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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2000ACL Anthology · ACL 2026

Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts

Sijia Luo, Xiaokang Zhang Renmin University of China, Yuxuan Hu, Bohan Zhang, Ke Wang, Jinbo Su Renmin University of China, Mengshu Sun, Lei Liang 等 9 位作者

原文摘要

Reinforcement Learning (RL) has become essential for eliciting complex reasoning capabilities in Large Language Models (LLMs). However, the substantial memory overhead of storing Key-Value (KV) caches during long-horizon rollouts acts as a critical bottleneck, often prohibiting efficient training on limited hardware. While existing KV compression techniques offer a remedy for inference, directly applying them to RL training induces a severe policy mismatch, leading to catastrophic performance collapse. To address this, we introduce Sparse-RL, which empowers stable RL training under sparse rollouts. We show that instability arises from a fundamental policy mismatch among the dense old policy, the sparse sampler policy, and the learner policy. To mitigate this issue, Sparse-RL incorporates Sparsity-Aware Rejection Sampling and Importance-based Reweighting to correct the off-policy bias introduced by compression-induced information loss. Experimental results show that Sparse-RL reduces rollout overhead compared to dense baselines while preserving the performance. Furthermore, Sparse-RL inherently implements sparsity-aware training, significantly enhancing model robustness during sparse inference deployment.

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.2001ACL Anthology · ACL 2026

C-World: A Computer Use Agent Environment Creator

Ziqiao Xi, Shuang Liang, Qi Liu University of California, San Diego, Jiaqing Zhang, Letian Peng, Fang Nan University of California, San Diego, Meshal Nayim, Tianhui Zhang University of California, San Diego 等 12 位作者

原文摘要

To close the gap between LLM-based agents and humans in planning and reasoning, agents need large-scale, diverse environments for continuous learning—yet building such environments is itself prohibitively expensive. We present C-World, an environment creation system that enables users to build agent environments on demand. We define a complete agent environment through four components: an Action Space of 5,571 format-unified tools across 204 common applications, a Task Distribution engine that synthesizes long-horizon workflows with wild constraints, a Transition Function implemented as a state controller that injects realistic failures and perturbations, and a Reward Signal combining verifiable metrics with LLM-based judgment. C-World operates in two modes: a realistic mode grounded in live API execution, and a synthesized mode powered by the World Engine, which approximates tool behavior without live service access, enabling scalable environment creation—including environments for domains and tools that do not yet exist in the real world. Evaluation of nine state-of-the-art LLMs reveals that planning ability is uniformly strong but execution remains the bottleneck, and that constraint following—not tool invocation—is the dominant failure mode. The World Engine achieves Spearman \rho = 0.883 ranking correlation with real execution, and fine-tuning on just 1,170 C-World trajectories outperforms baselines trained on 119k samples, demonstrating C-World’s dual value as a rigorous evaluation environment and a scalable data engine. Our code and data are available at https://ziqiao-git.github.io/C-World/.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2002ACL Anthology · ACL 2026

Do Morals Guide How LLMs Think? The Role of Ethical Perspectives in General Problem Solving

Iseo Kim, Eunjin Hong, Juae Kim Hankuk University of Foreign Studies

原文摘要

This study investigates how different moral conditions influence the general problem-solving capabilities of Large Language Models (LLMs). We aim to explore whether the role of morality as a cognitive function operating in human decision-making can be extended to LLMs. Specifically, we define distinct moral stages based on Kohlberg’s theory of moral development and design prompts to elicit model responses aligned with each corresponding condition. The validity of this alignment is verified using the Defining Issues Test, a human evaluation tool. Subsequently, models reflecting the characteristics of each condition are evaluated using the MMLU benchmark, which requires general problem-solving abilities across various domains. Experimental results show that different moral perspectives lead to changes in the model’s decision-making during general reasoning, reflected in both responses and internal representations. Notably, conditions grounded in more advanced moral stages tend to elicit more thoughtful and reflective problem-solving behavior, which is often associated with enhanced performance. Our study attempts to broaden the concept of LLM morality, which has traditionally only been considered in ethical judgment scenarios. Furthermore, it emphasizes that morality is not merely a means of ensuring safety but a crucial factor that shapes a model’s behavior and thought processes.

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.2003ACL Anthology · ACL 2026

ViLL-E: Video LLM Embeddings for Retrieval

Rohit Gupta University of Central Florida, Jayakrishnan Unnikrishnan Amazon, Fan Fei Amazon, Sheng Liu State University of New York, Buffalo, Son Tran Amazon, Mubarak Shah Amazon and University of Central Florida

原文摘要

Video Large Language Models (VideoLLMs) excel at video understanding tasks where outputs are textual, such as Video Question Answering and Video Captioning. However, they underperform specialized embedding-based models in Retrieval tasks, such as Text-toVideo Retrieval and Moment Retrieval. We introduce ViLL-E (Video-LLM-Embed), a unified VideoLLM architecture endowed with a novel embedding generation mechanism that allows the model to “think longer” for complex videos and stop early for easy ones. We train this model with a three-stage training methodology combining generative and contrastive learning: initial large-scale pre-training with video-caption pairs; followed by continual training on a smaller, detailed-caption dataset; and concluding with task-specific fine-tuning on a novel multi-task dataset covering Video QA, Temporal Localization, Video Retrieval, and Video-Text Matching. Our model significantly improves temporal localization (on avg. 7% over other VideoLLMs) and video retrieval (up to 4% over dual encoder models), achieving performance comparable to state-of-the-art specialized embedding models while remaining competitive on VideoQA tasks. Furthermore, our joint contrastive-generative training unlocks new zero-shot capabilities, significantly outperforming state-of-the-art methods in composed video retrieval (+5% over SotA) and retrieval from long text (+2% over SotA).

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.2004ACL Anthology · ACL 2026

From Trajectories to Graphs: Contract-Checked Editing for Verifier-Guided LLM Reasoning

Rui Li Hill Research, Shuang Cao Hill Research

原文摘要

Inference-time search can substantially improve LLM reasoning when tasks admit deterministic verification, but existing methods largely refine single trajectories and lack a reliable mechanism for composing partial solutions across candidates. We propose contract-checked graph editing: represent each candidate as an interface-typed reasoning DAG and validate every nontrivial edit with a deterministic structural gate (acyclicity, namespace closure, schema validity, terminal constraints) before invoking the verifier. The gate certifies runnability only and emits auditable rejection reasons; semantic correctness is determined solely by the verifier. Instantiated in Genetic Inference Search (GIS) with Qwen2.5-32B-Instruct under strictly matched token budgets (8K tokens), contract-checked grafting increases verifier-runnable recombination from 41.2% to 92.8% and improves accuracy over rStar (+6.1 on MATH, +9.1 on MATH L5) while using 42% fewer verifier calls. The same operators transfer across outer loops (beam, best-first, MCTS) and to structured generation and code, outperforming execution-guided beam search on Spider (+2.8) and improving multi-file code generation on HumanEval-MF (+9.2).

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.2005ACL Anthology · ACL 2026

SiLP: Enhancing Non-Dominant Language Capabilities with a Selective Bidirectional Language Projection Framework

Junpeng Liu, Jiuyi Li Dalian University of Technology, Kaiyu Huang Beijing Jiaotong University, Bo Jin Dalian University of Technology, Degen Huang Fuyao University of Science and Technology, Hui Xiong Hong Kong University of Science and Technology (Guangzhou)

原文摘要

Current large language models (LLMs) often exhibit performance imbalances between dominant languages (e.g., English) and non-dominant ones due to the skewed distribution of pretraining data. A common strategy to address this issue is to enhance cross-lingual alignment, thereby facilitating non-dominant language processing. However, existing methods typically rely on additional training objectives or language-specific parameters, which increase training complexity and cost. In this work, we propose a selective bidirectional language projection framework that enables efficient multilingual alignment and language shift using the intrinsic parameters. Specifically, we first identify the layers most sensitive to language projection between non-dominant and dominant languages through neuron activation analysis. We then perform sequential language projection within the selected layers by mapping non-dominant representations into the dominant language space and reverting them before generation. The bidirectional projection benefits the subsequent instruction tuning in non-dominant languages. Experiments on seven benchmarks demonstrate that our method remarkably enhances the performance of non-dominant languages. Further analyses indicate that our method learns better internal representations and exhibits strong generalization capabilities.

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.2006ACL Anthology · ACL 2026

Simple Agents, Biased Judges: Efficient Multi-Party Dialogue Generation & The Evaluation Gap

Kunal Samanta, Faisal Tareque Shohan, Amine Trabelsi Université de Sherbrooke, Richard Khoury Universite Laval, Université Laval

原文摘要

Multi-party social dialogue remains underexplored in the literature,in part due to the difficulty and cost of evaluation. As a result,recent work on synthetic dialogue generation often relies on automatedmetrics and LLM-as-a-Judge frameworks, despite limited evidence thatsuch judges reflect human preferences in social settings. In this work,we introduce a lightweight and controllable multi-party dialoguegeneration framework (MPOD) as an experimental instrument forstudying generation and evaluation in social interaction. Using thisframework, we conduct human evaluations of open-domain multi-partydialogue simulation and directly compare human judgments againststate-of-the-art LLM judges. Across 319 pairwise comparisons, weobserve near-random agreement between humans and automated judges(Cohen’s \kappa \approx 0.11), driven by systematic behaviorsincluding extreme tie aversion and strong sensitivity toassistant-style verbosity. Crucially, human–human inter-annotatoragreement (\kappa = 0.29) is substantially higher than human–LLMagreement. To isolate themechanism underlying this misalignment, we introduce a controlledTransplant Ablation, showing that LLM judges consistentlyprefer conversations containing a single proprietary, assistant-styleagent. Additional stress tests show that judges prefer GPT-styleconversations even when utterance order is randomly shuffled,indicating insensitivity to conversational structure and coherence.Our findings provide controlled evidence that currentinstruction-tuned LLM judges do not reliably reflect human preferences for naturalness, engagingness, and overall quality in multi-party social dialogue, calling into question their widespreaduse for validating synthetic conversational 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.2007ACL Anthology · ACL 2026

EdgeFormer: Latency-Aware Collaborative Multi-Head Attention of Transformer Inference in Edge Networks

Yiming Yao Beijing University of Aeronautics and Astronautics, Jianwei Niu Beihang University, Bin Dai Beihang University, Tao Ren

原文摘要

Recent breakthroughs in Transformer-based large models, have driven widespread tasks, yet their reliance on centralized cloud deployment raises significant privacy risks due to sensitive data exposure. While edge-based collaborative inference offers a privacy-preserving alternative, existing methods face critical limitations: static model partitioning cannot adapt to dynamic edge resource fluctuations, and rigid multi-head attention handling overlooks semantic-critical prioritization and parallelism. We propose EdgeFormer, a latency-aware framework for distributed Transformer inference in resource-constrained edge networks. EdgeFormer dynamically allocates model blocks across devices via efficiency-storage trade-off optimization and introduces collaborative Multi-Head Attention (cMHA), which distributes semantic-critical attention heads across devices while pruning redundant ones under real-time constraints. We further develop LiScore, a composite metric integrating attention diversity and latency costs, alongside a similarity-based retrieval method to reduce recomputation overhead. Extensive experiments demonstrate that EdgeFormer achieves up to 2.01 \\times inference acceleration over state-of-the-art baselines with \\leq1.06% accuracy loss, maintaining robustness under varying edge conditions.

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.2008ACL Anthology · ACL 2026

LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations

Robin Vujanic MongoDB, Thomas Rückstieß MongoDB

原文摘要

We present a knowledge distillation framework for text embedding models. A key distinguishing feature is that our distilled models are compatible with their teacher, enabling flexible asymmetric architectures where documents are encoded with the larger teacher model, while queries use smaller student models. We also show that our models automatically inherit MRL and robustness to output quantization whenever these properties are present in the teacher model, without explicitly training for them. To demonstrate the effectiveness of our framework we publish leaf-ir, a 23M parameters information retrieval oriented model that, besides being teacher-compatibile, sets a new state-of-the-art (SOTA) on BEIR, ranking no.1 on the public leaderboard for models of its size. Asymmetric mode further increases its retrieval performance. Our scheme is however not restricted to information retrieval. We demonstrate its wider applicability by synthesizing the multi-task leaf-mt model. This also sets a new SOTA, achieving no.1 on the public MTEB v2 (English) leaderboard for models of its size. Our technique is applicable to black-box models, requires no judgments nor hard negatives, and training can be conducted using small batch sizes. Thus, dataset and training infrastructure requirements for our framework are modest. We make our models publicly available under a permissive Apache 2.0 license.

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.2009ACL Anthology · ACL 2026

SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking

Weiyang Huang Harbin Institute of Technology ,(Shenzhen), Xuefeng Bai, Kehai Chen Harbin Institute of Technology (Shenzhen), Xinyang Chen Harbin Institute of Technology, Shenzhen, Yibin Chen, Weili Guan, Min Zhang Harbin Institute of Technology, Shenzhen and Harbin Institute of Technology

原文摘要

Large Reasoning Models (LRMs) have revolutionized complex problem-solving, yet they exhibit a pervasive “overthinking”, generating unnecessarily long reasoning chains. While current solutions improve token efficiency, they often sacrifice fine-grained control or risk disrupting the logical integrity of the reasoning process. To address this, we introduce Stepwise Adaptive Thinking (SAT), a framework that performs step-level, difficulty-aware pruning while preserving the core reasoning structure. SAT formulates reasoning as a Finite-State Machine (FSM) with distinct thinking modes (SLOW, NORMAL, FAST, SKIP). It navigates these states dynamically using a lightweight Process Reward Model (PRM), compressing easy steps while preserving depth for hard ones. Experiments across 9 LRMs and 7 benchmarks show that SAT achieves up to 40% reduction in reasoning tokens while generally maintaining or improving accuracy. Code is available at https://github.com/byxw13/SAT_Code.

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.201ACL Anthology · ACL 2026

CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation

Sizhe Wang, Zhengren Wang Peking University, Dongsheng Ma, Yongan Yu Mila - Quebec Artificial Intelligence Institute and McGill University, McGill University, Rui Ling, Zhiyu li, Feiyu Xiong MemTensor (Shanghai) Technology Co., Ltd., Wentao Zhang

原文摘要

Modern software development demands code that is maintainable, testable, and scalable by organizing the implementation into modular components with iterative reuse of existing codes. We formalize this iterative, multi-turn paradigm as codeflow and introduce CodeFlowBench, the first benchmark designed to comprehensively evaluate LLMs’ ability to perform codeflow - implementing new functionality by reusing existing functions over multiple turns. CodeFlowBench comprises two complementary components: CodeFlowBench-Comp, a core collection of 5,000+ competitive programming problems from Codeforces updated via an automated pipeline and CodeFlowBench-Repo, which is sourced from GitHub repositories to better reflect real-world scenarios. Furthermore, a novel evaluation framework featured dual assessment protocol and structural metrics derived from dependency trees is introduced. Extensive experiments reveal significant performance degradation in multi-turn codeflow scenarios. Furthermore, our in-depth analysis illustrates that model performance inversely correlates with dependency complexity. These findings not only highlight the critical challenges for supporting real-world workflows, but also establish CodeFlowBench as an essential tool for advancing code generation research.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2010ACL Anthology · ACL 2026

Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety

Wei-Chieh Huang, Henry Peng Zou University of Illinois at Chicago, Yaozu Wu The University of Tokyo, Dongyuan Li The University of Tokyo and Institute of Science Tokyo, Yankai Chen Cornell University, Weizhi Zhang University of Illinois at Chicago, Yangning Li, Angelo Zangari 等 15 位作者

原文摘要

Deep research frameworks have shown promising capabilities in synthesizing comprehensive reports from web sources. While deep research possesses significant potential to address complex issues through planning and research cycles, existing frameworks are deficient in sufficient evaluation procedures and stage-specific protections. They typically treat evaluation as exact match accuracy of question-answering, but overlook crucial aspects of report quality such as credibility, coherence, breadth, depth, and safety. This oversight may result in hazardous or malicious sources being integrated into the final report. To address this, we introduce DeepResearchGuard, a framework featuring four-stage safeguards with open-domain evaluation, and DRSafeBench, a novel stage-wise safety benchmark. Evaluating across GPT-4o, o4-mini, Gemini-2.5-flash, DeepSeek-v3, and GPT-5, DeepResearchGuard improves defense success rates by an absolute 16.53% while reducing over-refusal rates to approximately 6%. Through extensive experiments, we show that DeepResearchGuard enables comprehensive open-domain evaluation and stage-aware defenses that effectively block harmful content propagation, while systematically improving report quality without excessive over-refusal rates.

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.2011ACL Anthology · ACL 2026

P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist

Kwangwook Seo, Dongha Lee Yonsei University

原文摘要

Recent approaches in personalized reward modeling have primarily focused on leveraging user interaction history to align model judgments with individual preferences. However, existing approaches largely treat user context as a static or implicit conditioning signal, failing to capture the dynamic and multi-faceted nature of human judgment. In this paper, we propose P-Check, a novel personalized reward modeling framework, designed to train a plug-and-play checklist generator that synthesizes dynamic evaluation criteria for guiding the reward prediction. To better align these checklists with personalized nuances, we introduce Preference-Contrastive Criterion Weighting, a training strategy that assigns saliency scores to criteria based on their discriminative power for personalized judgment. We conduct extensive experiments and demonstrate that P-Check not only improves reward accuracy but also enhances downstream personalized generation, and remains robust in OOD scenarios.

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.2012ACL Anthology · ACL 2026

Locate and Explain: Joint Multimodal Emotion Cause Extraction and Summarization in Conversation

Jikun Wan, Chen Gong, Guohong Fu Soochow University, China,

原文摘要

Multimodal emotion cause analysis in conversation aims to identify the causes of emotions by leveraging multimodal information. Existing studies mainly formulate this problem as either utterance-level emotion cause extraction, which provides clear cause localization but limited explanation, or multimodal emotion cause generation, which offers fine-grained explanations but lacks explicit traceability to source utterances. Moreover, existing datasets rely heavily on human judgment and lack well-defined structured theoretical guidance, leading to subjective and inconsistent annotations. To address these issues, we introduce joint Multimodal Emotion Cause Extraction and Summarization in conversation (MECES), a new task that simultaneously extracts emotion cause utterances and generates cause summaries, enabling both precise localization and interpretable explanations of emotion cause. We further construct a MECES dataset guided by the Activating Events–Beliefs–Consequences theory from psychology. This dataset consists of 5,787 emotion utterances annotated with causes, comprising 12,231 emotion-cause pairs and 6,040 cause summaries. We also propose an effective end-to-end joint learning approach for MECES task, establishing strong benchmark results for this newly introduced task and dataset.

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.2013ACL Anthology · ACL 2026

Understanding the Behaviors of Environment-aware Information Retrieval

Ruifeng Yuan, Chaohao Yuan The Chinese University of Hong Kong, David Dai, Yu Rong Chinese University of Hong Kong, Hong Cheng The Chinese University of Hong Kong, Hou Pong Chan Alibaba Group, Chenghao Xiao Shanghai University of Finance and Economics

原文摘要

Recent retrieval-augmented generation (RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: different retrievers require fundamentally different query formulation strategies for optimal performance. In this work, we present the first systematic analysis of how LLMs can learn to adapt their query formulation strategies for different retrievers via reinforcement learning (RL). Our empirical study reveals that RL effectively teaches an LLM to tailor its queries to specific retriever characteristics. We discover that different retrievers exhibit surprisingly distinct optimal query styles (e.g., descriptive vs. question-like), suggesting strategies learned for one retriever ineffective for another. We further show that performance can be enhanced by incorporating retriever-specific human guidance and by scaling model size. To facilitate learning over multi-retrieval-step trajectories, we introduce a branching-based rollout technique that improves training stability. Our work provides the first empirical evidence and actionable insights for building truly retriever-aware RAG systems. Code and resources are available at https://github.com/LCO-Embedding/Envs-aware-Information-Retrieval.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2014ACL Anthology · ACL 2026

ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services

Fengxian Ji Mohamed bin Zayed University of Artificial Intelligence and Northeastern University(China), Jingpu Yang, Zirui Song, Lang Gao Mohamed bin Zayed University of Artificial Intelligence, Junhong Liang Mohamed bin Zayed University of Artificial Intelligence, Zhenhao Chen, Jinghui Zhang Mohamed bin Zayed University of Artificial Intelligence, Xiuying Chen Mohamed bin Zayed University of Artificial Intelligence

原文摘要

Recent image generation and editing models demonstrate robust adherence to instructions and high visual quality on academic benchmarks.However, their performance on paid, real-world design projects remains uncertain. We introduce ServImage, a benchmark that explicitly correlates model outputs with economic value in commercial design projects. ServImage consists of (i) ServImageBench: a dataset of 1.07k paid commercial design tasks and 2.05k designer deliverables totaling over $295k, covering portrait, product, and digital content, along with 33k candidate images and 33k human annotations.(ii) ServImageScore: an integrated scoring system that combines three quality dimensions: baseline requirements fulfilment, visual execution quality, and commercial necessity satisfaction. These three dimensions are designed to characterize the factors that drive human payment decisions and indicate whether an image is commercially acceptable.(iii) ServImageModel: under this scoring system, we propose a payment prediction model trained on the human-annotated candidate images, achieving 82.00% accuracy in predicting human payment decisions and producing calibrated payment probabilities.ServImage provides a comprehensive foundation for assessing the commercial viability of image generation models and offers a scalable resource for future research on economically grounded vision systems Github.

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.2015ACL Anthology · ACL 2026

FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs

Bingkang Shi, Jen-tse Huang, Luo Long, Tianyu Zong, Hongzhu Yi, Yuanxiang Wang University of the Chinese Academy of Sciences, Songlin Hu, Xiaodan Zhang 等 9 位作者

原文摘要

Large Language Models (LLMs) have increasingly enhanced or replaced traditional Non-Player Characters (NPCs) in video games. However, these LLM-based NPCs inherit underlying social biases (e.g., race or class), posing fairness risks during in-game interactions. To address the limited exploration of this issue, we introduce FairGamer, the first benchmark to evaluate social biases across three interaction patterns: transaction, cooperation, and competition. FairGamer assesses four bias types, including class, race, age, and nationality, across 12 distinct evaluation tasks using a novel metric, FairMCV. Our evaluation of seven frontier LLMs reveals that: (1) models exhibit biased decision-making, with Grok-4-Fast demonstrating the highest bias (average FairMCV = 76.9%); and (2) larger LLMs display more severe social biases, suggesting that increased model capacity inadvertently amplifies these biases. We release FairGamer at https://github.com/BingkangShi/FairGamer to facilitate future research on NPC fairness.

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.2016ACL Anthology · ACL 2026

EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection

Shuo Yang, Caren Han University of Melbourne, University of Western Australia and University of Sydney, Xueqi Ma, Yan Li, Mohammad Reza Ghasemi Madani University of Melbourne, Eduard Hovy

原文摘要

LLM-based agents depend on effective tool-use policies to solve complex tasks, yet optimizing these policies remains challenging dueto delayed supervision and the difficulty ofcredit assignment in long-horizon trajectories.Existing optimization approaches tend to beeither monolithic, which are prone to entangling behaviors, or single-aspect, which ignorecross-module error propagation. To addressthese limitations, we propose EVOTOOL, a self-evolving framework that optimizes a modulartool-use policy via a gradient-free evolutionary paradigm. EVOTOOL decomposes agent’stool-use policy into four modules, includingPlanner, Selector, Caller, and Synthesizer, anditeratively improves them through three mechanisms. Trajectory-Grounded Blame Attribution uses diagnostic traces to localize failuresto a specific module. Feedback-Guided Targeted Mutation then edits only that modulevia natural-language critique. Diversity-AwarePopulation Selection preserves complementarycandidates to ensure solution diversity. Acrossfour diverse benchmarks, EVOTOOL outperforms strong baselines by over 5 points on bothGPT-4.1 and Qwen3-8B, while achieving superior efficiency and transferability.

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.2017ACL Anthology · ACL 2026

A Lightweight Explainable Guardrail for Prompt Safety

Md Asiful Islam University of Arizona, Mihai Surdeanu University of Arizona

原文摘要

We propose a lightweight explainable guardrail (LEG) method to detect unsafe prompts. LEG uses a multi-task learning architecture to jointly learn a prompt classifier and an explanation classifier, where the latter labels prompt words that explain the safe/unsafe overall decision. LEG is trained on synthetic explanation data, which is generated using a novel strategy that counteracts the confirmation biases of LLMs. Lastly, LEG’s training process uses a novel loss that captures global explanation signals as a weak supervision and combines cross-entropy and focal losses with uncertainty-based weighting. LEG obtains equivalent or better performance than the state-of-the-art for both prompt classification and explainability, both in-domain and out-of-domain on three datasets, despite the fact that its model size is considerably smaller than current approaches.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2018ACL Anthology · ACL 2026

Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI

Hieu Man University of Oregon, Van-Cuong Pham University of Oregon, Nghia Trung Ngo University of Oregon, Franck Dernoncourt Adobe Research, Thien Huu Nguyen University of Oregon

原文摘要

Learning robust representations of authorial style is crucial for authorship attribution and AI-generated text detection. However, existing methods often struggle with content-style entanglement, where models learn spurious correlations between authors’ writing styles and topics, leading to poor generalization across domains. To address this challenge, we propose Explainable Authorship Variational Autoencoder (EAVAE), a novel framework that explicitly disentangles style from content through architectural separation-by-design. EAVAE first pretrains style encoders using supervised contrastive learning on diverse authorship data, then finetunes with a Variational Autoencoder (VEA) architecture using separate encoders for style and content representations. Disentanglement is enforced through a novel discriminator that not only distinguishes whether pairs of style/content representations belong to the same or different authors/content sources, but also generates natural language explanation for their decision, simultaneously mitigating confounding information and enhancing interpretability. Extensive experiments demonstrate the effectiveness of EAVAE. On authorship attribution, we achieve state-of-the-art performance on various datasets, including Amazon Reviews, PAN21, and HRS. For AI-generated text detection, EAVAE excels in few-shot learning over the M4 dataset.

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.2019ACL Anthology · ACL 2026

PodBench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation

Chenning Xu Tencent Hunyuan Team, Mao Zheng, Mingyu Zheng, Mingyang Song Tencent

原文摘要

Podcast script generation requires LLMs to synthesize structured, context-grounded dialogue from diverse inputs, yet systematic evaluation resources for this task remain limited. To bridge this gap, we introduce PodBench, a benchmark comprising 800 samples with inputs up to 21K tokens and complex multi-speaker instructions. We propose a multifaceted evaluation framework that integrates quantitative constraints with LLM-based quality assessment. Extensive experiments reveal that while proprietary models generally excel, open-source models equipped with explicit reasoning demonstrate superior robustness in handling long contexts and multi-speaker coordination compared to standard baselines. However, our analysis uncovers a persistent divergence where high instruction following does not guarantee high content substance. PodBench offers a reproducible testbed to address these challenges in long-form, audio-centric script 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.202ACL Anthology · ACL 2026

REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control

Chuyi Kong, Wei Gao Singapore Management University, Jing Ma Hong Kong Baptist University, Hongzhan Lin National University of Singapore, Yuxi Sun

原文摘要

The prevalence of fake news on social media calls for automated fact-checking systems that deliver not only accurate verdicts but also faithful explanations. However, existing large language model (LLM)-based methods often overlook deceptive misinformation styles in generated explanations, producing unfaithful rationales that may mislead human judgment. They also rely heavily on external knowledge sources, which can introduce hallucinations and incur substantial latency, undermining both reliability and responsiveness in real-time settings. To address these limitations, we propose REason-guided Fact-checking with Latent EXplanations (REFLEX), a self-refining framework that explicitly controls reasoning style by anchoring explanations to the predicted verdict. REFLEX leverages self-disagreement veracity signals between a backbone model and its fine-tuned variant to construct steering vectors, thereby naturally disentangling factual content from stylistic cues. Experiments on a real-world benchmark show that REFLEX achieves state-of-the-art performance under LLaMA-series models using only 465 self-refined samples. Owing to its transferability, REFLEX also yields gains of up to 7.54 Macro-F1 points on in-the-wild data. Further analysis shows that our method effectively mitigates faithful hallucination, leading to both more reliable explanations and more accurate verdicts than prior explainable fact-checking approaches.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2020ACL Anthology · ACL 2026

MIND: From Passive Mimicry to Active Reasoning through Capability-Aware Multi-Perspective CoT Distillation

Jin Cui Xi'an Jiaotong University, Jiaqi Guo Nankai University, Jiepeng Zhou, Ruixuan Yang, Jiayi Lu, Jiajun Xu Xi'an Jiaotong University, Jiangcheng Song, Boran Zhao 等 9 位作者

原文摘要

While Large Language Models (LLMs) have emerged with remarkable capabilities in complex tasks through Chain-of-Thought (CoT) reasoning, practical resource constraints have sparked interest in transferring these abilities to smaller models. However, achieving both domain performance and cross-domain generalization remains challenging. Existing approaches typically restrict students to following a single golden rationale and treat different reasoning paths independently. Due to distinct inductive biases and intrinsic preferences, alongside the student’s evolving capacity and reasoning preferences during training, a teacher’s “optimal” rationale could act as out-of-distribution noise. This misalignment leads to a degeneration of the student’s latent reasoning distribution, causing suboptimal performance. To bridge this gap, we propose MIND, a capability-adaptive framework that transitions distillation from passive mimicry to active cognitive construction. We synthesize diverse teacher perspectives through a novel “Teaching Assistant” network. By employing a novel Feedback-Driven Inertia Calibration mechanism, this network utilizes inertia-filtered training loss to align supervision with the student’s current adaptability, effectively enhancing performance while mitigating catastrophic forgetting. Extensive experiments demonstrate that MIND achieves state-of-the-art performance on both in-distribution and out-of-distribution benchmarks, and our sophisticated latent space analysis further confirms the mechanism of reasoning ability internalization.

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.2021ACL Anthology · ACL 2026

Knowledge Vector of Logical Reasoning in Large Language Models

Zixuan Wang, Yuanyuan Lei University of Florida

原文摘要

Logical reasoning serves as a central capability in LLMs and includes three main forms: deductive, inductive, and abductive reasoning. In this work, we study the knowledge representations of these reasoning types in LLMs and analyze the correlations among them. Our analysis shows that each form of logical reasoning can be captured as a reasoning-specific knowledge vector in a linear representation space, yet these vectors are largely independent of each other. Motivated by cognitive science theory that these subforms of logical reasoning interact closely in the human brain, as well as our observation that the reasoning process for one type can benefit from the reasoning chain produced by another, we further propose to refine the knowledge representations of each reasoning type in LLMs to encourage complementarity between them. To this end, we design a complementary subspace-constrained refinement framework, which introduces a complementary loss that enables each reasoning vector to leverage auxiliary knowledge from the others, and a subspace constraint loss that prevents erasion of their unique characteristics. Through steering experiments along reasoning vectors, we find that refined vectors incorporating complementary knowledge yield consistent performance gains. We also conduct a mechanism-interpretability analysis of each reasoning vector, revealing insights into the shared and specific features of different reasoning 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.2022ACL Anthology · ACL 2026

BanHADEX: Towards Explainable HAte Speech Detection in Bangla Using Human Annotated EXplanation

Faisal Hossain Raquib Rajshahi University of Engineering and Technology, Akm Moshiur Rahman Mazumder Independent University, Bangladesh, Md Fahim Penta Global Limited and Independent University, Bangladesh, Md Tahmid Hasan Fuad University of Manitoba, Md Farhan Ishmam, Faria Sultana, M Ashraful Amin Independent University, Bangladesh, Amin Ahsan Ali 等 9 位作者

原文摘要

Online safety in low-resource languages hinges not only on accurate hate speech detection but also on transparent, culturally grounded explanations. Yet prior works in Bangla largely focus on hate classification, while overlooking interpretability. We address this gap by introducing BanHADEX, the first hate explainability dataset in Bangla with human-annotated labels. BanHADEX contains 19,203 YouTube comments spanning April 2024–June 2025, annotated for binary hate classification with seven fine-grained hate categories, seven target groups, and concise explanations for each sample. Our data pipeline relies on a two-stage annotation protocol that uses majority voting for robust labeling. Our rich suite of experiments on open and closed-source LLMs reveals that explanation-guided LoRA substantially outperforms both classification and explanation quality across prompting and fine-tuning strategies. BanHADEX establishes the groundworks for faithful interpretability and safer moderation in linguistically rich yet 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.2023ACL Anthology · ACL 2026

SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter

Lee Jung-Mok, Kim Sung-Bin Pohang University of Science and Technology, Joohyun Chang Korea Advanced Institute of Science & Technology, Lee Hyun, Tae-Hyun Oh Korea Advanced Institute of Science & Technology

原文摘要

Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tasks, a comprehensive understanding of laughter in real-world scenarios remains underexplored. We introduce SMILE-Next, a dataset for real-world laughter understanding with multimodal textual representations and question–answer annotations across three tasks: laughter detection, laughter type classification, and laughter reasoning. Building on this dataset, we propose a laughter expert LLM that leverages disentangled multimodal textual cues, together with a Mixture-of-Laugh-Experts framework and laughter-specific self-instruction for task-adaptive specialization. Experimental results show that the combination of our proposed components substantially outperforms multimodal LLM baselines, advancing robust real-world laughter understanding.

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.2024ACL Anthology · ACL 2026

MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization

Ziqing Wang, Yibo Wen, Abhishek Pandey, Han Liu Northwestern University, Kaize Ding Northwestern University

原文摘要

In drug discovery, molecular optimization aims to iteratively refine a lead compound to improve molecular properties while preserving structural similarity to the original molecule. However, each oracle evaluation is expensive, making sample efficiency a key challenge for existing methods under a limited oracle budget. Trial-and-error approaches require many oracle calls, while methods that leverage external knowledge tend to reuse familiar templates and struggle on challenging objectives. A key missing piece is long-term memory that can ground decisions and provide reusable insights for future optimizations. To address this, we present MolMem (Molecular optimization with Memory), a multi-turn agentic reinforcement learning (RL) framework with a dual-memory system. Specifically, MolMem uses Static Exemplar Memory to retrieve relevant exemplars for cold-start grounding, and Evolving Skill Memory to distill successful trajectories into reusable strategies. Built on this memory-augmented formulation, we train the policy with dense step-wise rewards, turning costly rollouts into long-term knowledge that improves future optimization. Extensive experiments show that MolMem achieves 90% success on single-property tasks (1.5\times over the best baseline) and 52% on multi-property tasks using only 500 oracle calls. Our code is available at https://github.com/REAL-Lab-NU/MolMem.

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.2025ACL Anthology · ACL 2026

Activation Decomposition and Steering for LLM Backdoor Remediation

Lingfeng Zhong, Qiongkai Xu Macquarie University, Usman Naseem Macquarie University

原文摘要

Existing works on defending against LLM backdoor attacks rely on either auxiliary models or safety-related datasets for defending against backdoor attacks on large language models, which are not always available. To address these challenges, we propose our we propose our Contrastive-Selective Activation Decomposition and Steering (CS-ADS), which contrasts relatively more benign and poisoned settings to decompose the feature vectors for steering without relying on additional auxiliary models or datasets. With such disentangled vectors for remediation, our method can achieve feasible defense qualities even better than dataset-based contrastive steering strategies. This novel decomposition-based solution is motivated by the key insight that feature representations of prompt pairs can encode the same benign semantics in different proportions, even when both prompt pairs are similarly backdoored. Such discrepancies allow our method to identify effective remediation directions for steering the generation process, thereby preventing undesired outputs. We evaluate CS-ADS against multiple state-of-the-art backdoor attacks, and experimental results show that CS-ADS provides effective defense across 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.2026ACL Anthology · ACL 2026

A Survey of Large Language Models for Text-Guided Molecular Discovery: From Molecule Generation to Optimization

Ziqing Wang, Kexin Zhang, Zihan Zhao, Yibo Wen, Abhishek Pandey, Han Liu Northwestern University, Kaize Ding Northwestern University

原文摘要

Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language and symbolic notations, with emerging extensions to incorporate multi-modal inputs. To advance this emerging field, this survey provides an up-to-date and forward-looking review of the emerging use of LLMs for two central tasks: molecule generation and molecule optimization. We organize our survey around four fundamental challenges that have emerged as critical evaluation dimensions in recent studies: ensuring validity, enhancing synthesizability, achieving precise property control, and maximizing diversity. Based on this, we systematically analyze how current LLM learning paradigms are applied to tackle each challenge, revealing the distinct capabilities and inherent limitations of each approach. In addition, we include the commonly used datasets and evaluation protocols aligned with these challenges. We conclude by discussing future directions, positioning this survey as a resource for researchers working at the intersection of LLMs and molecular science. A continuously updated reading list is available at https://github.com/REAL-Lab-NU/Awesome-LLM-Centric-Molecular-Discovery.

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.2027ACL Anthology · ACL 2026

Reward Alignment Optimization: A Direct Point-wise Alignment Approach

Zelin Li Microsoft, Jia Leng, Dawei Song Beijing Institute of Technology and Open University, Yangen Hu

原文摘要

Direct Alignment Algorithms (DAAs) such as DPO simplify RLHF by optimizing policies directly from preference pairs. However, the Bradley–Terry probability-gap objective can induce likelihood displacement and, under weak KL constraints, may even reduce the probability of preferred responses, while implicit rewards can be limited in generalizaiton. We propose Reward Alignment Optimization (RAO), a point-wise direct alignment method that uses an explicit reward model to specify exact target generation probabilities and align the policy offline towards them. Our key insight is a theoretical principle we call “prefix consistency”, which links the normalization terms of prompts that share a prefix. Leveraging this property, RAO decouples target reward differentials from bias terms, prevents decreasing preferred-response probabilities, and better exploits reward information both within and across prompts. Extensive experiments on multiple base LLMs show that RAO consistently outperforms existing DAAs while enabling controllable target probability distributions.

cs.CL

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