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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1163ACL Anthology · ACL 2026

CSPO: Alleviating Reward Ambiguity for Structured Table-to-LaTeX Generation

Yunfan Yang, Cuiling Lan Microsoft, Jitao Sang Beijing Jiaotong University, Yan Lu Microsoft Research Asia

原文摘要

Tables contain rich structured information, yet when stored as images their contents remain “locked” within pixels. Converting table images into LaTeX code enables faithful digitization and reuse, but current multimodal large language models (MLLMs) often fail to preserve structural, style, or content fidelity. Conventional post-training with reinforcement learning (RL) typically relies on a single aggregated reward, leading to reward ambiguity that conflates multiple behavioral aspects and hinders effective optimization. We propose Component-Specific Policy Optimization (CSPO), an RL framework that disentangles optimization across LaTeX tables components—structure, style, and content. In particular, CSPO assigns component-specific rewards and backpropagates each signal only through the tokens relevant to its component, alleviating reward ambiguity and enabling targeted component-wise optimization. To comprehensively assess performance, we introduce a set of hierarchical evaluation metrics. Extensive experiments demonstrate the effectiveness of CSPO, underscoring the importance of component-specific optimization for reliable structured generation.

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.1164ACL Anthology · ACL 2026

Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents

Ymyang, Jiang Zhong Chongqing University, Li Jin, Xiao Sun, Jingwang Huang, Gaojinpeng, Qing Liu, Yang Bai 等 12 位作者

原文摘要

Multimodal Retrieval-Augmented Generation (MRAG) enhances reasoning capabilities by integrating external knowledge. However, existing benchmarks primarily focus on simple image-text interactions, overlooking complex visual formats like charts that are prevalent in real-world applications. In this work, we introduce a novel task, \textbf{Chart-based MRAG}, to address this limitation. To generate high-quality evaluation samples, we propose \textbf{CHARGE} (\textbf{CHAR}t-based document question-answering \textbf{GE}neration), a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis.By combining CHARGE with expert validation, we construct \textbf{Chart-MRAG Bench}, a comprehensive benchmark for chart-based MRAG evaluation, featuring 4,738 question-answering pairs across 8 domains from real-world documents.Our experiments reveal three critical limitations in current approaches: (1) unified multimodal embedding retrieval methods struggles in chart-based scenarios, (2) even with ground-truth retrieval, state-of-the-art Multimodal Large Language Models (MLLMs) achieve only 71.15% Correctness and 80.74% Coverage scores, and (3) Widely-used MLLMs demonstrate consistent text-over-visual modality bias. These findings highlight great challenges in processing information-dense visual formats. We will make our code and dataset publicly available.

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.1165ACL Anthology · ACL 2026

Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts

Di Zhang, Xun Wu, Shaohan Huang, Lingjie Jiang Peking University, Yaru Hao Microsoft Research Asia, Li Dong Microsoft Research, Zewen Chi Microsoft Research, Zhifang Sui Peking University 等 9 位作者

原文摘要

Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for improving reasoning capabilities. However, training RLVR with Mixture-of-Experts (MoE) policies remains fragile and is often prone to reward collapse.We identify a MoE-specific source of instability, referred to as router shift (RS), where changes in expert routing across policy updates exacerbate off-policy mismatch. This effect leads to increasingly volatile importance-ratio signals and bursty clipping behavior, which consistently precede training collapse.Motivated by this diagnosis, we propose Router-Shift Policy Optimization (RSPO). RSPO computes a per-token router-shift ratio conditioned on the previously activated experts, applies stop-gradient and a lower-bound floor, and softly rescales importance ratios prior to clipping and aggregation. This design explicitly accounts for routing-induced distributional drift during off-policy optimization.We evaluate the effect of RSPO under two settings: a synthetic countdown task and real-world reasoning tasks on MATH and Code. Across both settings, RSPO achieves better performance and exhibits greater stability compared to recent MoE-based RLVR 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.1166ACL Anthology · ACL 2026

AIRCoder: Adaptive Integration of Multi-dimensional Retrieval for Repository-level Code Completion

Chuanqi Shi Southeast University, Miao Gao Southeast university, Zhiqiang Gao Southeast University

原文摘要

Repository-level code completion relies on retrieval strategies to select relevant context from large codebases. Most existing methods employ single-dimensional retrieval based on textual similarity or dependency existence, leading to inconsistent performance across completion scenarios. Even task-adaptive and hybrid methods incur high computational costs while failing to explicitly consider structural hierarchy for retrieving functionally similar code. We introduce AIRCoder, a multi-dimensional retrieval framework that combines eight complementary metrics across three dimensions: textual similarity, dependency existence, and structural hierarchy. By proposing a structure-preserving chunking strategy and lightweight fusion module, AIRCoder learns context-dependent weights to adaptively integrate retrieval metrics for each query. Experiments on CrossCodeEval and RepoEval demonstrate that AIRCoder achieves an average improvement of 4.63% in exact match over the best baseline, with 10.2\times higher efficiency and strong cross-language generalization across Python, Java, C#, and TypeScript.

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.1167ACL Anthology · ACL 2026

ReLook: Vision-Grounded RL with a Multimodal LLM Critic for Agentic Web Coding

Yuhang Li, Chenchen Zhang, Ruilin Lv, Ao Liu Tencent, Ken Deng, Yuanxing Zhang Kuaishou- 快手科技, Jiaheng Liu Nanjing University, Bo Zhou

原文摘要

While Large Language Models (LLMs) excel at algorithmic code generation, they struggle with front-end development, where correctness is judged on rendered pixels and interaction. We present ReLook, an agentic, vision-grounded reinforcement learning framework that empowers an agent to close a robust generate–diagnose–refine loop by invoking a multimodal LLM (MLLM) as a tool. During training, the agent employs an MLLM-in-the-loop to serve as a visual critic, evaluating code via screenshots and providing actionable feedback. Crucially, we enforce a strict zero-reward policy for invalid renders to guarantee renderability and mitigate reward hacking. To prevent behavioral collapse, we introduce Forced Optimization, a strict acceptance rule that admits only improving revisions, yielding monotonically better trajectories. At inference, we decouple the critic and run a lightweight, critic-free self-edit cycle, keeping latency comparable to base decoding while retaining most of the gains. Across three widely used benchmarks, ReLook consistently outperforms strong baselines in vision-grounded front-end code generation, highlighting the benefits of agentic perception, visual rewards, and training–inference decoupling.

cs.CL · cs.AI · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1168ACL Anthology · ACL 2026

SafetyMem: Adaptive Jailbreak Defense via Dual-Component Safety Memory

Hao Wang, Ziyi Ni, Huacan Wang QuantaAlpha, Pin Lyu Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Lei Sha Beihang University

原文摘要

Current defenses for Large Language Models (LLMs) often suffer from a ”memory gap”: parameter-modifying methods are computationally rigid, while inference-time filters cannot retain or reuse defense knowledge across interactions. To address this, we propose SafetyMem, a novel framework that secures LLMs through a dual-component safety memory system. SafetyMem consists of Semantic Safety Memory (SSM), which consolidates diverse jailbreak attempts into a structured knowledge base of attack patterns, and Episodic Safety Memory (ESM), which maintains an evolving set of procedural rules refined from historical detection failures. Unlike static defenses, SafetyMem allows the model to ”remember” and adapt to emerging adversarial strategies without parameter retraining. To further enhance robustness, we introduce an adversarial memory expansion mechanism that proactively generates challenging variants to solidify these memories. Experiments on standard and stealthy jailbreak benchmarks show that SafetyMem substantially reduces attack success rates while preserving efficiency and interpretability, consistently outperforming state-of-the-art baselines across multiple 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.1169ACL Anthology · ACL 2026

Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervision

Ge Chang, Jinbo Su Renmin University of China, Jiacheng Liu, Pengfei Yang, Yuhao Shang, Huiwen Zheng, Hongli Ma GDS, Yan Liang 等 10 位作者

原文摘要

Integrating textual graphs into Large Language Models (LLMs) is promising for complex graph-based QA. However, a key bottleneck is retrieving informative yet compact subgraphs that fit the LLM context. Existing retrievers often struggle, relying either on shallow embedding similarity or costly interactive policies that require excessive supervision.To address these challenges, we introduce Graph-S^3, an agentic textual graph reasoning framework featuring an LLM-based retriever trained with synthetic stepwise supervision. Rather than relying on final answer rewards—which often yield sparse and unstable signals—we optimize the retriever by evaluating each step against offline-extracted golden subgraphs.Our approach distills golden subgraphs via a specialized data synthesis pipeline to formulate dense rewards, facilitating a two-stage training scheme that effectively learns the interactive graph exploration policy.Based on extensive experiments on three common datasets in comparison with seven strong baselines, our approach achieves an average improvement of 15.6% in accuracy and 17.2% in F_1 score. The advantage is even higher in more complicated multi-hop 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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.117ACL Anthology · ACL 2026

Splits! Flexible Sociocultural Linguistic Investigation at Scale

Eylon Caplan Purdue University, Tania Chakraborty, Dan Goldwasser Purdue University

原文摘要

Variation in language use, shaped by speakers’ sociocultural background and specific context of use, offers a rich lens into cultural perspectives, values, and opinions. For example, Chinese students discuss healthy eating with words like timing, regularity, and digestion, whereas Americans use vocabulary like balancing food groups and avoiding fat and sugar, reflecting distinct cultural models of nutrition (Banna et al., 2016). The computational study of these Sociocultural Linguistic Phenomena (SLP) has traditionally been done in NLP via tailored analyses of specific groups or topics, requiring specialized data collection and experimental operationalization—a process not well-suited to quick hypothesis exploration and prototyping. To address this, we propose constructing a “sandbox” designed for systematic and flexible sociolinguistic research. Using our method, we construct a demographically/topically split Reddit dataset, Splits!, validated by self-identification and by replicating several known SLPs from existing literature. We showcase the sandbox’s utility with a scalable, two-stage process that filters large collections of potential SLPs (PSLPs) to surface the most promising candidates for deeper, qualitative investigation.

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.1170ACL Anthology · ACL 2026

Cognitive Scaffold: From Fluid Context to Crystallized Memory for Long-Horizon DeepResearch Agents

Qiuyuan Ai, Zenghuang Fu, Zhaoyang Li, Ping Jiang Peking University, Haoyu Wu Mininglamp.com, Jie Song Peking University, Guannan He Peking University

原文摘要

Scaling LLM-based agents to long-horizon deep research is constrained by the context-noise trade-off, where linear history accumulation degrades reasoning and dilutes fine-grained evidence. To address this, we introduce the Cognitive Scaffold, a factorized memory architecture that decouples the cognitive state into a Fluid Working Context for immediate reasoning and a persistent Knowledge Graph for long-term retention. Unlike unstructured summarization, our framework employs a Rejection Sampling Fine-Tuning (RFT) pipeline to crystallize saturated context into structured event snapshots, strictly enforcing atomic constraints to preserve numerical values and entities. During reasoning, a thought-driven dual-path retrieval mechanism enables the agent to proactively recover precise evidence. Empirical evaluations on Xbench-DeepSearch, BrowseComp-ZH, and GAIA demonstrate that Cognitive Scaffold consistently outperforms baselines, achieving 74.7% Avg@3 and 87.0% Pass@3 on Xbench-DeepSearch, 48.5% Avg@3 and 65.9% Pass@3 on BrowseComp-ZH, and 72.8% Avg@3 and 88.3% Pass@3 on GAIA, while reducing compression hallucinations to 5.3%. We open-source our codebase to facilitate future 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.1171ACL Anthology · ACL 2026

Revisiting Metric Reliability for Fine-grained Evaluation of Machine Translation and Summarization in Indian Languages

Amir Hossein Yari Mohamed bin Zayed University of Artificial Intelligence, Kalmit Kulkarni, Ahmad Raza Khan, Fajri Koto Mohamed bin Zayed University of Artificial Intelligence

原文摘要

While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated almost exclusively for English and other high-resource languages. This narrow focus leaves Indian languages—spoken by over 1.5 billion people—largely overlooked, casting doubt on the universality of current evaluation practices. To address this gap, we introduce ITEM, a large-scale benchmark that systematically evaluates the alignment of 26 automatic metrics with human judgments across six major Indian languages, enriched with fine-grained annotations. Our extensive evaluation—covering agreement with human judgments, sensitivity to outliers, language-specific reliability, inter-metric correlations, and resilience to controlled perturbations—reveals four central findings: (1) LLM-based evaluators show the strongest alignment with human judgments at both segment and system levels; (2) outliers exert a significant impact on metric-human agreement; (3) In TS, metrics are more effective at capturing content fidelity, whereas in MT, they better reflect fluency; and (4) Metrics differ in their robustness and sensitivity when subjected to diverse perturbations. Collectively, these findings offer critical guidance for advancing metric design and evaluation in Indian 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.1172ACL Anthology · ACL 2026

Learning to Think on Hypergraph: HyperCoT for Structure-Guided N-ary Knowledge Graph Completion

Mengxue Yang, Jinming Li, Chun Yang, Jiaqi Zhu Institute of Software, Chinese Academy of Sciences, Jiafan Li, Guanhua Zhang Institute of Software Chinese Academy of Sciences, Ying Li University of the Chinese Academy of Sciences

原文摘要

N-ary knowledge graph completion (KGC) aims to infer missing components in facts with multiple entities under distinct semantic roles, commonly formulated as a knowledge hypergraph link prediction task. Most embedding-based approaches score individual hyperedges relying on enriched structural representations, but overlook intermediate propagation states containing complementary local and global structural evidence. Despite their capability to generate chain-of-thought (CoT) representations for the classical KGC task, large language models (LLMs) struggle with hypergraph structure involving multiple facts, while current hypergraph QA methods only provide LLMs with a single query signal rather than path-level evidence. These limitations hinder the transferability of existing methods, especially those leveraging LLMs, to solve the knowledge hypergraph link prediction problem. To bridge this gap, we propose HyperCoT, a structure-aware approach that models multi-hop structural reasoning as a depth-sensitive progressive evidence accumulation process. It constructs a Graphical Chain-of-Thought (Graph-CoT) by aggregating role-aware hyperedge states along strongly correlated reasoning paths, and injects the resulting path-level structural evidence into each token in query and candidate entities to prompt LLMs. Experiments on three real-world datasets demonstrate that HyperCoT consistently outperforms strong n-ary KGC baselines, particularly in high arity and structural sparsity scenarios, meanwhile yielding interpretable multi-hop reasoning traces.

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.1173ACL Anthology · ACL 2026

Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations

Wen Luo Peking University, Guangyue Peng Peking University, Wei Li, Shaohang Wei Peking University, Feifan Song Peking University, Liang Wang Microsoft Research, Nan Yang Microsoft Research Asia, Xingxing Zhang Microsoft Research Asia 等 11 位作者

原文摘要

Despite their impressive capabilities, large language models (LLMs) frequently generate hallucinations. Previous work shows that their internal states encode rich signals of truthfulness, yet the origins and mechanisms of these signals remain unclear. In this paper, we demonstrate that truthfulness cues arise from two distinct information pathways: (1) a Question-Anchored pathway that depends on question–answer information flow, and (2) an Answer-Anchored pathway that derives self-contained evidence from the generated answer itself. First, we validate and disentangle these pathways through attention knockout and token patching. Afterwards, we uncover notable and intriguing properties of these two mechanisms. Further experiments reveal that (1) the two mechanisms are closely associated with LLM knowledge boundaries; and (2) internal representations are aware of their distinctions. Finally, building on these insightful findings, two applications are proposed to enhance hallucination detection performance. Overall, our work provides new insight into how LLMs internally encode truthfulness, offering directions for more reliable and self-aware generative 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.1174ACL Anthology · ACL 2026

LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech

Bingshen Mu, Xian Shi, Xiong Wang Alibaba Group, Hexin Liu, Jin Xu Alibaba Group, Lei Xie Northwest Polytechnical University

原文摘要

Forced alignment (FA) predicts start and end timestamps for words or characters in speech, but existing methods are language-specific and prone to cumulative temporal shifts. The multilingual speech understanding and long-sequence processing abilities of speech large language models (SLLMs) make them promising for FA in multilingual, crosslingual, and long-form speech settings. However, directly applying the next-token prediction paradigm of SLLMs to FA results in hallucinations and slow inference. To bridge the gap, we propose LLM-ForcedAligner, reformulating FA as a slot-filling paradigm: timestamps are treated as discrete indices, and special timestamp tokens are inserted as slots into the transcript. Conditioned on the speech embeddings and the transcript with slots, the SLLM directly predicts the time indices at slots. During training, causal attention masking with non-shifted input and label sequences allows each slot to predict its own timestamp index based on itself and preceding context, with loss computed only at slot positions. Dynamic slot insertion enables FA at arbitrary positions. Moreover, non-autoregressive inference is supported, avoiding hallucinations and improving speed. Experiments across multilingual, crosslingual, and long-form speech scenarios show that LLM-ForcedAligner achieves a 69% 78% relative reduction in accumulated averaging shift compared with prior methods. The checkpoint and inference code will be released later.

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.1175ACL Anthology · ACL 2026

Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens

Seunghee Koh KAIST, Sunghyun Baek, Youngdong Kim Hanbat National University, Junmo Kim Korea Advanced Institute of Science and Technology

原文摘要

Unlearning in large language models (LLMs) has emerged as a promising safeguard against adversarial behaviors. When the forgetting loss is applied uniformly without considering token-level semantic importance, model utility can be unnecessarily degraded. Recent studies have explored token-wise loss regularizers that prioritize informative tokens, but largely rely on ground-truth confidence or external linguistic parsers, which limits their ability to capture contextual information or the model’s overall predictive state. Intuitively, function words like “the” primarily serve syntactic roles and are highly predictable with little ambiguity, but informative words admit multiple plausible alternatives with greater uncertainty. Based on this intuition, we propose Entropy-guided Token Weighting (ETW), a token-level unlearning regularizer that uses entropy of the predictive distribution as a proxy for token informativeness. We demonstrate that informative tokens tend to have higher entropy, whereas structural tokens tend to have lower entropy. This behavior enables ETW to achieve more effective unlearning while better preserving model utility than existing token-level 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.1176ACL Anthology · ACL 2026

Latent-Condensed Transformer for Efficient Long Context Modeling

Zeng You South China University of Technology, Yaofo Chen, Qiuwu Chen, Ying Sun AIGCode, Shuhai Zhang, Yingjian Li, Yaowei Wang Harbin Institute of Technology, Shenzhen and Pengcheng Laboratory, Mingkui Tan

原文摘要

Large language models (LLMs) face significant challenges in processing long contexts due to the linear growth of the key-value (KV) cache and quadratic complexity of self-attention. Existing approaches address these bottlenecks separately: Multi-head Latent Attention (MLA) reduces the KV cache by projecting tokens into a low-dimensional latent space, while sparse attention reduces computation. However, sparse methods cannot operate natively on MLA’s compressed latent structure, missing opportunities for joint optimization. In this paper, we propose Latent-Condensed Attention (LCA), which directly condenses context within MLA’s latent space, where the representation is disentangled into semantic latent vectors and positional keys. LCA separately aggregates semantic vectors via query-aware pooling and preserves positional keys via anchor selection. This approach jointly reduces both computational cost and KV cache without adding parameters. Beyond MLA, LCA’s design is architecture-agnostic and readily extends to other attention mechanisms such as GQA. Theoretically, we prove a length-independent error bound. Experiments show LCA achieves up to 2.5× prefilling speedup and 90% KV cache reduction at 128K context while maintaining competitive 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.1177ACL Anthology · ACL 2026

MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning

Zhihui Chen, Kai He National University of Singapore, Qingyuan Lei, Bin Pu, Jian Zhang, Yuling Xu, Mengling Feng National University of Singapore

原文摘要

Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and safety. Existing defenses are inadequate for healthcare. Medical detectors are largely black-box, while MLLM-based explainers are typically post-hoc, lack medical expertise, and may hallucinate evidence on ambiguous cases. We present MedForge, a data-and-method solution for pre-hoc, evidence-grounded medical forgery detection. We introduce MedForge-90K, a large-scale benchmark of realistic lesion edits across 19 pathologies with expert-guided reasoning supervision via doctor inspection guidelines and gold edit locations. Building on it, MedForge-Reasoner performs localize-then-analyze reasoning, predicting suspicious regions before producing a verdict, and is further aligned with Forgery-aware GSPO to strengthen grounding and reduce hallucinations. Experiments demonstrate state-of-the-art detection accuracy and trustworthy, expert-aligned explanations.

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.1178ACL Anthology · ACL 2026

CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks

Xu Zhang, Hao Li Washington University, Saint Louis, Zhichao Lu City University of Hong Kong

原文摘要

Multimodal Large Language Models (MLLMs) achieve strong reasoning and perception capabilities but are increasingly vulnerable to jailbreak attacks. While existing work focuses on explicit attacks, where malicious content resides in a single modality, recent studies reveal implicit attacks, in which benign text and image inputs jointly express unsafe intent. Such joint-modal threats are difficult to detect and remain underexplored, largely due to the scarcity of high-quality implicit data. We propose ImpForge, an automated red-teaming pipeline that leverages reinforcement learning with tailored reward modules to generate diverse implicit samples across 14 domains. Building on this dataset, we further develop CrossGuard, an intent-aware safeguard providing robust and comprehensive defense against both explicit and implicit threats. Extensive experiments across safe and unsafe benchmarks, implicit and explicit attacks, and multiple out-of-domain settings demonstrate that CrossGuard significantly outperforms existing defenses, including advanced MLLMs and guardrails, achieving stronger security while maintaining high utility. This offers a balanced and practical solution for enhancing MLLM robustness against real-world multimodal threats. Our code is released https://github.com/ZhangXu0963/CrossGuard.

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.1179ACL Anthology · ACL 2026

Can Spectral-Clipping Enable Better Learning While Forgetting Less for Low-Rank Adaptation?

Hyowon Wi Korea Advanced Institute of Science & Technology and Yonsei University, Noseong Park Korea Advanced Institute of Science & Technology

原文摘要

In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning the full set of parameters. In this work, we uncover several key insights regarding the \textit{singular} components of network parameters based on Singular Value Decomposition (SVD).Firstly, the \textit{principal} singular components with large singular values in pre-trained network parameters can be effectively reused during fine-tuning, whereas the \textit{minor} components with smaller singular values are more task-specific and require substantial adaptation. Secondly, we first establish the theoretical connection that the uncontrolled growth of singular values in LoRA adapters leads to the forgetting of pre-trained knowledge — a well-known issue referred to as \textit{catastrophic forgetting}.Building on these observations, we propose \textbf{SCLoRA}, which injects parameterized singular components with spectral clipping into the pre-trained model in a way that is aware of the spectral distribution of the pre-trained model. \textbf{SCLoRA} effectively adapts to new tasks by focusing updates on components that require adaptation, while simultaneously alleviating catastrophic forgetting. We conduct extensive experiments and demonstrate that \textbf{SCLoRA} not only improves downstream performance but also effectively retains pre-trained knowledge.

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.118ACL Anthology · ACL 2026

Thinking beyond the anthropomorphic paradigm benefits LLM research

Lujain Ibrahim, Myra Cheng Stanford University

原文摘要

Anthropomorphism, or the attribution of human traits to technology, is an automatic and unconscious response that occurs even in those with advanced technical expertise. In this position paper, we analyze hundreds of thousands of research articles to present empirical evidence of the prevalence and growth of anthropomorphic terminology in research on large language models (LLMs). We argue for challenging the deeper assumptions reflected in this terminology — which, though often useful, may inadvertently constrain LLM development — and broadening beyond them to open new pathways for understanding and improving LLMs. Specifically, we identify and examine five anthropomorphic assumptions that shape research across the LLM development lifecycle. For each assumption (e.g., that LLMs must use natural language for reasoning, or that they should be evaluated on benchmarks originally meant for humans), we demonstrate empirical, non-anthropomorphic alternatives that remain under-explored yet offer promising directions for LLM research and development.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1180ACL Anthology · ACL 2026

Re^3: Relevance & Recency Retrieval for Mitigating Temporal Hallucination

Jiawei Cao, Jie Ouyang University of Science and Technology of China, Mingyue Cheng University of Science and Technology of China, Zhaomeng Zhou, Chunli Liu Hefei University of Technology, Yupeng Li University of Science and Technology of China, Zirui Liu, Shijin Wang State Key Laboratory of Cognitive Intelligence

原文摘要

Retrieval-Augmented Generation (RAG) is a mainstream approach to mitigating hallucinations in Large Language Models (LLMs), yet in dynamic real-world scenarios, such as weather forecasting or evolving news events, existing retrievers suffer from both temporal-semantic misalignment and outdated-document interference. To address this, we propose Relevance Recency Retrieval (Re^3), a novel framework that mitigates temporal hallucinations via two core components: a Time-Aware Dual Relevance Encoder that embeds heterogeneous temporal signals into the semantic space to ensure retrieval fidelity, and a Conflict-Aware Recency Filter that performs listwise arbitration to identify and suppress obsolete factual versions. To rigorously evaluate this setting, we introduce Re^2 Bench, a large-scale benchmark comprising over 1.3 million instances designed to assess system robustness in realistic environments where temporal constraints and conflicting factual versions coexist. Experiments on three public benchmarks and Re^2 Bench demonstrate that Re^3 consistently outperforms the strongest baselines by an average of 9.7% in generation accuracy, with gains of up to 25.2% on challenging dynamic tasks, while demonstrating robustness across diverse RAG 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.1181ACL Anthology · ACL 2026

TeCES: Collaborative Geometric Knowledge Representation Framework under Evolving Fact Snapshots

Jiujiang Guo, Zhengliang Guo, Kai Wang, Meiyang Wang Hebei University of Science and Technology, Dehua Peng, Shaozu Yuan, Chengyin Hu, Shuan Ai Jilin Business and Technology College 等 9 位作者

原文摘要

Existing knowledge graph completion research is gradually shifting from representing logical semantics of static facts to modeling evolving semantics of temporal facts, yet lacks collaborative modeling of both within a unified framework. To this end, we use concept of snapshots to decompose fact features into two complementary mechanisms: (a) intra-snapshot semantic coupling, where entities and relations exhibit snapshot-specific meanings through multidimensional interactions; (b) trans-snapshot evolutionary synergy, where relations between entities evolve across snapshots and manifest varying states. These snapshot mechanisms jointly reveal underlying logic of facts. To track them, we propose TeCES, a framework for high-fidelity modeling of evolving snapshots. TeCES embeds facts into a 2-grade geometric algebra (GA) system to capture complex semantics via multilevel structures. Temporal information is attached to each entity for mapping into snapshot spaces, while relations and timestamps are reconfigured into composite GA representations. Geometric products enable multidimensional interactions, revealing relation state changes over time. Lastly, the head entity at each snapshot combines with fused temporal-relational representation via geometric product to approximate the target tail entity at multiple levels. Overall, TeCES supports joint modeling of evolving snapshots within a lightweight GA system and significantly outperforms SOTA models on six benchmarks.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1182ACL Anthology · ACL 2026

MicroC-KT: Modeling Community Effect via Learning Micro-Environment for Evidence-Grounded Explainable Knowledge Tracing

Zhiyi Duan Inner Mongolia University, Zixing Shi Inner Mongolia University, Bing Jia Inner Mongolia University, Qi Wang Jilin University

原文摘要

Knowledge Tracing (KT) is essential for tracking students’ evolving knowledge states and predicting their future performance. While current graph-based methods focus on exercise-concept relations, they often overlook the inherent group structures among students. Similarly, emerging LLM-based approaches rely on individual histories, lacking the broader context of group references and contrastive evidence. As a result, existing individual-isolation paradigms fail to provide stable predictions and evidence-based explanations. To bridge this gap, we propose Micro-Community Knowledge Tracing (MicroC-KT), a framework that incorporates learning micro-environments to provide social-cognitive anchors for KT. MicroC-KT identifies latent learning communities via hypergraph modeling and generates dual-granular summaries to facilitate community matching and peer retrieval. By extracting contrastive group evidence, the model prompts an LLM to generate both accurate answer predictions and verifiable analysis reports. Experiments on four public datasets demonstrate that MicroC-KT significantly outperforms state-of-the-art baselines in predictive performance while providing more reliable and evidence-based explanations.

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.1183ACL Anthology · ACL 2026

ImF: Embedding an Implicit Fingerprint in Your Large Language Models

Jiaxuan Wu, Wanli Peng China Agricultural University, Hang fu, Xue Yiming China Agricultural University, Juan Wen China Agricultural University

原文摘要

Training and serving large language models (LLMs) is resource-intensive, making reliable intellectual property (IP) protection and black-box ownership verification increasingly important.Model fingerprinting enables such verification by injecting a small set of secret query–response behaviors, but many existing fingerprints rely on explicit markers or predetermined outputs that are weakly grounded in prompt semantics.This semantic mismatch yields atypical fingerprint responses, reduces stealthiness, and exposes fingerprints to removal by response normalization.We formalize this vulnerability via a new removal attack, Generation Revision Intervention (GRI), which applies system-prompt-level revision and response standardization to steer models toward typical answers, substantially compromising representative injected baselines.To close this semantic gap, we propose the Implicit Fingerprints (ImF): we encode ownership information into a natural-looking target response y via linguistic steganography, then derive a CoT-augmented query x that embeds semantic cues from y to guide the model toward an output sufficiently close to y for decoding-based verification.Experiments on 15 LLMs show that ImF improves stealthiness and remains verifiable under model updates and deployment-time prompt interventions; additional analyses further show stability under common decoding variation and realistic related-model partial 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.1184ACL Anthology · ACL 2026

Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning

Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang 等 10 位作者

原文摘要

Temporal Knowledge Graph Question Answering (TKGQA) is inherently challenging, as it requires sophisticated reasoning over dynamic facts with multi-hop dependencies and complex temporal constraints. Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability. We propose Temp-R1, the first autonomous end-to-end agent for TKGQA trained through reinforcement learning. To address cognitive overload in single-action reasoning, we expand the action space with specialized internal actions alongside external action. To prevent shortcut learning on simple questions, we introduce reverse curriculum learning that trains on difficult questions first, forcing the development of sophisticated reasoning before transferring to easier cases. Our 8B-parameter Temp-R1 achieves state-of-the-art performance on MultiTQ and TimelineKGQA, improving 19.8% over strong baselines on complex questions. Our work establishes a new paradigm for autonomous temporal reasoning agents. The code is available at https://github.com/zjukg/Temp-R1.

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.1185ACL Anthology · ACL 2026

S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA

Minghan Li Soochow University, Junjie Zou, Xinxuan Lv, Chao Zhang, Guodong Zhou Soochow University, China

原文摘要

Retrieval-Augmented Generation (RAG) grounds language models in external evidence, but multi-hop question answering remains difficult because iterative pipelines must control what to retrieve next and when the available evidence is adequate. In practice, systems may answer from incomplete evidence chains, or they may accumulate redundant or distractor-heavy text that interferes with later retrieval and reasoning. We propose S2G-RAG (Structured Sufficiency and Gap-judging RAG), an iterative framework with an explicit controller, S2G-Judge. At each turn, S2G-Judge predicts whether the current evidence memory supports answering and, if not, outputs structured gap items that describe the missing information. We map these gap items into the next retrieval query, producing stable multi-turn retrieval trajectories. To reduce noise accumulation, we maintain a sentence-level Evidence Context by extracting a compact set of relevant sentences from retrieved documents. Experiments on TriviaQA, HotpotQA, and 2WikiMultiHopQA show that S2G-RAG improves multi-hop QA performance and robustness under multi-turn retrieval. Furthermore, S2G-RAG can be integrated into existing RAG pipelines with a lightweight component, without modifying the search engine or retraining the generator.

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.1186ACL Anthology · ACL 2026

Jailbreaking Multimodal Large Language Models using Multi-Clip Video

Choongwon Kang Sung Kyun Kwan University, Seungjong Sun Sung Kyun Kwan University, Hyunmin Jun Sung Kyun Kwan University, Jang Hyun Kim Sung Kyun Kwan University

原文摘要

As multimodal large language models (MLLMs) have advanced to process video inputs, concerns have emerged about their potential for malicious misuse. Prior jailbreak studies have shown that safety alignment in MLLMs can be bypassed through visual inputs, yet it remains unclear which properties of video inputs induce this vulnerability. To address this gap, we introduce Multi-Clip Video (MCV) SafetyBench, a dataset of 2,920 videos designed to evaluate how the diversity of video inputs affects the vulnerability of MLLMs. Each video consists of multiple short clips depicting diverse contexts related to a harmful query. Experiments on eight representative video MLLMs show that attack success consistently increases with the number of clips. Our results further indicate that the video modality is (1) more vulnerable than the image modality, (2) more vulnerable to dynamic videos than to static videos, and (3) more vulnerable when videos contain more diverse contexts. Building on these findings, we propose a defense strategy that leverages the relative robustness of the image modality. Warning: This paper may contain potentially offensive content.

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.1187ACL Anthology · ACL 2026

HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation

Rongxin Chen Chinese Academy of Sciences, Tianyu Wu Institute of Computing Technology, Chinese Academy of Sciences, Bingbing Xu Institute of Computing Technology, Chinese Academy of Sciences, JiaTang Luo, Xiucheng Xu, Huawei Shen Institute of Computing Technology, Chinese Academy of Sciences

原文摘要

High-fidelity agent initialization is crucial for credible Agent-Based Modeling across diverse domains. A robust framework should be Topic-Adaptive, capturing macro-level joint distributions while ensuring micro-level individual rationality. Existing approaches fall into two categories: static data-based retrieval methods that fail to adapt to unseen topics absent from the data, and LLM-based generation methods that lack macro-level distribution awareness, resulting in inconsistencies between micro-level persona attributes and reality. To address these problems, we propose HAG, a Hierarchical Agent Generation framework that formalizes population generation as a two-stage decision process. Firstly, utilizing a World Knowledge Model to infer hierarchical conditional probabilities to construct the Topic-Adaptive Tree, achieving macro-level distribution alignment. Then, grounded real-world data, instantiation and agentic augmentation are carried out to ensure micro-level consistency. Given the lack of specialized evaluation, we establish a multi-domain benchmark and a comprehensive PACE evaluation framework. Extensive experiments show that HAG significantly outperforms representative baselines, reducing population alignment errors by an average of 37.7% and enhancing sociological consistency by 18.8%.

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.1188ACL Anthology · ACL 2026

Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation

Qianchi Zhang, Hainan Zhang, Liang Pang Institute of Computing Technology, Chinese Academy of Sciences, Hong-Wei Zheng, Zhiming Zheng

原文摘要

Retrieval-Augmented Generation (RAG) has become a key paradigm for reducing factual hallucinations in Large Language Models (LLMs), yet little is known about how the order of retrieved documents affects model behavior. We empirically show that under a Top-5 retrieval setting with the gold document included, LLM answers vary substantially across permutations of the retrieved set, even when the gold document is fixed in the first position. This reveals a previously underexplored sensitivity to retrieval permutations. Although existing robust RAG methods focus primarily on enhancing LLM robustness to low-quality retrieval and mitigating positional bias to distribute attention fairly over long contexts, neither approach directly addresses permutation sensitivity. In this paper, we propose Stable-RAG, which exploits permutation sensitivity estimation to mitigate permutation-induced hallucinations. Stable-RAG runs the generator under multiple retrieval orders, clusters hidden states, and decodes from a cluster-center representation that captures the dominant reasoning pattern. It then uses these reasoning results to align hallucinated outputs toward the correct answer, encouraging the model to produce consistent and accurate predictions across document permutations. Experiments on three QA datasets show that Stable-RAG improves answer accuracy, reasoning consistency, and generalization across datasets, retrievers, and input lengths compared with strong baselines.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1189ACL Anthology · ACL 2026

From Logical to Computational Sparsity: Structure-Aware Block-Sparse Attention for Long-Code Completion

Yanli Wang, Yanlin Wang Sun Yat-Sen University, Bowen Zhang, Yiwei Zhang, Daya Guo, Jiachi Chen, Hongyu Zhang Chongqing University, Zibin Zheng Sun Yat-sen University

原文摘要

Code Large Language Models face critical Time-To-First-Token (TTFT) latency challenges when handling long code completion due to the quadratic complexity (O(n^2)) of attention mechanisms. While existing sparse attention methods attempt to address this issue, they suffer from three key limitations: (1) general sparse patterns cause excessive accuracy degradation without considering code structure, (2) code-specific methods achieve only logical sparsity without actual computational speedup, and (3) limited adaptation to complex scenarios such as repository-level completion. We propose SabreCoder, a training-free Structure-aware block-sparse attention mechanism that bridges the gap between logical and computational sparsity. SabreCoder parses code into semantic chunks, constructs chunk-level sparse patterns through dependency analysis and similarity matching, and maps them to GPU-friendly block-sparse formats. Extensive experiments on LCC and CrossCodeEval benchmarks demonstrate that SabreCoder reduces TTFT by 45-55% while maintaining accuracy within 3% of dense attention.

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.119ACL Anthology · ACL 2026

Coordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification

Qisheng Hu Nanyang Technological University, Quanyu Long, Wenya Wang Nanyang Technological University

原文摘要

Multi-hop claim verification is inherently challenging, requiring multi-step reasoning to construct verification chains while iteratively searching for information to uncover hidden bridging facts. This process is fundamentally interleaved, as effective reasoning relies on dynamically retrieved evidence, while effective search demands reasoning to refine queries based on partial information. To achieve this, we propose Hierarchical Agent Reasoning and Information Search (HARIS), explicitly modeling the coordinated process of reasoning-driven searching and search-informed reasoning. HARIS consists of a high-level reasoning agent that focuses on constructing the main verification chain, generating factual questions when more information is needed, and a low-level search agent that iteratively retrieves more information, refining its search based on intermediate findings. This design allows each agent to specialize in its respective task, enhancing verification accuracy and interpretability. HARIS is trained using reinforcement learning with outcome-based rewards. Experimental results on the EX-FEVER and HOVER benchmarks demonstrate that HARIS achieves strong performance, greatly advancing multi-hop claim verification.

cs.CL

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