跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1030新增论文
+790其中 AI 相关
+128已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-12 02:50 · 全站最近入库：2026-11-11 22:47各信源进度 →

统计口径与数据新鲜度

统计 2026-11-11 03:42 — 2026-11-12 03:42（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-12 02:50最近发现新增版本：2026-11-11 22:47库内论文最新更新时间：2026-11-11 08:00

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

21,673 已收录论文18,321 AI 相关6,056 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 5,654 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-12 02:48（北京时间）
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.727ACL Anthology · ACL 2026

Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems

Shuhua Yang, Jiahao Zhang, Yilong Wang, Dongwon Lee The Pennsylvania State University, Suhang Wang Pennsylvania State University

原文摘要

Graph-based retrieval-augmented generation (GraphRAG) systems construct knowledge graphs over document collections to support multi-hop reasoning. While prior work shows that GraphRAG responses may leak retrieved subgraphs, the feasibility of query-efficient reconstruction of the hidden graph structure remains unexplored under realistic query budgets. We study a budget-constrained black-box setting where an adversary adaptively queries the system to steal its latent entity–relation graph. We propose AGEA (Agentic Graph Extraction Attack), a framework that leverages a novelty-guided exploration–exploitation strategy, external graph memory modules, and a two-stage graph extraction pipeline combining lightweight discovery with LLM-based filtering. We evaluate AGEA on medical, agriculture, and literary datasets across Microsoft-GraphRAG and LightRAG systems. Under identical query budgets, AGEA significantly outperforms prior attack baselines, recovering up to 90% of entities and relationships while maintaining high precision. These results demonstrate that modern GraphRAG systems are highly vulnerable to structured, agentic extraction attacks, even under strict query limits. The code is available at https://github.com/shuashua0608/AGEA.

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.728ACL Anthology · ACL 2026

Function Words as Statistical Cues for Language Learning

Xiulin Yang, Heidi R. Getz Georgetown University, Ethan Gotlieb Wilcox

原文摘要

What statistical properties might support learning abstract grammatical knowledge from linear input? We address this question by examining the statistical distribution of function words. Function words have been argued to aid acquisition through three distributional properties: high frequency, reliable syntactic association, and phrase-boundary alignment. We conduct a cross-linguistic corpus analysis of 186 languages, which confirms that all three properties are universal. Using counterfactual language modeling and ablation experiments on English, we show that preserving these properties facilitates acquisition in neural learners, with a Goldilocks effect: function words must be frequent enough to be reliable, yet diverse enough to remain informative to structural dependency. Probing analyses further reveal that different learning conditions produce systematically different reliance on function words.

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.729ACL Anthology · ACL 2026

Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors

Raoyuan Zhao Ludwig-Maximilians-Universität München, Yihong Liu Ludwig-Maximilians-Universität München, Lena Altinger München Klinik gGmbH, Hinrich Schuetze, Michael A. Hedderich Ludwig-Maximilians-Universität München

原文摘要

Large language models (LLMs) are increasingly deployed in multilingual, real-world applications with user inputs – naturally introducing typographical errors (typos). Yet most benchmarks assume clean input, leaving the robustness of LLMs to typos across languages largely underexplored. To address this gap, we introduce MulTypo, a multilingual typo generation algorithm that simulates human-like errors based on language-specific keyboard layouts and typing behavior. We evaluate 18 open-source LLMs across three model families and five downstream tasks spanning language inference, multi-choice question answering, mathematical reasoning, and machine translation tasks. Our results show that typos consistently degrade performance, particularly in generative tasks and those requiring reasoning – while the natural language inference task is comparatively more robust. Instruction tuning improves clean-input performance but may increase brittleness under noise. We also observe language-dependent robustness: high-resource languages are generally more robust than low-resource ones, and translation from English is more robust than translation into English. Our findings underscore the need for noise-aware training and multilingual robustness evaluation. We release a Python package for MulTypo and make the source code publicly available at https://github.com/cisnlp/multypo.

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.73ACL Anthology · ACL 2026

Logic Matters in Lightweight Hallucination Classification for RAG System

Ningyuan Yang, Kaizhu Huang Duke Kunshan University and Xi'an Jiaotong-Liverpool University

原文摘要

We propose a lightweight, modular framework for hallucination detection in Retrieval-Augmented Generation (RAG) systems, addressing the critical challenge where logical dependencies span across fragmented retrieval results. To address the inherent limitations of compact models in processing long-context information and performing multi-hop reasoning, our approach systematically analyzes the logical relationships among retrieved documents within the vector space. By capturing these geometric patterns through a novel feature extraction framework, the proposed classifier significantly enhances context-aware hallucination detection without requiring complex architectures or pre-training on datasets. Meanwhile, to evaluate multi-document reasoning, we release HotPotQA-derived, a hallucination dataset preserving separate retrieved texts. Experimental results on HotPotQA-derived and several open-source datasets demonstrate that our framework can achieve results comparable to or even surpassing those of large language models (LLMs) on the task of hallucination detection.

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.730ACL Anthology · ACL 2026

Listening Like Humans: Semantics-Guided Noise-Robust Multimodal Speech Recognition

Yan Fang The Hong Kong University of Science and Technology, Jun Chen The Hong Kong University of Science and Technology (Guangzhou), Yian Yao, Shuxin Zhong The Hong Kong University of Science and Technology, Min Sun China Mobile Communications Company, Kaishun Wu

原文摘要

Severe acoustic degradation is often caused by overlapping noise, disfluencies, and environmental distortions. This phenomenon results in the dissolution of linguistic structures and the generation of unreliable ASR outputs. Inspired by human speech comprehension, we propose Speech-MLM, a novel multimodal framework that reframes ASR as semantics-guided speech reconstruction. This perspective introduces three core challenges: (C1) collapse of linguistic structure under acoustic degradation, (C2) semantic ambiguity under noise, and (C3) misalignment across modalities. To address these issues, we propose Speech-MLM, a multimodal ASR framework that integrates speech, spectrogram-derived visual cues, and textual variants to enhance robustness. It consists of: (i) Cognitive Structure Extractor that recovers prosodic structure from visualized acoustic features, (ii) Semantic Weaver that learns semantic equivalence across varied textual forms, and (iii) Retrieval-Guided Fusion Learner that unifies modalities within a shared semantic space. Experiments on multiple real-world noisy datasets demonstrate that Speech-MLM achieves an average 38.85% reduction in WER, while also attaining 98.71% BERTScore and 96.7% USE, over advanced baselines, demonstrating substantial gains in semantic robustness and generalization across domains.

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.731ACL Anthology · ACL 2026

Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation

Abdullah Mazhar, Het Riteshkumar Shah, Aseem Srivastava Mohamed bin Zayed University of Artificial Intelligence, Smriti Joshi, Md Shad Akhtar Indraprastha Institute of Information Technology, Delhi

原文摘要

The increasing use of large language models in mental health applications calls for principled evaluation frameworks that assess alignment with psychotherapeutic best practices beyond surface-level fluency. While recent systems exhibit conversational competence, they lack structured mechanisms to evaluate adherence to core therapeutic principles. In this paper, we study the problem of evaluating AI-generated therapist-like responses for clinically grounded appropriateness and effectiveness. We assess each therapists utterance along six therapeutic principles: non-judgmental acceptance, warmth, respect for autonomy, active listening, reflective understanding, and situational appropriateness using a fine-grained ordinal scale. We introduce FAITH-M, a benchmark annotated with expert-assigned ordinal ratings, and propose CARE, a multi-stage evaluation framework that integrates intra-dialogue context, contrastive exemplar retrieval, and knowledge-distilled chain-of-thought reasoning. Experiments show that CARE achieves an F-1 score of 63.34 versus the strong baseline Qwen3 F-1 score of 38.56 which is a 64.26% improvement, which also serves as its backbone, indicating that gains arise from structured reasoning and contextual modeling rather than backbone capacity alone. Expert assessment and external dataset evaluations further demonstrate robustness under domain shift, while highlighting challenges in modeling implicit clinical nuance. Overall, CARE provides a clinically grounded framework for evaluating therapeutic fidelity in AI mental health 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.732ACL Anthology · ACL 2026

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

Erel Kaplan, Tomer Bitan, Lian Ghrayeb, Le Chen Argonne National Laboratory, Tom Yotam Code Metal, Niranjan Hasabnis Codemetal AI, Gal Oren Stanford University and Computer Science Department, Technion - Israel Institute of Technology

原文摘要

Parallel programming is central to HPC and AI, but producing code that is correct and fast remains challenging, especially for OpenMP GPU offload, where data movement and tuning dominate. Autonomous coding agents can compile, test, and profile on target hardware, but outputs are brittle without domain scaffolding.We present ParaCodex, an HPC-engineer workflow that turns a Codex-based agent into an autonomous OpenMP GPU offload system using staged hotspot analysis, explicit data planning, correctness gating, and profiling-guided refinement. We evaluate translation from serial CPU kernels to OpenMP GPU offload kernels on HeCBench, Rodinia, and NAS. After excluding five kernels, ParaCodex succeeded on all 31 valid kernels. In 27/31 (87%) of these valid cases, the generated kernels improved GPU time over reference implementations, a result that holds independently on both the A100 and RTX 4060. The resulting OpenMP kernels achieve geometric-mean speedups of 3.1 (A100) and 3.6 (RTX 4060) on HeCBench and 1.5 and 1.1 on Rodinia, and outperform a zero-shot Codex baseline on all suites. We also evaluate CUDA -> OpenMP offload translation on ParEval, where ParaCodex maintains high compilation and validation rates in code-only and end-to-end settings.ParaCodex is available at https://github.com/Scientific-Computing-Lab/ParaCodex

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.733ACL Anthology · ACL 2026

CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training

Qi Li, Cheng-Long Wang King Abdullah University of Science and Technology, Yinzhi Cao Johns Hopkins University, Di Wang KAUST

原文摘要

Training models on a carefully chosen portion of data rather than the full dataset is now a standard preprocess for modern ML. From vision coreset selection to large-scale filtering in language models, it enables scalability with minimal utility loss. A common intuition is that training on fewer samples should also reduce privacy risks. In this paper, we challenge this assumption. We show that subset training is not privacy free: the very choices of which data are included or excluded can introduce new privacy surface and leak more sensitive information. Such information can be captured by adversaries either through side-channel metadata from the subset selection process or via the outputs of the target model. To systematically study this phenomenon, we propose CoLA (Choice Leakage Attack), a unified framework for analyzing privacy leakage in subset selection. In CoLA, depending on the adversary’s knowledge of the side-channel information, we define two practical attack scenarios: Subset-aware Side-channel Attacks and Black-box Attacks. Under both scenarios, we investigate two privacy surfaces unique to subset training: (1) Training-membership MIA (TM-MIA), which concerns only the privacy of training data membership, and (2) Selection-participation MIA (SP-MIA), which concerns the privacy of all samples that participated in the subset selection process. Notably, SP-MIA enlarges the notion of membership from model training to the entire data-model supply chain. Experiments on vision and language models show that existing threat models underestimate subset-training privacy risks: the expanded privacy surface leaks both training and selection membership, extending risks from individual models to the broader ML ecosystem.

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.734ACL Anthology · ACL 2026

MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs

Zhan Qu Technische Universität Dresden; ScaDS.AI, Michael Färber Technische Universität Dresden

原文摘要

Large Language Models (LLMs) are increasingly applied to medicine, yet their adoption is limited by concerns over reliability and safety. Existing evaluations either test factual medical knowledge in isolation or assess patient-level reasoning without verifying correctness, leaving a critical gap. We introduce MediEval, a benchmark that links MIMIC-IV electronic health records (EHRs) to a unified knowledge base built from UMLS and other biomedical vocabularies. MediEval generates diverse factual and counterfactual medical statements within real patient contexts, enabling systematic evaluation across a 4-quadrant framework that jointly considers knowledge grounding and contextual consistency. Using this framework, we identify critical failure modes, including hallucinated support and truth inversion, that current proprietary, open-source, and domain-specific LLMs frequently exhibit. To address these risks, we propose Counterfactual Risk-Aware Fine-tuning (CoRFu), a DPO-based method with an asymmetric penalty targeting unsafe confusions. CoRFu improves by +16.4 macro-F1 points over the base model and eliminates truth inversion errors, demonstrating both higher accuracy and substantially greater safety.

cs.CL

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.735ACL Anthology · ACL 2026

The Path Not Taken: Duality in Reasoning about Program Execution

Eshgin Hasanov University of Central Florida, Md Mahadi Hassan Sibat University of Central Florida, Santu Karmaker, Aashish Yadavally University of Central Florida

原文摘要

Large language models (LLMs) have shown remarkable capabilities across diverse coding tasks. However, their adoption requires a true understanding of program execution rather than relying on surface-level patterns. Existing benchmarks primarily focus on predicting program properties tied to specific inputs (e.g., code coverage, program outputs). As a result, they provide a narrow view of dynamic code reasoning and are prone to data contamination. We argue that understanding program execution requires evaluating its inherent duality through two complementary reasoning tasks: (i) predicting a program’s observed behavior for a given input, and (ii) inferring how the input must be mutated toward a specific behavioral objective. Both tasks jointly probe a model’s causal understanding of execution flow. We instantiate this duality in DexBench, a benchmark comprising 445 paired instances, and evaluate 13 LLMs. Our results demonstrate that dual-path reasoning provides a robust and discriminative proxy for dynamic code understanding.

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.736ACL Anthology · ACL 2026

Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs

Myra Cheng Stanford University, Robert D. Hawkins, Dan Jurafsky Stanford University

原文摘要

Recent evaluations show that large language models (LLMs) frequently fail to challenge users’ harmful beliefs in domains ranging from medical advice to social reasoning. We present a unifying analysis through the lens of pragmatics: these safety failures can be understood and addressed as LLMs exhibiting excessive accommodation and insufficient epistemic vigilance. We show that the pragmatic factors affecting accommodation and epistemic vigilance in humans (at-issueness, linguistic encoding, and source reliability) influence LLM behaviors in similar ways. We demonstrate how these factors explain performance differences across three safety benchmarks that test models’ ability to challenge harmful beliefs, spanning misinformation (Cancer-Myth, SAGE-Eval) and sycophancy (ELEPHANT). This pragmatic lens further motivates prompting interventions, such as adding the phrase “wait a minute”, that drastically improve performance on these difficult benchmarks by shifting pragmatic cues. Our results have practical implications for benchmark design and underscore the importance of pragmatics for understanding model behavior and improving 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.737ACL Anthology · ACL 2026

Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition

Tiejin Chen Arizona State University, Huaiyuan Yao, Jia Chen University of California, Riverside, Evangelos E. Papalexakis University of California, Riverside, Hua Wei Arizona State University

原文摘要

While Large Language Model-based Multi-Agent Systems (MAS) consistently outperform single-agent systems on complex tasks, their intricate interactions introduce critical reliability challenges arising from communication dynamics and role dependencies. Existing Uncertainty Quantification methods, typically designed for single-turn outputs, fail to address the unique complexities of the MAS. Specifically, these methods struggle with three distinct challenges: the cascading uncertainty in multi-step reasoning, the variability of inter-agent communication paths, and the diversity of communication topologies. To bridge this gap, we introduce MATU, a novel framework that quantifies uncertainty through tensor decomposition. MATU moves beyond analyzing final text outputs by representing entire reasoning trajectories as embedding matrices and organizing multiple execution runs into a higher-order tensor. By applying tensor decomposition, we disentangle and quantify distinct sources of uncertainty, offering a comprehensive reliability measure that is generalizable across different agent structures. We provide comprehensive experiments to show that MATU effectively estimates holistic and robust uncertainty across diverse tasks and communication topologies.

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.738ACL Anthology · ACL 2026

Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities

Changdae Oh University of Wisconsin - Madison, Seongheon Park Department of Computer Science, University of Wisconsin - Madison, To Eun Kim Carnegie Mellon University, Jiatong Li University of Wisconsin-Madison, Wendi Li, Samuel Yeh, Sean Du, Hamed Hassani University of Pennsylvania 等 11 位作者

原文摘要

Uncertainty quantification (UQ) for large language models (LLMs) is a key building block for safety guardrails of daily LLM applications. Yet, even as LLM agents are increasingly deployed in highly complex tasks, most UQ research still centers on single-turn question-answering. We argue that UQ research must shift to realistic settings with interactive agents, and that a new principled framework for agent UQ is needed. This paper presents three pillars to build a solid ground for future agent UQ research: (1. Foundations) We present the first general formulation of agent UQ that subsumes broad classes of existing UQ setups; (2. Challenges) We identify four technical challenges specifically tied to agentic setups—selection of uncertainty estimator, uncertainty of heterogeneous entities, modeling uncertainty dynamics in interactive systems, and lack of fine-grained benchmarks—with numerical analysis on a real-world agent benchmark, \tau^2-bench; (3. Future Directions) We conclude with noting on the practical implications of agent UQ and remaining open problems as forward-looking discussion for future explorations.

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.739ACL Anthology · ACL 2026

Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence

Amirhosein Ghasemabadi Huawei Technologies Ltd., Keith G. Mills Louisiana State University, Baochun Li University of Toronto, Di Niu University of Alberta

原文摘要

Test-Time Scaling (TTS) methods for enhancing Large Language Model (LLM) reasoning often incur substantial inference costs, due to reliance on long chain-of-thought (CoT) generation, self-consistency sampling methods, or searching under Process Reward Models (PRMs). This paper introduces Guided by Gut (GG), an efficient self-guided TTS framework that enables LLMs to perform step-by-step reasoning at a low cost, without any reward models or verifiers. GG performs a lightweight tree search guided solely by intrinsic confidence signals of the LLM at each reasoning step and improves the reliability of such internal confidence signals by reinforcement learning. Empirical evaluations on challenging mathematical reasoning benchmarks demonstrate that GG enables smaller models (e.g., 1.5B-7B parameters) to achieve accuracy matching or surpassing significantly larger models (e.g., 32B–70B parameters), while reducing GPU memory usage by up to 10×. Compared to TTS with PRMs, GG achieves comparable accuracy with 8× faster inference speeds and 4–5× lower memory usage. Additionally, GG reduces KV cache memory usage by approximately 50% compared to Best-of-N sampling, facilitating more efficient and practical deployment of TTS techniques.

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.74ACL Anthology · ACL 2026

Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning

Shuyao Xu University of Virginia and National University of Singapore, Cheng Peng, Jiangxuan Long University of Hong Kong, Weidi Xu Infly Technology, Wei Chu Inf Tech, Yuan Qi Fudan University

原文摘要

Recent advances in model distillation show that data from advanced reasoning models can effectively train smaller student models. However, standard practices discard incorrect reasoning traces—valuable, yet underutilized data. This paper addresses the critical question: How can both positive and negative distilled reasoning traces be effectively leveraged to maximize LLM reasoning performance in an offline setting? We employ a two-stage training recipe: first, Supervised Fine-Tuning (SFT) on positive traces, followed by a refinement stage using both positive and negative traces. We find that a simple REINFORCE-style objective, which we term the Reinforcement Distillation (REDI) objective, outperforms established preference optimization methods like DPO and SimPO in this distillation context. Our empirical evaluations demonstrate the effectiveness of this approach. Notably, our Qwen-REDI-1.5B model, trained on just 131k traces from the open Open-R1 dataset, achieves an 83.1% score on MATH-500. Its performance matches that of DeepSeek-R1-Distill-Qwen-1.5B, a model trained on 800k proprietary data. This result showcases the remarkable data efficiency of utilizing previously discarded negative 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.740ACL Anthology · ACL 2026

Knowing When Not to Answer: Lightweight KB-Aligned OOD Detection for Safe RAG

Ilias Triantafyllopoulos New York University, Renyi Qu Microsoft, Salvatore Giorgi National Institutes of Health, Brenda Curtis National Institute on Drug Abuse/NIH, Lyle Ungar University of Pennsylvania, João Sedoc New York University

原文摘要

Retrieval-Augmented Generation (RAG) systems are increasingly deployed in high-stakes domains, where safety depends not only on how a system answers, but also on whether a query should be answered given a knowledge base (KB). Out-of-domain (OOD) queries can cause dense retrieval to surface weakly related context and lead the generator to produce fluent but unjustified responses. We study lightweight, KB-aligned OOD detection as an always-on gate for RAG systems. Our approach applies PCA to KB embeddings and scores queries in a compact subspace selected either by explained-variance retention (EVR) or by a separability-driven -test ranking. We evaluate geometric semantic-search rules and lightweight classifiers across 16 domains, including high-stakes COVID-19 and Substance Use KBs, and stress-test robustness using both LLM-generated attacks and an in-the-wild 4chan attack. We find that low-dimensional detectors achieve competitive OOD performance while being faster, cheaper, and more interpretable than prompted LLM-based judges. Finally, human and LLM-based evaluations show that OOD queries primarily degrade the relevance of RAG outputs, highlighting the need for efficient external OOD detection to maintain safe, in-scope behavior.

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.741ACL Anthology · ACL 2026

Guidelines as Environments: A World Model Approach to Rule Following

Haiqing Li, Wenliang Zhong University of Texas at Arlington, Yinhao Wu, Hehuan Ma University of Texas at Arlington, Yuzhi Guo University of Texas at Arlington, Thao M. Dang University of Texas at Arlington, Junzhou Huang University of Texas, Arlington

原文摘要

Guideline-following is increasingly important in compliance, customer support, and other regulated workflows, where correctness is defined by explicit rule systems rather than heuristics. Learning to follow guidelines is challenging because guidelines are interdependent: rules can trigger, suppress, or conflict with one another, while locally plausible responses may violate global constraints. Most existing methods treat guidelines as static text and rely on implicit reasoning or deeper decoding, making rule interactions and satisfaction status hard to observe and control. A more feasible approach is to model guideline execution with an explicit state that tracks evolving rule evidence across steps. However, conventional world models are a poor fit: they typically assume privileged feedback or well-defined transition dynamics, assumptions that do not hold when reasoning occurs purely in language space under ambiguous, text-defined constraints. As a solution, we propose RGCWM, a Rule-Grounded Causal World Model that builds an explicit state space from the guideline text itself. RGCWM represents rule applicability and satisfaction as a continuously updated evidence state, externalizes inter-rule dependencies as a causal structure, and plans at inference time by counterfactually evaluating candidate responses under model-estimated state transitions. Experiments show that this shift from implicit text reasoning to state-based reasoning enables stable, controllable execution of complex interacting rules across diverse domains.

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.742ACL Anthology · ACL 2026

Cross-Modal Taxonomic Generalization in (Vision-) Language Models

Tianyang Xu Toyota Technological Institute at Chicago, Marcelo Sandoval-Castañeda Adobe Systems and Toyota Technological Institute at Chicago, Karen Livescu, Greg Shakhnarovich, Kanishka Misra University of Texas at Austin

原文摘要

What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grounded evidence? We study this question for a scenario where part of the input comes from a different modality—in our case, in a vision-language model (VLM), where a pretrained LM is aligned with a pretrained image encoder. As a case study we focus on the task of predicting hypernyms of objects represented in images. We do so in a VLM setup where the image encoder and LM are kept frozen, and only the intermediate mappings are learned. We progressively deprive the VLM of explicit evidence for hypernyms, and test whether the knowledge is recoverable from the LM. We find that the LMs we study can recover this knowledge and generalize even in the most extreme version of this experiment (when the model receives no evidence of a hypernym during training). Additional experiments suggest that this cross-modal taxonomic generalization persists under counterfactual image–label mappings only when the counterfactual data have high visual similarity within each category. Taken together, these findings suggest that cross-modal generalization in LMs arises from an interaction between linguistic structure and the information present in the input.

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.743ACL Anthology · ACL 2026

Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph

Lingxiao Guan University of Michigan - Ann Arbor, Yuanhao Huang, Jie Liu University of Michigan - Ann Arbor

原文摘要

In Question Answering (QA), Retrieval Augmented Generation (RAG) has revolutionized performance in various domains. However, how to effectively capture multi-document relationships remains an open question. This is particularly critical for biomedical tasks due to their reliance on information spread across multiple documents. In this work, we propose a novel method CLAIMS, which utilizes propositional claims to construct a local knowledge graph from retrieved documents. Summaries are then derived via layerwise summarization from the knowledge graph to contextualize a small language model to perform QA. The structured summaries effectively capture explicit and implicit relationships between entities in the documents, thus having a more comprehensive context to provide to LLMs. CLAIMS achieved comparable or superior performance over RAG baselines on several biomedical QA benchmarks. We also evaluated its generalizability and each individual step of our approach with a targeted set of metrics, demonstrating its 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.744ACL Anthology · ACL 2026

Sigmoid Head for Quality Estimation under Language Ambiguity

Tu Anh Dinh Karlsruher Institut für Technologie, Jan Niehues

原文摘要

Language model (LM) probability is not a reliable quality estimator, as natural language is ambiguous. When multiple output options are valid, the model’s probability distribution is spread across them, which can misleadingly indicate low output quality. This issue is caused by two reasons: (1) LMs’ final output activation is softmax, which does not allow multiple correct options to receive high probabilities simultaneuously and (2) LMs’ training data is single, one-hot encoded references, indicating that there is only one correct option at each output step. We propose training a module for Quality Estimation on top of pre-trained LMs to address these limitations. The module, called Sigmoid Head, is an extra unembedding head with sigmoid activation to tackle the first limitation. To tackle the second limitation, during the negative sampling process to train the Sigmoid Head, we use a heuristic to avoid selecting potentially alternative correct tokens. Our Sigmoid Head is computationally efficient during training and inference. The probability from Sigmoid Head is notably better quality signal compared to the original softmax head. As the Sigmoid Head does not rely on human-annotated quality data, it is more robust to out-of-domain settings compared to supervised QE.

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.745ACL Anthology · ACL 2026

RExBench: Can coding agents autonomously implement AI research extensions?

Nicholas Edwards Universität Vienna, Yukyung Lee Boston University, Boston University, Yujun Audrey Mao Boston University, Boston University, Yulu Qin Boston University, Boston University, Sebastian Schuster University of Vienna, Najoung Kim Boston University

原文摘要

Agents based on Large Language Models (LLMs) have shown promise for performing sophisticated software engineering tasks autonomously. In addition, there has been progress towards developing agents that can perform parts of the research pipeline in machine learning and the natural sciences. We argue that research extension and its implementation is a critical capability for such systems, and introduce RExBench to support the evaluation of this capability. RExBench is a benchmark consisting of realistic extensions of 12 research papers that aim to investigate novel research hypotheses. Each task is set up as an extension to an existing research paper and codebase, accompanied by domain expert-written instructions. RExBench is robust to data contamination, and supports an automatic evaluation infrastructure that executes agent outputs to determine whether the success criteria are met. We use this benchmark to evaluate 12 LLM agents implemented using two different frameworks: aider and OpenHands. We find that all agents fail to autonomously implement the majority of the extensions, with the best agent at around 33% success rate. Although the success rate improves with additional human-written hints, the best performance under this setting remains below 44%. This indicates that current agents are still short of being able to handle realistic research extension tasks without substantial human guidance.

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.746ACL Anthology · ACL 2026

Narrative License and Model Sycophancy in LLM Summaries of Scientific Work

Calvin Isch, Grace Jennings University of Pennsylvania, University of Pennsylvania

原文摘要

Large language models (LLMs) are increasingly used to summarize academic work, yet model summaries can subtly exaggerate or mischaracterize findings. We examine how Narrative License (NL), rhetorical shifts that amplify claims beyond the underlying evidence, emerges in LLM summaries of scholarly articles. Using diverse prompting strategies across six leading models, we assess three dimensions of NL: causal overreach, rhetorical confidence, and sentiment (N = 100 peer-reviewed articles). Under basic summarization prompts, models frequently increase NL relative to academic abstracts; however, guardrail prompts can reduce these distortions. We further test how model “sycophancy” shapes NL, finding that stated stances and user personas produce predictable shifts in each element. These findings suggest that users and the benchmarks used to evaluate summarization should explicitly consider subtle rhetorical distortions and user alignment to ensure faithful scientific communication.

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.747ACL Anthology · ACL 2026

AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation

Nicolas Bougie, Gian Maria Marconi Woven by Toyota, Xiaotong Ye, Narimawa Watanabe

原文摘要

Evaluating recommender systems remains challenging due to the gap between offline metrics and real user behavior, as well as the scarcity of interaction data. Recent work explores large language model (LLM) agents as synthetic users, yet they typically rely on few-shot prompting, which yields a shallow understanding of the environment and limits their ability to faithfully reproduce user actions. We introduce AlignUSER, a framework that learns world-model-driven agents from human interactions. Given rollout sequences of actions and states, we formalize world modeling as a next state prediction task that helps the agent internalize the environment. To align actions with human personas, we generate counterfactual trajectories around demonstrations and prompt the LLM to compare its decisions with human choices, identify suboptimal actions, and extract lessons. The learned policy is then used to drive agent interactions with the recommender system. We evaluate AlignUSER across multiple datasets and demonstrate closer alignment with genuine humans than prior work, both at the micro and macro levels.

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.748ACL Anthology · ACL 2026

Domain Generalizable AI Guardrails with Augmented Policy Training

Minqian Liu, Ioana Baldini Bloomberg, David Rabinowitz Bloomberg, David S Rosenberg Bloomberg, Sebastian Gehrmann Bloomberg, Mark Dredze Department of Computer Science, Whiting School of Engineering and Bloomberg

原文摘要

AI guardrail systems support usage policies by determining whether a user query or a generated response is allowed or forbidden under the policy. Fine-tuned guardrails – such as LlamaGuard and ShieldGemma – include policy definitions in prompts during training that can be updated during inference to aid generalization. However, our analysis reveals that these models still overfit the training policies, which prevents adaptation to new domains. We propose Augmented Policy Training (APT), a training recipe that enhances guardrail adaptability to unseen policies by using a suite of policy perturbation strategies during training to reduce overfitting and increase generalization. Notably, a small 1B model trained in this manner achieves comparable or better performance than existing 8B guardrails on unseen policies. Our work reveals critical limitations of existing AI guardrails, offers a promising solution, and provides actionable insights for adapting systems to new domains and policies.

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.749ACL Anthology · ACL 2026

APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI

Pratyay Banerjee Amazon, Masud Moshtaghi Amazon, Shivashankar Subramanian Amazon, Amita Misra Amazon, Ankit Chadha Amazon

原文摘要

Large language models still struggle with reliable long-term conversational memory: simply enlarging context windows or applying naïve retrieval often introduces noise and destabilizes responses. We present APEX-MEM, a conversational memory system that combines three key innovations: (1) a property graph which use domain-agnostic ontology to structure conversations as temporally grounded events in an entity-centric framework, (2) append-only storage that preserves the full temporal evolution of information, and (3) a multi-tool retrieval agent that understands and resolves conflicting or evolving information at query time, producing a compact and contextually relevant memory summary. This retrieval-time resolution preserves the full interaction history while suppressing irrelevant details. APEX-MEM achieves 88.88% accuracy on LOCOMO and 86.2% on LongMemEval, outperforming state-of-the-art session-aware approaches and demonstrating that structured property graphs enable more temporally coherent long-term conversational reasoning.

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.75ACL Anthology · ACL 2026

Grammar Search for Multi-Agent Systems

Mayank Singh University of Arizona, Vikas Yadav, Shiva Krishna Reddy Malay ServiceNow Inc, Shravan Nayak Université de Montréal, Sai Rajeswar, Sathwik Tejaswi Madhusudhan Cartesia, Eduardo Blanco University of Arizona

原文摘要

Automatic search for Multi-Agent Systems has recently emerged as a key focus in agentic AI research. Several prior approaches have relied on LLM-based free-form search over the code space. In this work, we propose a more structured framework that explores the same space through a fixed set of composable components. We show that, despite lacking the generative flexibility of LLMs during the candidate generation stage, our method outperforms prior approaches on a majority of evaluated benchmarks across two backbone LLMs and two domains: mathematics and question answering. Furthermore, our method offers additional advantages, including a more cost-efficient search process and the generation of modular, interpretable multi-agent systems.

cs.CL · cs.AI · 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.750ACL Anthology · ACL 2026

emg2speech: synthesizing speech from electromyography using self-supervised speech models

Harshavardhana T Gowda, Daniel C Comstock University of California, Davis, Lee M. Miller University of California, Davis

原文摘要

We present a neuromuscular speech interface that translates electromyographic (EMG) signals recorded from orofacial muscles during speech articulation directly into audio. We find that self-supervised speech (S3) representations are strongly linearly related to the electrical power of muscle activity: a simple linear mapping predicts EMG power from S3 representations with a correlation of r = 0.85. In addition, EMG power vectors associated with distinct articulatory gestures form structured, separable clusters. Together, these observations suggest that S3 models implicitly encode articulatory mechanisms, as reflected in EMG activity. Leveraging this structure, we map EMG signals into the S3 representation space and synthesize speech, enabling end-to-end EMG-to-speech generation without explicit articulatory modeling or vocoder training. We demonstrate this system with a participant with amyotrophic lateral sclerosis (ALS), converting orofacial EMG recorded while she silently articulated speech into audio.

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.751ACL Anthology · ACL 2026

CoAct: Co-Active LLM Preference Learning with Human-AI Synergy

Ruiyao Xu Northwestern University, Mihir Parmar Google, Tiankai Yang University of Southern California, Zhengyu Hu University of Washington, Yue Zhao University of Southern California, Kaize Ding Northwestern University

原文摘要

Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated preference data remains expensive and scarce. Existing methods address this challenge through either self-rewarding, which scales by using purely AI-generated labels but risks unreliability, or active learning, which ensures quality through oracle annotation but cannot fully leverage unlabeled data. In this paper, we present CoAct, a novel framework that synergistically combines self-rewarding and active learning through strategic human-AI collaboration. CoAct leverages self-consistency to identify both reliable self-labeled data and samples requiring oracle verification. Additionally, oracle feedback guides the model to generate new instructions within its solvable capability. Evaluated on three reasoning benchmarks across two model families, CoAct achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct, consistently outperforming all 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.752ACL Anthology · ACL 2026

Label and Explanation Variation in LLM-Based Annotation: a Case Study in Natural Language Inference

Artur Kulmizev, Erika Lombart, Patrick Watrin UCL, Marie-Catherine de Marneffe UCLouvain

原文摘要

Large language models (LLMs) have shown considerable promise for annotation purposes, yet questions remain about their ability to capture human label variation (HLV) — genuine disagreement between annotators often observed across NLP tasks. Here, we investigate how label and explanation variation manifests within and across LLMs with respect to the Natural Language Inference (NLI) task. Using zero-shot prompting with exact human annotation instructions, we treat individual model generations as participants and examine three response sampling strategies: varying generation parameters, leveraging within-family model size differences, and pooling responses from distinct LLMs. We show that, while model ensembles can generate label distributions similar to humans, they likewise exhibit distinct, idiosyncratic judgments and disagreement patterns. We further analyze explanation variation, observing that, although models generate longer explanations than humans, they demonstrate substantially less stylistic diversity. Our findings suggest that, while LLMs may serve as useful tools for generating diverse annotations, they should not be viewed as drop-in replacements for human annotators — particularly in applications requiring authentic representation of diversity in human judgments, such as NLI.

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.753ACL Anthology · ACL 2026

Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation

Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Chen Chen University of Kansas, Dongjie Wang University of Kansas, Zijun Yao University of Kansas

原文摘要

In electronic health record (EHR) mining, learning high-quality representations of medical concepts (e.g., standardized diagnosis, medication, and procedure codes) is fundamental for downstream clinical prediction. However, robust concept representation learning is hindered by two key challenges: (i) clinically important cross-type dependencies (e.g., diagnosis-medication and medication-procedure relations) are often missing or incomplete in existing ontology resources, limiting the ability to model complex EHR patterns; and (ii) rich clinical semantics are often missing from structured resources, and even when available as text, are difficult to integrate with KG structure for representation learning. To address these challenges, we present MedCo, an LLM-empowered graph learning framework for medical concept representation. MedCo first builds a global knowledge graph (KG) over medical codes by combining statistically reliable associations mined from EHRs with type-constrained LLM prompting to infer semantic relations. It then utilizes LLMs to enrich the KG into a text-attributed graph by generating node descriptions and edge rationales, providing semantic signals for both concepts and their relationships. Finally, MedCo jointly trains a LoRA-tuned LLaMA text encoder with a heterogeneous GNN, fusing text semantics and graph structure into unified concept embeddings. Extensive experiments on MIMIC-III and MIMIC-IV show that MedCo consistently improves prediction performance and serves as an effective plug-in concept encoder for standard EHR pipelines.

cs.CL

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