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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1721ACL Anthology · ACL 2026

MiniRAG: A Lightweight RAG system with Small Language Models

Tianyu Fan, Jingyuan Wang, Xubin Ren, Chao Huang University of Hong Kong

原文摘要

The growing demand for efficient and lightweight Retrieval-Augmented Generation (RAG) systems has highlighted significant challenges when deploying Small Language Models (SLMs) in existing RAG frameworks. Current approaches face severe performance degradation due to SLMs’ limited semantic understanding and text processing capabilities, creating barriers for widespread adoption in resource-constrained scenarios. To address these fundamental limitations, we present MiniRAG, a novel RAG system designed for simplicity and efficiency. MiniRAG introduces two key technical innovations: (1) a semantic-aware heterogeneous graph indexing mechanism that combines text chunks and named entities in a unified structure, reducing reliance on complex semantic understanding, and (2) a lightweight topology-enhanced retrieval approach that leverages graph structures for efficient knowledge discovery without requiring advanced language capabilities. Our extensive experiments demonstrate that MiniRAG achieves comparable performance to LLM-based methods even when using SLMs while requiring only 25% of the storage space. Additionally, we contribute a comprehensive benchmark dataset for evaluating lightweight RAG systems under realistic on-device scenarios with complex queries.

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.1722ACL Anthology · ACL 2026

Glyph: Scaling Context Windows via Visual-Text Compression

Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei The Hong Kong University of Science and Technology, Wenyi Hong Department of Computer Science and Technology, Tsinghua University, Ruiliang Lyu Zhipu AI, Weihan Wang, Zhe Su 等 14 位作者

原文摘要

Large language models (LLMs) conventionally represent text as sequences of discrete tokens, making long-context scaling largely a matter of processing more tokens more efficiently.We instead explore a complementary direction: increasing how much original context each token represents.To this end, we introduce Glyph, a framework that renders long texts into compact visual pages and processes them with a vision-language model (VLM), allowing a fixed context window to cover substantially more text.To make visual compression practical, Glyph combines continual pre-training on rendered long-text data, an LLM-driven genetic search to identify rendering configurations that balance compression and task performance, and post-training with supervised fine-tuning and reinforcement learning.Across multiple long-context benchmarks, Glyph achieves 3–4× token compression while maintaining performance comparable to strong text-only LLMs such as Qwen3-8B, with over 4× faster prefilling and decoding and 2× faster supervised fine-tuning.Under more aggressive compression, a VLM with a 128K context window can handle tasks that would otherwise require up to 1M input tokens.Our code and model are released at https://github.com/thu-coai/Glyph.

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.1723ACL Anthology · ACL 2026

TA-GRPO-d: Trajectory-Aware GRPO for Optimizing Denoising Trajectories in Diffusion LLMs

Gyunyeop Kim Gachon University, Sangwoo Kang Gachon University

原文摘要

Diffusion large language models (dLLMs) generate text by repeatedly unmasking a partially noised sequence in parallel, promising lower latency than autoregressive decoding. However, most discrete dLLMs still rely on fixed denoising schedules, which are non-adaptive to input difficulty and cannot learn efficient unmasking orders. This paper introduces a reinforcement learning (RL) framework that transforms dLLM decoding into a trajectory-aware, learnable policy. We propose a confidence-gated denoising strategy that dynamically decides which tokens to unmask and how many to unmask per step, enabling adaptive exploration of denoising trajectories. Building on Group Relative Policy Optimization, we reformulate it into a trajectory-aware variant, TA-GRPO-d, which combines a trajectory-level signal—captured as the z-score of the AUC over intermediate rewards—with a token-level unmasking-time weight. This design allows the model to learn not only the final output quality but also the efficiency of the decoding path itself. Experiments on MATH-500, Countdown, Sudoku, and code benchmarks (HumanEval, MBPP) show that TA-GRPO-d maintains or improves accuracy while reducing average denoising steps by up to half, achieving both faster inference and lower computational cost. Our approach provides an RL framework for optimizing dLLM decoding policies toward adaptive, efficient reasoning. Code is available at our 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.1724ACL Anthology · ACL 2026

AG-GRPO: Answer-Guided GRPO for Masked Diffusion Language Models

Juhyeong Kim Gachon University, Gyunyeop Kim Gachon University, Sangwoo Kang Gachon University

原文摘要

Reinforcement learning with verifiable rewards (RLVR) typically evaluates only final outcomes, providing limited learning signal about whether the generated reasoning is consistent with the correct answer. As a result, even when ground-truth answers are available during training, on-policy rollouts can repeatedly produce reasoning that is inconsistent with the answer.We propose Answer-Guided Group Relative Policy Optimization (AG-GRPO) for masked diffusion language models (dLLMs), which generate text through iterative masked-token restoration. AG-GRPO combines standard answer-free (AF) rollouts, sampled without access to the ground-truth answer, with answer-guided (AG) rollouts. In AG rollouts, the model generates reasoning conditioned on an anchored ground-truth answer suffix, and then re-predicts the answer from the generated reasoning for reward computation. We compute group-relative advantages over the combined AF/AG rollout set, allowing answer-guided training signals to improve the answer-free policy used at test time.Across mathematics, puzzle-solving, and code-generation benchmarks, AG-GRPO consistently improves over the pretrained dLLM and prior RL method for masked dLLMs. We further analyze optimization dynamics to study how shared group-relative advantages support signal transfer and affect convergence. Our code is available at https://github.com/JuHyng/ag_grpo.

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.1725ACL Anthology · ACL 2026

Grammar as Control: Modular Language Generation for the Long Tail

Ndapa Nakashole

原文摘要

Large language models (LLMs) can, in principle, bootstrap language technologies for long-tail languages due to their pattern recognition capabilities. Yet in practice, without structured guidance, they produce narrow, unrepresentative samples that fail to cover the morphosyntactic space of typologically underrepresented languages.We propose Modular Typology-Informed Generation (mTIG), a prompting framework that transforms descriptive grammars into explicit control mechanisms that guide LLMs to generate typologically balanced synthetic data for downstream training. mTIG decomposes grammars into modular grammar slices, each targeting a specific morphosyntactic phenomenon (e.g., passive voice, causative morphology).Across three low-resource languages, mTIG improves typological entropy by up to 19% and yields a “student-beats-teacher” effect, where distilled models outperform the source LLM by up to +20 chrF in machine translation. These findings show that grammar-as-control can construct training corpora wherever formal linguistic descriptions exist.

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.1726ACL Anthology · ACL 2026

MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks

Junhao Ruan, Abudukeyumu Abudula, Bei Li Meituan, Yongjing Yin Meituan, Xinyu Liu, Kechen Jiao, Xin Chen Meituan, Jingang Wang Meituan 等 11 位作者

原文摘要

Accurate evaluation of conversational retrieval is pivotal for advancing Retrieval-Augmented Generation (RAG) systems. However, existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics. To address these challenges, we introduce MTR-Suite, a unified framework for auditing, synthesizing, and benchmarking retrieval. It features: (1) MTR-Eval, an LLM-based auditor quantifying alignment gaps in previous benchmarks; (2) MTR-Pipeline, a multi-agent system using greedy traversal clustering to generate high-fidelity dialogues at 1/400th human cost; and (3) MTR-Bench, a rigorous general-domain benchmark. MTR-Bench mimics production-style challenges (hard topic switching, verbosity), offering superior discriminative power. We make our code and data publicly available to facilitate future research.

cs.CL · cs.MA

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1727ACL Anthology · ACL 2026

Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning

Weiqin Wang, Yile Wang Shenzhen University, Kehao Chen Fuzhou University, Hui Huang Shenzhen University

原文摘要

Test-time reinforcement learning mitigates the reliance on annotated data by using majority voting results as pseudo-labels, emerging as a complementary direction to reinforcement learning with verifiable rewards (RLVR) for improving reasoning ability of large language models (LLMs). However, this voting strategy often induces confirmation bias and suffers from sparse rewards, limiting the overall performance. In this work, we propose subgroup-specific step-wise confidence-weighted pseudo-label estimation (SCOPE), a framework integrating model confidence and dynamic subgroup partitioning to address these issues. Specifically, SCOPE integrates the proposed step-wise confidence into pseudo-label estimation, prioritizing high-quality reasoning paths over simple frequency count. Furthermore, it dynamically partitions the candidate outputs pool into independent subgroups by balancing reasoning quality against exploration diversity. By deriving local consensus via repeat sampling for each subgroup, SCOPE provides diverse supervision targets to encourage broader exploration. We conduct experiments across various models and benchmarks, experimental results show that SCOPE consistently outperforms recent baselines. Notably, SCOPE achieves relative improvements of 13.1% on challenging AIME 2025 and 8.1% on AMC.

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.1728ACL Anthology · ACL 2026

Beyond Markovian Forgetfulness: Episodic Memory for Reasoning-Intensive Retrieval

Dohyeon Lee Korea Advanced Institute of Science & Technology, Yeonseok Jeong Seoul National University, Seung-won Hwang Seoul National University

原文摘要

Reasoning-intensive information retrieval uses large language models to solve complex queries via multi-step reasoning. However, existing methods have critical limitations. Chain-of-Thought (CoT) approaches suffer from inefficiency, while state-based methods, despite better token efficiency, often fall into reasoning cycles that trap the query refinement process. To address these issues, we propose Episodic Memory for Retrieval (EMR), which enhances the state-based framework with an episodic memory. This module stores the full history of prior states for a query, allowing the model to avoid repetition of such cycles. Experiments on the BRIGHT benchmark show that EMR consistently outperforms both CoT and state-based baselines. Moreover, it is highly token-efficient, reducing token usage by 72% on average. Our results show that episodic memory is an effective and token-efficient mechanism for reasoning-intensive retrieval. The gains also generalize across different base models and stay efficient in terms of end-to-end latency. The code is available in https://github.com/ldilab/EMR.

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.1729ACL Anthology · ACL 2026

DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward

Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang Soochow University, Yixin Ji Soochow University, Qianben Chen ByteDance Inc., Zhe Zhao 等 11 位作者

原文摘要

The ability to model sparse and underspecified rewards, characteristic of human preferences, is fundamental to scaling Reinforcement Learning (RL). Current preference-based reward modeling largely relies on verifiable rewards, where human-annotated labels define rule-based signals. However, these methods face a fundamental bottleneck we term the Matryoshka Doll Problem: a recursive dependency where each reward verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation. In this work, we propose Dual RM, which couples discriminative and generative reward models (DisRMs and GenRMs) under a non-parametric meta-reward. Rather than verifying the correctness of GenRM’s reasoning, the meta-reward evaluates its practical impact on response quality. Specifically, GenRM identifies multi-dimensional evaluation rubrics and iteratively refines the response, while DisRM quantifies the quality shifts induced by each rubric. Furthermore, we implement rubric-based test-time scaling to improve sample efficiency and preference alignment under both DPO and GRPO. Our experiments demonstrate that Dual RM achieves strong performance across major preference benchmarks. Notably, even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench.

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.173ACL Anthology · ACL 2026

FC-TTS: Style and Timbre Control in Zero-Shot Text-to-Speech with Disentangled Speech Representations

Yoonhyung Lee, Hyunsin Park Qualcomm Inc, QualComm, Jinhwan Park Qualcomm Inc, QualComm, Jinkyu Lee

原文摘要

Recent advances in zero-shot text-to-speech (TTS) have enabled accurate imitation of reference speech in terms of both speaking style and speaker timbre. However, achieving disentangled control over these aspects from separate references remains a challenging task. Several studies have proposed disentangled speech representations that decompose speech into interpretable attributes (\textit{e.g.}, timbre, prosody, and content), providing a promising foundation for TTS with attribute control from separate references. Yet, how to effectively integrate such representations into TTS systems to achieve independent and precise control remains underexplored. In this paper, we present FC-TTS, a zero-shot TTS framework that enables disentangled control of style and timbre by conditioning on two distinct reference utterances. Unlike existing systems that inherit limitations from those pre-trained disentangled representations, FC-TTS introduces key design strategies, including architectural choices, training framework, and auxiliary training objectives, which improve the reliability of attribute separation and dual-reference control. Experiments show that FC-TTS achieves high-fidelity synthesis and competitive zero-shot naturalness, while uniquely supporting consistent and independent manipulation of style and timbre. Audio samples are available at https://qualcomm-ai-research.github.io/fc-tts

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.1730ACL Anthology · ACL 2026

Evolutionary Negative Module Pruning for Better LoRA Merging

Anda Cao Zhejiang University, Zhuo Gou Zhejiang University, Yi Wang Zhejiang University, Kaixuan Chen, Yu Wang Zhejiang University, Can Wang, Mingli Song Zhejiang University, Jie Song Zhejiang University

原文摘要

Merging multiple Low-Rank Adaptation (LoRA) experts into a single backbone is a promising approach for efficient multi-task deployment. While existing methods strive to alleviate interference via weight interpolation or subspace alignment, they rest upon the implicit assumption that all LoRA matrices contribute constructively to the merged model. In this paper, we uncover a critical bottleneck in current merging paradigms: the existence of \textit{negative modules}—specific LoRA layers that inherently degrade global performance upon merging. We propose \textbf{E}volutionary \textbf{N}egative \textbf{M}odule \textbf{P}runing (\textbf{ENMP}), a plug-and-play LoRA pruning method to locate and exclude these detrimental modules prior to merging. By leveraging an evolutionary search strategy, ENMP effectively navigates the discrete, non-differentiable landscape of module selection to identify optimal pruning configurations. Extensive evaluations demonstrate that ENMP consistently boosts the performance of existing merging algorithms, achieving a new state-of-the-art across both language and vision domains. Code is available at https://github.com/CaoAnda/ENMP-LoRAMerging.

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.1731ACL Anthology · ACL 2026

Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming

Yuhao Li Beijing University of Posts and Telecommunications, Haifeng Sun, Xuesong Zhang, Shu Yao, Haoyu Zheng, Yvchuan Wang Beijing University of Posts and Telecommunications, Huazheng Wang, Zirui Zhuang Beijing University of Posts and Telecommunications 等 11 位作者

原文摘要

Recent advances in large language models (LLMs) have significantly improved code-generation capabilities, particularly through retrieval-augmented generation (RAG) for private libraries. While RAG leverages API documentation to address the scarcity of private code corpora, its performance critically depends on the quality of retrieved examples. Existing approaches often overlook the intrinsic characteristics of these examples, particularly how factors such as complexity, readability, and correctness impact their effectiveness. In this study, we systematically investigate these three critical aspects—complexity, readability, and correctness—and find that optimal examples should exhibit moderate complexity, semantic correctness, and step-by-step execution patterns. Based on these findings, we propose ComboPrompt, a novel example enhancement method that strategically combines existing API examples to improve complexity, refines code structure for readability, and incorporates automated validation ensuring correctness. Extensive evaluations across five private library benchmarks and different LLMs demonstrate that ComboPrompt achieves up to 22% accuracy improvement over baseline approaches. Code is available at 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.1732ACL Anthology · ACL 2026

Resonating with RoPE: Spectral Quantization for High-Fidelity Key Cache Compression

Xuefei Wang Shandong University, Haoyu Tang Shandong University, Tianyuan Liang Shandong University, Zhibin Wang, Yupeng Hu, Weili Guan

原文摘要

The linear growth of KV cache bottlenecks long-context LLMs, yet RoPE-induced oscillations complicate Key cache quantization. To address this issue, we propose SpectrumQuant, a frequency-domain framework that utilizes the Discrete Cosine Transform (DCT) to convert these oscillations into sparse spectral representations. Specifically, our pipeline integrates dominant frequency extraction, hybrid bit-width allocation, and high-frequency pre-emphasis to maximize fidelity while minimizing memory footprint. To eliminate computational overhead, we develop fused Triton kernels featuring deferred inverse transformation and on-chip sparse accumulation. Extensive experiments on several benchmarks confirm SpectrumQuant achieves efficient compression with performance and latency comparable to FP16 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.1733ACL Anthology · ACL 2026

CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards

Wei Tian, Yuhao Zhou, Man Lan

原文摘要

Large Language Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction. We propose CSRP, a three-stage framework that progressively builds correction capability through Continual Pre-training (CPT) on 5.9M balanced samples to internalize domain knowledge, Chain-of-Thought SFT with explicit error reasoning for diagnostic transparency, and Group Relative Policy Optimization with a novel Efficiency-Aware Reward that explicitly penalizes unnecessary edits. On the NACGEC benchmark, CSRP achieves state-of-the-art performance with 50.99 F_{0.5} and 57.17 precision, substantially outperforming previous best results while effectively mitigating the over-correction bias inherent in MLE-trained models. Our method also advances CSCD spelling correction to 59.61 F1, surpassing GPT-4 by 5.20 points. Comprehensive ablation studies demonstrate that the RL alignment stage contributes a 8% relative gain over the SFT baseline, and that this gain is orthogonal to the contribution of large-scale CPT, validating that explicit optimization for edit efficiency is essential for high-quality grammatical error correction. Our code is available at https://github.com/TW-NLP/ChineseErrorCorrector.

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.1734ACL Anthology · ACL 2026

Adaptive Retrieval for Reasoning

Jongho Kim Seoul National University, Jaeyoung Kim, Jihyuk Kim LG Corporation, Yu Jin Kim LG AI Research, Seung-won Hwang Seoul National University, Moontae Lee LG Corporation and University of Illinois, Chicago

原文摘要

We study leveraging adaptive retrieval to ensure sufficient “bridge” documents are retrieved for reasoning-intensive retrieval. Bridge documents are those that contribute to the reasoning process yet are not directly relevant to the initial query. While existing reasoning-based reranker pipelines attempt to surface these documents in ranking, they suffer from bounded recall. Naive solution with adaptive retrieval into these pipelines often leads to planning error propagation. To address this, we propose REPAIR, a framework that bridges this gap by repurposing reasoning plans as dense feedback signals for adaptive retrieval. Our key distinction is enabling mid-course correction during reranking through selective adaptive retrieval, retrieving documents that support the pivotal plan. Experimental results on reasoning-intensive retrieval and complex QA tasks demonstrate that our method outperforms existing baselines by 5.6%pt.

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.1735ACL Anthology · ACL 2026

On Emergent Social World Models — Evidence for Functional Integration of Theory of Mind and Pragmatic Reasoning in Language Models

Polina Tsvilodub Eberhard-Karls-Universität Tübingen, Jan-Felix Klumpp Eberhard-Karls-Universität Tübingen, Amir Mohammadpour Eberhard-Karls-Universität Tübingen, Jennifer Hu Johns Hopkins University, Michael Franke University of Tuebingen

原文摘要

This paper investigates whether LMs recruit shared computational mechanisms for general Theory of Mind (ToM) and language-specific pragmatic reasoning in order to contribute to the general question of whether LMs may be said to have emergent “social world models”, i.e., representations of mental states that are repurposed across tasks (the functional integration hypothesis). Using behavioral evaluations and causal-mechanistic experiments via functional localization methods inspired by cognitive neuroscience, we analyze LMs’ performance across seven subcategories of ToM abilities (Beaudoin et al., 2020) on a substantially larger localizer dataset than used in prior like-minded work. Results from stringent hypothesis-driven statistical testing offer suggestive evidence for the functional integration hypothesis, indicating that LMs may develop interconnected “social world models” rather than isolated competencies. This work contributes novel ToM localizer data, methodological refinements to functional localization techniques, and empirical insights into the emergence of social cognition in artificial 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.1736ACL Anthology · ACL 2026

ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution

Shouzheng Huang, Meishan Zhang Harbin Institute of Technology (Shenzhen), China, Baotian Hu Harbin Institute of Technology, Min Zhang Harbin Institute of Technology, Shenzhen and Harbin Institute of Technology

原文摘要

Large Language Models (LLMs) enhance their problem-solving capability by utilizing external tools. However, in open-world scenarios with massive and evolving tool repositories, existing methods relying on static embedding retrieval or parameter memorization of tools struggle to align user intent with tool semantics or generalize to unseen tools, respectively, leading to suboptimal accuracy of open-world tool retrieval and execution. To address these, we present ToolOmni, a unified agentic framework that enables LLMs for open-world tool use by proactive retrieval and grounded execution within a reasoning loop. First, we construct a cold-start multi-turn interaction dataset to instill foundational agentic capabilities via Supervised Fine-Tuning (SFT). Then, we introduce open-world tool learning based on a Decoupled Multi-Objective GRPO algorithm, which simultaneously optimizes LLMs for both tool retrieval accuracy and execution efficacy in online environments. Extensive experiments demonstrate that ToolOmni achieves state-of-the-art performance both in retrieval and execution, surpassing strong baselines by a significant margin of +10.8% in end-to-end execution success rate, while exhibiting exceptional robustness and generalization capabilities.

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.1737ACL Anthology · ACL 2026

From TDMA to CDMA: A Multi-bit Watermark for Diffusion Language Models

Baizhou Huang, Xiaojun Wan

原文摘要

While DLMs have emerged as an alternative to ARMs, robust content provenance mechanisms for this architecture remain unexplored. Existing multi-bit watermarking schemes, heavily reliant on the sequential context of ARMs, cannot be directly applied to DLMs. In this paper, we reframe the multi-bit watermarking problem through a novel Digital Signal Processing (DSP) lens. We draw an analogy between prior works and TDMA (Time Division Multiple Access) in telecommunications, revealing their inherent limitations. To overcome these limitations, we introduce CDMArk, the first multi-bit watermarking framework tailored for DLMs, orchestrating a paradigm shift from TDMA to CDMA (Code Division Multiple Access). Our method encodes the entire watermark message across all tokens holographically. We further provide rigorous statistical guarantees for the watermark detection process. Extensive experiments demonstrate that CDMArk achieves a new state-of-the-art Pareto frontier between imperceptibility and effectiveness.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1738ACL Anthology · ACL 2026

LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning

Zerui Chen, Qinggang Zhang Jilin University, Zhishang Xiang, Zhimin Wei, Linfeng Gao, Xiao Huang The Hong Kong Polytechnic University, Zhihong Zhang, Jinsong Su Xiamen University

原文摘要

Graph-based Retrieval-Augmented Generation (GraphRAG) advances flat document retrieval by structuring knowledge as relational graphs, enabling more coherent and effective reasoning. However, applying it to specific domains like legal reasoning faces critical challenges. (i) Legal corpora are heterogeneous, containing multi-granular knowledge from cases, articles and interpretations. A flat knowledge graph cannot adequately differentiate between factual details, applied rules, and abstract principles, limiting accurate retrieval. (ii) Reliable legal judgment demands transparent, evidence-based reasoning. Traditional RAG passes retrieved context directly to an LLM without verification, resulting in opaque, error-prone reasoning. To this end, we propose LegalGraphRAG, a framework designed for reliable legal reasoning. Our approach introduces two core components: a hierarchical legal graph that hierarchically organizes legal sources to enable retrieval at appropriate abstraction levels, and a multi-agent system for reliable legal reasoning, where a Researcher retrieves candidate evidence, an Auditor rigorously verifies its validity against source documents, and an Adjudicator synthesizes the set of verified evidence to render a final judgment. Extensive experiments show that LegalGraphRAG achieves the state-of-the-art performance, outperforming existing GraphRAG baselines in accurate and trustworthy legal analysis. Our code, datasets and implementation details are available at https://github.com/XMUDeepLIT/LegalGraphRAG.

cs.CL · cs.MA

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1739ACL Anthology · ACL 2026

Characterizing the Expressivity of Local Attention in Transformers

Jiaoda Li, Ryan Cotterell

原文摘要

The transformer is the most popular neural architecture for language modeling. The cornerstone of the transformer is its global attention mechanism, which lets the model aggregate information from all preceding tokens before generating the next token. One common variant of attention is called local attention, which restricts each token to aggregating information from a bounded window of predecessors, reducing the quadratic cost of global attention to linear. Although this restriction is usually motivated by efficiency, it has also been found to improve model quality, a phenomenon that has so far lacked a satisfactory explanation. We provide a formal account of this phenomenon in terms of recognizer expressivity. It has been shown that fixed-precision transformers with global attention correspond to a fragment of linear temporal logic containing a single past operator. We additionally prove that adding local attention introduces a second temporal operator, strictly enlarging the class of recognizable regular languages. Moreover, global and local attention are expressively complementary: neither subsumes the other, and combining them yields the richest fragment. Experiments on formal language recognition and natural language modeling corroborate the theory, showing that hybrid global–local transformers outperform their global-only counterparts.

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.174ACL Anthology · ACL 2026

SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation

Shuo Yang, Zheyu Zhang Technical University of Munich, Bardh Prenkaj, Gjergji Kasneci Technische Universität München and University of Tuebingen

原文摘要

Generating high-fidelity synthetic tabular data remains a critical challenge for enhancing data availability in privacy-sensitive and low-resource domains. Recent approaches leverage LLMs by representing table rows as sequences, yet suffer from two fundamental limitations: (1) they model feature dependencies densely, introducing spurious correlations; and (2) they assume static relationships between features, ignoring how these dependencies vary with feature values. To overcome these limitations, we introduce SAGE (Sparse Adaptive Guidance), a novel LLM-based generation framework that enforces sparse and dynamic dependency guidance. SAGE discretizes features into value-aware pseudo-features and constructs a mutual information-based sparse dependency graph. This graph adaptively guides generation through explicit context selection or implicit logit correction, enabling LLMs to focus on truly relevant information during synthesis. Our extensive experiments across six datasets and multiple tasks reveal that SAGE not only improves data fidelity and downstream utility, boosting F1 scores by 10% compared to previous LLM-based methods, but also reduces policy violations by one point. These results highlight the importance of adaptive structure in tabular data generation and provide new insights into context-sensitive control of 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.1740ACL Anthology · ACL 2026

TH-RAG : Topic-Based Hierarchical Knowledge Graphs for Robust Multi-hop Reasoning in Graph-based RAG Systems

JungHyoun Kim, Soohyeong Kim, Seok Jun Hwang, Jeonghyeon Park Hanyang University, Yong Suk Choi

原文摘要

Retrieval-augmented generation (RAG) enables large language models (LLMs) to incorporate external knowledge at inference. Graph-based RAG extends this by organizing corpora into knowledge graphs, improving multi-hop reasoning and offering a global understanding of the corpus. However, triplet-based graphs generated by LLMs are often fragmented and sparsely connected, which reduces coherence and hinders reasoning. Prior enrichment methods such as clustering, community detection, or approximate graph algorithms attempt to restore connectivity but incur high computational cost and risk semantic distortion. To address these issues, we propose TH-RAG, a hierarchical framework that organizes triplets into subtopics and topics, enhancing connectivity, integrating dispersed information, and supporting robust multi-hop reasoning. Experiments on abstractive and specific QA benchmarks show that TH-RAG outperforms strong baselines in accuracy and robustness while remaining efficient, providing a scalable foundation for graph-based RAG.

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.1741ACL Anthology · ACL 2026

Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation

Huije Lee Korea Advanced Institute of Science & Technology, Jisu Shin Korea Advanced Institute of Science & Technology, Hoyun Song Korea Advanced Institute of Science & Technology, Changgeon Ko, Jong C. Park Korea Advanced Institute of Science and Technology

原文摘要

Static benchmarks for harmful content detection face limitations in scalability and diversity, and may also be affected by contamination from web-scale pre-training corpora. To address these issues, we propose a framework for synthesizing harmful content, leveraging persona-guided large language model (LLM) agents. Our approach constructs two-dimensional user personas by integrating demographic identities and topical interests with situational harmful strategies, enabling the simulation of diverse and contextually grounded harmful interactions. We evaluate the framework along three dimensions: harmfulness, challenge level, and diversity. Both human and LLM-based evaluations confirm that our framework achieves a high harmful generation success rate. Experiments across multiple detection systems reveal that our synthetic scenarios are more challenging to detect than those in existing benchmarks. Furthermore, a multi-faceted analysis confirms that our approach achieves linguistic and topical diversity comparable to human-curated datasets, establishing our framework as an effective tool for robust stress-testing of harmful content detection 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.1742ACL Anthology · ACL 2026

DiVE: Decoupling Intra-layer Visual Evidence for Mitigating Hallucinations in Large Vision-Language Models

Xinwei Li, Li Lin Southeast University, Hui Jiao University of Liverpool, Li Yao Southeast University, Tien-Tsin Wong, Hanqian Wu

原文摘要

Recent Large Vision-Language Models (LVLMs) have achieved significant progress yet frequently suffer from visual hallucinations, often stemming from an over-reliance on language priors rather than visual evidence. Existing decoding-based approaches often rely on input perturbations to weaken language priors, but they do not explicitly decouple visual evidence from mixed vision–language representations. To address these limitations, we propose DiVE (Decoupling intra-layer Visual Evidence). DiVE dynamically identifies layers enriched with visual information and performs intra-layer decoupling to extract aggregated visual evidence. By suppressing this evidence to construct a language-prior-dominated reference distribution, DiVE employs contrastive decoding to calibrate the output logits, thereby mitigating hallucinations. Extensive experiments across diverse LVLM architectures demonstrate that DiVE achieves state-of-the-art performance among decoding-based methods on multiple benchmarks. Crucially, it eliminates the latency of an extra forward pass, offering a lightweight and efficient solution.

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.1743ACL Anthology · ACL 2026

Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents

Muyu He Collinear AI, Anand Kumar Collinear AI, Soumyadeep Bakshi, James Zou Stanford University, Nazneen Rajani

原文摘要

Despite rapid progress in building conversational AI agents, robustness is still largely untested. Small shifts in user behavior, such as being more impatient, incoherent, or skeptical, can cause sharp drops in agent performance, revealing how brittle current AI agents are. Today’s benchmarks fail to capture this fragility: agents may perform well under standard evaluations but degrade spectacularly in more realistic and varied settings. We address this robustness testing gap by introducing TraitBasis, a lightweight, model-agnostic method for systematically stress testing AI agents. TraitBasis learns directions in activation space corresponding to steerable user traits (e.g., impatience or incoherence), which can be controlled, scaled, composed, and applied at inference time without any fine-tuning or extra data. Using TraitBasis, we extend τ-Bench to τ-bench, where user behaviors are altered via controlled trait vectors. We observe an average 4%–20% performance degradation on τ-bench across frontier models, highlighting the lack of robustness of current AI agents to variations in user behavior. Together, these results highlight both the critical role of robustness testing and the promise of TraitBasis as a simple, data-efficient, and compositional tool. By powering simulation-driven stress tests and training loops, TraitBasis opens the door to building AI agents that remain reliable in the unpredictable dynamics of real-world human interactions. We plan to open-source τ-bench, across four domains: airline, retail, telecom, and telehealth, so the community can systematically QA their agents under realistic, behaviorally diverse intents and trait scenarios.

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.1744ACL Anthology · ACL 2026

Efficient Process Reward Modeling via Contrastive Mutual Information

Nakyung Lee, Sangwoo Hong Konkuk University, Jungwoo Lee Seoul National University

原文摘要

Recent research has devoted considerable effort to verifying the intermediate reasoning steps of chain-of-thought (CoT) trajectories using process reward models (PRMs) and other verifier models. However, training a PRM typically requires human annotators to assign reward scores to each reasoning step, which is both costly and time-consuming. Existing automated approaches, such as Monte Carlo (MC) estimation, also demand substantial computational resources due to repeated LLM rollouts. To overcome these limitations, we propose contrastive pointwise mutual information (CPMI), a novel automatic reward labeling method that leverages the model’s internal probability to infer step-level supervision while significantly reducing the computational burden of annotating dataset. CPMI quantifies how much a reasoning step increases the mutual information between the step and the correct target answer relative to hard-negative alternatives. This contrastive signal serves as a proxy for the step’s contribution to the final solution and yields a reliable reward. The experimental results show that CPMI-based labeling reduces dataset construction time by 84% and token generation by 98% compared to MC estimation, while achieving higher accuracy on process-level evaluations and mathematical reasoning 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.1745ACL Anthology · ACL 2026

R^3: End-to-End Reasoning-based Planning for Multi-step Retrosynthesis via Reinforcement Learning

YiFei Wang, Qizhi Pei, Jiangtao Feng, Yuntian Shi, Yi Duan, Lihao Wang, Lei Bai Shanghai AI Laboratory, Lijun Wu Shanghai Artificial Intelligence Laboratory 等 10 位作者

原文摘要

Multi-step retrosynthetic planning is a fundamental challenge in organic chemistry, traditionally modeled as a combinatorial search problem guided by single-step prediction models. However, this search-centric paradigm often disconnects from the explicit chemical reasoning processes employed by human experts. In this paper, we propose R^3 (Reinforced Reasoning Retrosynthesis), a novel framework that reformulates this task as end-to-end generative reasoning. Instead of traversing a search tree, R^3 simulates the problem-solving logic of chemists to directly generate complete synthetic pathways. To achieve this, we initialize the model with domain knowledge and employ end-to-end Reinforcement Learning (RL) to optimize the entire planning policy. Experimental results on Retrobench show that R^3 achieves a state-of-the-art Top-1 accuracy of 43.7%, demonstrating that generative reasoning offers a superior alternative to traditional search algorithms in solving complex retrosynthetic problems.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1746ACL Anthology · ACL 2026

River-LLM: Large Language Model Seamless Exit Based on KV Share

Yingtao Shen, An Zou Shanghai Jiaotong University

原文摘要

Large Language Models (LLMs) have demonstrated exceptional performance across diverse domains but are increasingly constrained by high inference latency. Early Exit has emerged as a promising solution to accelerate inference by dynamically bypassing redundant layers. However, in decoder-only architectures, the efficiency of Early Exit is severely bottlenecked by the KV Cache Absence problem, where skipped layers fail to provide the necessary historical states for subsequent tokens. Existing solutions, such as recomputation or masking, either introduce significant latency overhead or incur severe precision loss, failing to bridge the gap between theoretical layer reduction and practical wall-clock speedup. In this paper, we propose River-LLM, a training-free framework that enables seamless token-level Early Exit. River-LLM introduces a lightweight KV-Shared Exit River that allows the backbone’s missing KV cache to be naturally generated and preserved during the exit process, eliminating the need for costly recovery operations. Furthermore, we utilize state transition similarity within decoder blocks to predict cumulative KV errors and guide precise exit decisions. Extensive experiments on mathematical reasoning and code generation tasks demonstrate that River-LLM achieves 1.71\times to 2.16\times practical speedup while maintaining high generation quality.

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.1747ACL Anthology · ACL 2026

Execution as Verification: Fine-Grained Self-Correcting Reasoning for Complex KBQA

Minghan Zhang, Zhen Yang, Haodong Zou, Jie Chen Anhui University, Zhen Duan Anhui University, Shu Zhao Anhui University

原文摘要

Knowledge Base Question Answering (KBQA) leverages structured knowledge bases to offer superior interpretability and hallucination resistance, making it a critical technology for precise knowledge reasoning. However, the prevailing LLM-based generate-then-execute formulation of semantic parsing is limited by strict syntactic constraints, making it primarily prone to structural deviations that render queries unexecutable, while suffering from semantic deviations that yield incorrect execution results. To address these challenges, we propose the Execution as Verification (EVER) framework, reframing semantic parsing as an iterative, self-correcting reasoning process driven by execution feedback. First, motivated by the insight that query executability serves as a strong proxy for answer correctness, we introduce Fine-Grained Execution-Aware Planning. This mechanism decomposes complex semantic parsing into a sequence of stepwise reasoning processes oriented by executability verification, ensuring high query executability. We further design a Self-Guided Semantic Correction mechanism based on execution result verification, utilizing execution feedback to verify and calibrate semantic deviations, thereby ensuring the semantic correctness of executable queries. Experimental results on the WebQSP and CWQ datasets demonstrate that our method achieves significant improvements in both query executability and answer accuracy, achieving state-of-the-art performance, particularly in complex multi-hop scenarios. Our code is available at https://github.com/ahu-zmh/EVER.

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.1748ACL Anthology · ACL 2026

Zero-Shot Detection of LLM-Generated Text using Temperature Sensitivity

Shixuan Ma Beijing University of Posts and Telecommunications, Jiahao Li University of Science and Technology of China, Zhendong Mao University of Science and Technology of China, Quan Wang Beijing University of Posts and Telecommunications

原文摘要

The widespread deployment of Large Language Models (LLMs) has spurred significant progress in the detection of LLM-generated text. However, existing detection methods often rely on statistical features that are insufficient for reliable detection; for example, even though LLM-generated and human-written texts exhibit different probability distributions in surrogate models, they can produce nearly identical entropy values, thereby conflating the two types of text. In this paper, we propose that modulating the decoding temperature and monitoring how the probability distributions respond can better probe the intrinsic discrepancies between two types of text. Building upon this insight, we introduce a new feature termed Temperature Sensitivity (TS) and demonstrate that LLM-generated text tends to exhibit higher TS than human-written text. Finally, we propose NTS, a novel and simple zero-shot detector built upon normalized temperature sensitivity. Extensive experiments across three datasets, multiple domains, and various source models demonstrate the superior effectiveness and robustness of our proposed approach. Code avaliable at: https://github.com/Shixuan-Ma/NTS.

cs.CL

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