transcripts, and creativity outcomes. Using sentence embeddings and factor analysis, we identify four interpretable discussion dimensions: Coherence, Exploration, Convergence, and Participation. Analysis reveals a depth-breadth trade-off: coherent idea development inversely relates to semantic exploration. Larger teams explore more broadly but converge less effectively while team diversity shapes participation patterns more than discussion content. Novelty and usefulness in the creativity outcomes follow distinct pathways: Exploration and Convergence predict novelty, whereas Coherence predicts usefulness. These findings ground our understanding of how teams talk their way to creative solutions and provide guidance for designing multiagent systems.

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.2112ACL Anthology · ACL 2026

Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA

Kyubyung Chae, Jewon Yeom, Jeongjae Park, Seunghyun Bae Seoul National University, Ijun Jang, Hyunbin Jin, Jinkwan Jang, Taesup Kim Seoul National University

原文摘要

Legal QA benchmarks have predominantly focused on case law, overlooking the unique challenges of statute-centric regulatory reasoning. In statutory domains, relevant evidence is distributed across hierarchically linked documents, creating a statutory retrieval gap where conventional retrievers fail and models often hallucinate under incomplete context. We introduce SearchFireSafety, a structure- and safety-aware benchmark for statute-centric legal QA. Instantiated on fire-safety regulations as a representative case, the benchmark evaluates whether models can retrieve hierarchically fragmented evidence and safely abstain when statutory context is insufficient. SearchFireSafety adopts a dual-track evaluation framework combining real-world questions that require citation-aware retrieval and synthetic partial-context scenarios that stress-test hallucination and refusal behavior. Experiments across multiple large language models show that graph-guided retrieval substantially improves performance, but also reveal a critical safety trade-off: domain-adapted models are more likely to hallucinate when key statutory evidence is missing. Our findings highlight the need for benchmarks that jointly evaluate hierarchical retrieval and model safety in statute-centric regulatory settings.

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.2113ACL Anthology · ACL 2026

PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues

Prajwal Vijay Kajare, Priyanshu Priya, Bikash Santra Indian Institute of Technology, Jodhpur, Asif Ekbal Indian Institute of Technology, Jodhpur

原文摘要

Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships. Developing negotiation dialog systems that can recognize and respond strategically to emotions is therefore essential to create more effective human-centered interactions. Beyond generating emotionally appropriate responses, interpretability - understanding how a system generates a particular emotion-aware response, is critical for fostering reliability and building rapport. Driven by these aspects, in this work, we introduce PRISMA, an interpretable emotionally intelligent negotiation dialogue system targeting two application domains, viz. job interviews and resource allocation. To enable interpretability, we propose an Emotion-aware Negotiation Strategy-informed Chain-of-Thought (ENS-CoT) reasoning mechanism, which mimics human negotiation by perceiving, understanding, using, and managing emotions. Leveraging ENS-CoT, we curate two new datasets: JobNego (for job interview negotiation) and ResNego (for resource allocation negotiation). We then leverage these datasets to develop PRISMA by augmenting self-training with Direct Preference Optimization (DPO), guiding agents toward more accurate, interpretable, and emotionally appropriate negotiation responses. Automatic and human evaluations on JobNego and ResNego datasets demonstrate that PRISMA substantially enhances the interpretability and generates appropriate emotion-aware responses, while improving overall negotiation effectiveness.

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.2114ACL Anthology · ACL 2026

SkMTEB: Slovak Massive Text Embedding Benchmark and Model Adaptation

Marek Suppa Comenius University in Bratislava, Andrej Ridzik Kempelen Institute of Intelligent Technologies, Daniel Hládek Technical University in Kosice, Natália Kňažeková Kempelen Institute of Intelligent Technologies and Comenius University in Bratislava, Viktória Ondrejová

原文摘要

We introduce SkMTEB, the first comprehensive MTEB-style text embedding benchmark for Slovak, a low-resource West Slavic language, comprising 31 datasets across 7 task types—nearly 4\times the depth of existing multilingual benchmark coverage for Slovak. Our evaluation of 31 embedding models reveals that large instruction-tuned multilingual models achieve the strongest performance, while existing Slovak-specific models trained for NLU tasks transfer poorly to embedding tasks. To address the need for efficient, locally-deployable Slovak embeddings, we develop e5-sk-small (45M parameters) and e5-sk-large (365M) by applying vocabulary trimming and fine-tuning to Multilingual E5 models. Despite size reductions of up to 62%, our open-source models achieve competitive performance with proprietary APIs while remaining locally deployable for semantic search and retrieval-augmented generation (RAG). We release the benchmark, models, datasets, and code openly, hoping our approach offers a replicable path for other under-resourced languages.

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.2115ACL Anthology · ACL 2026

A Linguistics-Aware LLM Watermarking via Syntactic Predictability

Shinwoo Park, Hyejin Park Rensselaer Polytechnic Institute, Hyeseon An, Yo-Sub Han Yonsei University

原文摘要

As large language models (LLMs) continue to advance rapidly, reliable governance tools have become critical. Publicly verifiable watermarking is particularly essential for fostering a trustworthy AI ecosystem. A central challenge persists: balancing text quality against detection robustness. Recent studies have sought to navigate this trade-off by leveraging signals from model output distributions (e.g., token-level entropy); however, their reliance on these model-specific signals presents a significant barrier to public verification, as the detection process requires access to the logits of the underlying model. We introduce STELA, a novel framework that aligns watermark strength with the linguistic degrees of freedom inherent in language. STELA dynamically modulates the signal using part-of-speech (POS) n-gram–modeled linguistic indeterminacy, weakening it in grammatically constrained contexts to preserve quality and strengthening it in contexts with greater linguistic flexibility to enhance detectability. Our detector operates without access to any model logits, thus facilitating publicly verifiable detection. Through extensive experiments on typologically diverse languages—analytic English, isolating Chinese, and agglutinative Korean—we show that STELA surpasses prior methods in detection robustness. Our code is available at https://github.com/Shinwoo-Park/stela_watermark.

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.2116ACL Anthology · ACL 2026

Selective Contrastive Learning For Gloss Free Sign Language Translation

Chang Hao Lai, Rui Zhao Xiamen University, Xuewen Zhong Xiamen University, Jinsong Su Xiamen University, Yidong Chen Xiamen University

原文摘要

Sign language translation (SLT) converts continuous sign videos into spoken-language text, yet it remains challenging due to the intrinsic modality mismatch between visual signs and written text, particularly in gloss-free settings. Recent SLT systems increasingly adopt CLIP-like Vision-Language pretraining (VLP) for cross-modal alignment, but the random in-batch contrast provides few, batch-dependent negatives and may mislabel semantically similar (or even identical) pairs as negatives, introducing noisy and potentially inconsistent alignment supervision.In this work, we first conduct a preliminary trajectory-based analysis that tracks negative video-text similarity over training. The results show that only a small subset of negatives exhibits the desired behavior of being consistently pushed away, while the remaining negatives display heterogeneous and often non-decreasing similarity dynamics, suggesting that random in-batch negatives are frequently uninformative for effective alignment.Inspired by this, we propose Selective Contrastive Learning for SLT (SCL-SLT) with a Pair Selection (PS) strategy. PS scores candidate negatives using similarity dynamics from reference checkpoints and constructs mini-batches via a curriculum that progressively emphasizes more challenging negatives, thereby strengthening contrastive supervision while reducing the influence of noisy or semantically invalid negatives.

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.2117ACL Anthology · ACL 2026

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models

Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng International Digital Economy Academy, International Digital Economy Academy, Yingce Xia Zhongguancun Academy, Shufang Xie Renmin University of China, Kaili Liu 等 18 位作者

原文摘要

Large language models (LLMs) are increasingly applied to scientific research, yet existing evaluations often fail to reflect the fine-grained capabilities required in practice. Most benchmarks are manually curated or domain-generic, limiting scalability and alignment with real scientific use cases. In this paper, we propose a new framework named SciCustom to address the problem. It enables the custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs. SciCustom first organizes scientific knowledge into ontology-grounded knowledge units with controlled granularity and trains a tagger to map large-scale data instances into this knowledge space. Given a custom requirement, relevant knowledge units are identified via voting-based multi-model consensus. These units enable relevance-aware benchmark retrieval via binary search, followed by proxy subset selection and data-grounded benchmark generation for efficient evaluation. Experiments in chemistry and healthcare demonstrate that SciCustom reveals fine-grained differences in LLM scientific capabilities that standard benchmarks overlook, while requiring neither expert annotation nor synthetic question generation. This work provides a scalable and application-aware foundation for benchmarking scientific capabilities in 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.2118ACL Anthology · ACL 2026

NSF-SciFy: Mining the NSF Awards Database for Scientific Claims

Delip Rao, Weiqiu You University of Pennsylvania, University of Pennsylvania, Eric Wong University of Pennsylvania, Chris Callison-Burch University of Pennsylvania, University of Pennsylvania

原文摘要

We introduce NSF-SciFy, a comprehensive dataset of scientific claims and investigation proposals extracted from National Science Foundation award abstracts. While previous scientific claim verification datasets have been limited in size and scope, NSF-SciFy represents a significant advance with 2.8 million claims from 400,000 abstracts spanning all science and mathematics disciplines. We present two focused subsets: NSF-SciFy-MatSci with 114,000 claims from materials science awards, and NSF-SciFy-20K with 135,000 claims across five NSF directorates. Using zero-shot prompting, we develop a scalable approach for joint extraction of scientific claims and investigation proposals. We demonstrate the dataset’s utility through three downstream tasks: non-technical abstract generation, claim extraction, and investigation proposal extraction. Fine-tuning language models on our dataset yields substantial improvements, with relative gains often exceeding 100%, particularly for claim and proposal extraction tasks. Our error analysis reveals that extracted claims exhibit high precision but lower recall, suggesting opportunities for further methodological refinement. NSF-SciFy enables new research directions in large-scale claim verification, scientific discovery tracking, and meta-scientific analysis.

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.2119ACL Anthology · ACL 2026

Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective

Sijie Mai, Shiqin Han

原文摘要

Multimodal affective computing aims to predict humans’ sentiment, emotion, intention, and opinion using language, acoustic, and visual modalities. However, current models often learn spurious correlations that harm generalization under distribution shifts or noisy modalities. To address this, we propose a causal modality-invariant representation (CmIR) learning framework for robust multimodal learning. At its core, we introduce a theoretically grounded disentanglement method that separates each modality into ‘causal invariant representation’ and ‘environment-specific spurious representation’ from a causal inference perspective. CmIR ensures that the learned invariant representations retain stable predictive relationships with labels across different environments while preserving sufficient information from the raw inputs via invariance constraint, mutual information constraint, and reconstruction constraint. Experiments across multiple multimodal benchmarks demonstrate that CmIR achieves state-of-the-art performance. CmIR particularly excels on out-of-distribution data and noisy data, confirming its robustness and generalizability.

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.212ACL Anthology · ACL 2026

Multi-Granularity Semantic Revision for Large Language Model Distillation

Xiaoyu Liu, Yun Zhang Hong Kong University of Science and Technology, Wei Li, Simiao Li Huawei Noah's Ark Lab, Xudong Huang Huawei Technologies Ltd., Hanting Chen Peking University, Yehui Tang Huawei Technologies Ltd., Jie Hu Huawei Technologies Ltd. 等 10 位作者

原文摘要

Knowledge distillation is crucial for compressing Large Language Models (LLMs), enabling smaller student models to learn from larger teacher models. However, existing LLM distillation methods overly rely on student-generated outputs, which may introduce generation errors and misguide the distillation process. Moreover, existing distillation loss functions struggle to align the most informative part due to the complex output distributions of LLMs. To address these problems, we propose a multi-granularity semantic revision method for LLM distillation. At the sequence level, we propose a sequence correction and re-generation (SCRG) strategy. SCRG identifies error tokens by calculating the semantic cognitive difference between teacher and student outputs, corrects them using teacher-generated tokens, and re-generates the sequence to minimize errors. At the token level, we design a distribution adaptive clipping Kullback-Leibler (DAC-KL) loss, which uses a learnable sub-network to focus on semantically dense areas of the teacher’s output, reducing the impact of redundant information. At the span level, we utilize span priors to compute probability correlations within sequences, ensuring consistency between teacher and student outputs to enhance semantic information transfer. Extensive experiments on models ranging from 0.1B to 13B parameters demonstrate the effectiveness of our approach compared to existing methods.

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.2120ACL Anthology · ACL 2026

Multi-Task Representation Alignment on Language Understanding: A Mutual Information Perspective

Dou Hu Communication University of China, Lingwei Wei Institute of Information Engineering, Chinese Academy of Sciences, Hongjiang Xiao Communication University of China, Songlin Hu, Yuan Zhang Communication University of China

原文摘要

Multi-task learning (MTL) enables joint learning over multiple tasks based on shared representations, but suffers from task interference issue during optimization. Existing works mainly focus on task balancing or probabilistic modeling but fail to address the issue since they struggle to learn sufficient representations for all target tasks. To address this, we propose a multi-task representation alignment (MTRA) framework to achieve task-specific alignment and self-alignment on the shared representations from a mutual information perspective. MTRA ensures that the learned representations contain task-relevant features while mitigating the negative effects of task-irrelevant features. First, we design a task-specific alignment objective to align the shared representations and task-specific representations with the expected targets of all tasks via information maximization. Besides, we design a self-alignment objective to eliminate task-irrelevant features via conditional information minimization. Experiments on two multi-task language benchmarks show that MTRA outperforms 13 representative MTL methods under the same settings, particularly under label-noisy and data-constrained conditions. Further analysis shows that the learned shared representations exhibit sufficient task informativeness and superior alignment properties.

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.2121ACL Anthology · ACL 2026

EconProver: Towards More Economical Test-Time Scaling for Automated Theorem Proving

Mukai Li, Linfeng Song Google, Zhenwen Liang Tencent AI Lab, Jiahao Xu Tencent AI Lab, Shansan Gong, Qi Liu University of Hong Kong, Haitao Mi Tencent AI Lab, Dong Yu CapitalOne

原文摘要

Large Language Models (LLMs) have recently advanced the field of Automated Theorem Proving (ATP), attaining substantial performance gains through widely adopted test-time scaling strategies, notably reflective Chain-of-Thought (CoT) reasoning and increased sampling passes. However, they both introduce significant computational overhead for inference. Moreover, existing cost analyses typically regulate only the number of sampling passes, while neglecting the substantial disparities in sampling costs introduced by different scaling strategies. In this paper, we systematically compare the efficiency of different test-time scaling strategies for ATP models and demonstrate the inefficiency of the current state-of-the-art (SOTA) open-source approaches. We then investigate approaches to significantly reduce token usage and sample passes while maintaining the original performance. Specifically, we propose two complementary methods that can be integrated into a unified EconRL pipeline for amplified benefits: (1) a dynamic Chain-of-Thought (CoT) switching mechanism designed to mitigate unnecessary token consumption, and (2) Diverse parallel-scaled reinforcement learning (RL) with trainable prefixes to enhance pass rates under constrained sampling passes. Experiments on miniF2F and ProofNet demonstrate that our EconProver-GD achieves comparable performance to baseline methods with only 12% of the computational cost. This work provides actionable insights for deploying lightweight ATP models without sacrificing 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.2122ACL Anthology · ACL 2026

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning

Siyuan Gan, Jiaheng Liu Nanjing University, Boyan Wang nanjing university, Tianpei Yang Nanjing University, Runqing Miao China Mobile Communications Company Limited Research Institute, Yuyao Zhang China Mobile Communications Company Limited Research Institute, Fanyu Meng JIUTIAN Research, Junlan Feng 等 11 位作者

原文摘要

Large reasoning models (LRMs) have attracted much attention due to their exceptional performance. However, their performance mainly stems from thinking, a long Chain of Thought (CoT), which significantly increase computational overhead. To address this overthinking problem, existing work focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query. Unfortunately, using RL will suffer the the reward hacking problem, e.g., the model engages in thinking but is judged as not doing so, resulting in incorrect rewards.To mitigate this problem, existing works either employ supervised fine-tuning (SFT), which incurs high computational costs, or enforce uniform token limits on non-thinking responses, which yields limited mitigation of the problem.In this paper, we propose Thinking-Based Non-Thinking (TNT). It does not employ SFT, and sets different maximum token usage for responses not using thinking across various queries by leveraging information from the solution component of the responses using thinking. Experiments on five mathematical benchmarks demonstrate that TNT reduces token usage by around 50\\%$ compared to DeepSeek-R1-Distill-Qwen-1.5B/7B and DeepScaleR-1.5B, while significantly improving accuracy. In fact, TNT achieves the optimal trade-off between accuracy and efficiency among all tested methods. Additionally, the probability of reward hacking problem in TNT’s responses, which are classified as not using thinking, remains below $10\\%$ across all tested 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.2123ACL Anthology · ACL 2026

LLM-Based Multi-Agent Systems for Clinical Workflows: A Survey of AI Hospitals

Zonghai Yao University of Massachusetts at Amherst, Hong yu University of Massachusetts at Lowell

原文摘要

This survey reviews LLM-based multi-agent systems for clinical and healthcare workflows, including diagnosis, triage, consultation, discharge, mental health, and EHR-linked decision support. We define AI hospitals as workflow-level clinical systems in which agents take explicit roles, hand off shared state, use EHR- or guideline-grounded tools, and operate with safety gates and audit-ready logs. We argue that these systems should be compared at the workflow level, rather than only by model components or end-task accuracy, because clinical action, evidence, and accountability are expressed through state transitions and handoffs. We organize the literature through a workflow-level taxonomy covering roles and handoffs, memory and evidence, tools, and reasoning, control, and escalation. We further synthesize major workflow settings and task families, introduce a four-layer evaluation stack spanning safety, process, outcome, and operations, and connect model capabilities to workflow observables relevant to deployment. Finally, we present Integration Readiness Levels (IRL1-IRL6), task-level instrumentation requirements, and recurring workflow failure modes as a practical framework for comparing, evaluating, and deploying clinical LLM agents and AI hospitals.

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.2124ACL Anthology · ACL 2026

Mind’s Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs

Rohit Sinha, Aditya Sanjiv Kanade Microsoft, Sai Srinivas Kancheti, Vineeth N. Balasubramanian, Tanuja Ganu Microsoft

原文摘要

Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for cognitive and psychological reasoning remains largely unexplored. We introduce Mind’s Eye, a multiple-choice benchmark of eight visuo-cognitive tasks inspired by classic human intelligence tests and organized under a novel A–R–T taxonomy: Abstraction, Relation, and Transformation. The tasks probe core processes of fluid intelligence such as pattern induction, analogical Relation mapping, and mental Transformation. We evaluate a diverse suite of closed-source and open-source MLLMs and compare their performance with human participants. Humans achieve 80% accuracy, while top performing MLLMs remain below 50%. Error analysis reveals failures in (i) visual attention allocation, (ii) internal perceptual manipulation, (iii) over reliance on domain priors, and (iv) weak abstraction of underlying visual concepts. Our findings suggest that current MLLMs exhibit limited fluid reasoning and visuo-cognitive integration compared with human participants, highlighting the need for cognitively grounded evaluation frameworks like Mind’s Eye.

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.2125ACL Anthology · ACL 2026

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning

Chuanrui Hu Qihoo 360, Xingze Gao, Zuyi Zhou Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Dannong Xu Institute for Computer Science, Artificial Intelligence and Technology, Yi Bai, Xintong Li Harbin Engineering University, Hui Zhang, Tong Li Shanda Group 等 11 位作者

原文摘要

Large Language Models (LLMs) are increasingly deployed as long-term interactive agents, yet their limited context windows make it difficult to sustain coherent behavior over extended interactions. Existing memory systems for LLMs often store isolated records and retrieve fragments, limiting their ability to consolidate evolving experience and resolve conflicts. We introduce EverMemOS, a self-organizing memory operating system that implements an engram-inspired lifecycle for computational memory. First, Episodic Trace Formation converts dialogue streams into MemCells that capture episodic traces, atomic facts, and time-bounded foresight. Second, Semantic Consolidation organizes MemCells into thematic MemScenes, distilling stable semantic structures and updating user profiles. Finally, Reconstructive Recollection performs MemScene-guided agentic retrieval to compose the necessary and sufficient context for downstream reasoning. Experiments on LoCoMo, LongMemEval, and PersonaMem-v2 show that EverMemOS significantly outperforms state-of-the-art methods on memory-augmented reasoning tasks.

cs.CL · cs.AI

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