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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.677ACL Anthology · ACL 2026

SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding

Yiqiao Jin, Rachneet Kaur J.P. Morgan Chase, Zhen Zeng, Sumitra Ganesh J.P. Morgan Chase, Srijan Kumar Georgia Institute of Technology

原文摘要

Multi-page visual documents such as manuals, brochures, presentations, and posters convey key information through layout, colors, icons, and cross-slide references. While multimodal large language models (MLLMs) offer opportunities in document understanding, current systems struggle with complex, multi-page visual documents, particularly in fine-grained reasoning over elements and pages. We introduce SlideAgent, a versatile agentic framework for understanding multi-modal, multi-page, and multi-layout documents, especially slide decks. SlideAgent employs specialized agents and decomposes reasoning into three specialized levels–global, page, and element–to construct a structured, query-agnostic representation that captures both overarching themes and detailed visual or textual cues. During inference, SlideAgent selectively activates specialized agents for multi-level reasoning and integrates their outputs into coherent, context-aware answers.Extensive experiments show that SlideAgent significantly improves accuracy over both proprietary (+7.9%) and open-source models (+9.8%).

cs.CL · cs.AI · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.679ACL Anthology · ACL 2026

Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts

Riyang Bao Emory University, Cheng Yang, Dazhou Yu Emory University, Zhexiang Tang, Gengchen Mai Google and University of Texas at Austin, Liang Zhao Emory University

原文摘要

Geospatial reasoning is essential for real-world applications such as urban analytics, transportation planning, and disaster response. However, existing LLM-based agents often fail at genuine geospatial computation, relying instead on web search or pattern matching while hallucinating spatial relationships. We present Spatial-Agent, an AI agent grounded in foundational theories of spatial information science. Our approach formalizes geo-analytical question answering as a concept transformation problem, where natural-language questions are parsed into executable workflows represented as GeoFlow Graphs—directed acyclic graphs with nodes corresponding to spatial concepts and edges representing transformations. Drawing on spatial information theory, Spatial-Agent extracts spatial concepts, assigns functional roles with principled ordering constraints, and composes transformation sequences through template-based generation. Extensive experiments on MapEval-API and MapQA benchmarks demonstrate that Spatial-Agent significantly outperforms existing baselines including ReAct and Reflexion, while producing interpretable and executable geospatial workflows.

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.684ACL Anthology · ACL 2026

ZARA: Training-Free Motion Time-Series Reasoning via Evidence-Grounded LLM Agents

Zechen Li Google, Baiyu Chen University of New South Wales, Hao Xue The Hong Kong University of Science and Technology, Flora D. Salim

原文摘要

Motion sensor time-series are central to Human Activity Recognition (HAR), yet conventional approaches are constrained to fixed activity sets and typically require costly parameter retraining to adapt to new behaviors. While Large Language Models (LLMs) offer promising open-set reasoning capabilities, applying them directly to numerical time-series often leads to hallucinations and weak grounding. To address this challenge, we propose ZARA (Zero-training Activity Reasoning Agents), a knowledge- and retrieval-augmented agentic framework for motion time-series reasoning in a training-free inference setting. Rather than relying on black-box projections, ZARA distills reference data into a statistically grounded textual knowledge base that transforms implicit signal patterns into verifiable natural-language priors. Guided by retrieved evidence, ZARA iteratively selects discriminative cues and performs grounded reasoning over candidate activities. Extensive experiments on eight benchmarks show that ZARA generalizes robustly to unseen subjects and across datasets, demonstrating strong transferability across heterogeneous sensor domains. These results mark a step toward trustworthy, plug-and-play motion understanding beyond dataset-specific artifacts. Our code is available at https://github.com/zechenli03/ZARA.

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.685ACL Anthology · ACL 2026

AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs

Xuanwen Ding, Chengjun Pan, Zejun Li, Jiwen Zhang Fudan University, Siyuan Wang The Chinese University of Hong Kong, Zhongyu Wei Fudan University

原文摘要

Evaluating multimodal large language models (MLLMs) is becoming increasingly expensive as benchmarks grow in scale and cross-modality complexity. Inspired by structuralism in cognitive psychology, we tackle this difficulty with an adaptive evaluation framework for efficient benchmarking, namely AutoJudger. Instead of passively scoring on a fixed test set, AutoJudger treats evaluation as an interview-like process by keeping a hypothesized ability structure of the evaluated model and actively selecting the informative questions so as to refine these ability boundaries. Specifically, AutoJudger has three core components: ability decomposition to organize evaluation along meaningful capability dimensions, ability estimation to maintain an up-to-date quantitative profile of the model competence, and adaptive question selection to choose the most informative questions. To operationalize this paradigm, we introduce A^2-Judger, a novel MLLM-based Agentic instantiation of AutoJudger equipped with semantic-aware retrieval and dynamic memory. Experiments on four representative multimodal benchmarks show that A^2-Judger significantly improves sample efficiency while maintaining reliable evaluation results.

cs.CL · cs.AI · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.697ACL Anthology · ACL 2026

Agentic Rubrics as Contextual Verifiers for SWE Agents

Mohit Raghavendra, Anisha Gunjal Scale AI, Bing Liu Scale AI, Yunzhong He Scale AI

原文摘要

Verification is critical for improving agents: it provides the reward signal for Reinforcement Learning and enables inference-time gains through Test-Time Scaling (TTS). Despite its importance, verification in software engineering (SWE) agent settings often relies on code execution, which can be difficult to scale due to environment setup overhead. Scalable alternatives such as patch classifiers and heuristic methods exist, but they are less grounded in codebase context and harder to interpret. To this end, we explore Agentic Rubrics: an expert agent interacts with the repository to create a context-grounded rubric checklist, and candidate patches are then scored against it without requiring test execution. On SWE-Bench Verified under parallel TTS evaluation, Agentic Rubrics achieve a score of 54.2% on Qwen3-Coder-30B-A3B and 40.6% on Qwen3-32B, with at least a +3.5 percentage-point gain over the strongest baseline in our comparison set. We further analyze rubric behavior, showing that rubric scores are consistent with ground-truth tests while also flagging issues that tests do not capture. Our ablations show that agentic context gathering is essential for producing codebase-specific, unambiguous criteria. Together, these results suggest that Agentic Rubrics provide an efficient, scalable, and granular verification signal for SWE agents.

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.718ACL Anthology · ACL 2026

LLM Agents in Law: Taxonomy, Applications, and Challenges

Shuang Liu Carnegie Mellon University, Ruijia Zhang, Ruoyun Ma ByteDance Inc., Yujia Deng Facebook, Lanyi Zhu University of Washington, Jiayu Li University of Chicago, Zelong Li Amazon, Zhibin Shen Columbia University 等 9 位作者

原文摘要

Large language models (LLMs) have precipitated a dramatic improvement in the legal domain, yet the deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability. Recently, LLM agents have attracted significant attention as a solution to these challenges, utilizing advanced capabilities such as planning, memory, and tool usage to meet the rigorous standards of legal practice. In this paper, we present a comprehensive survey of LLM agents for legal tasks, analyzing how these architectures bridge the gap between technical capabilities and domain-specific needs. Our major contributions include: (1) systematically analyzing the technical transition from standard legal LLMs to legal agents; (2) presenting a structured taxonomy of current agent applications across distinct legal practice areas; (3) discussing evaluation methodologies specifically for agentic performance in law; and (4) identifying open challenges and outlining future directions for developing robust and autonomous legal assistants.

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.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.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.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.754ACL Anthology · ACL 2026

Evolving Agents

Leonardo Ranaldi

原文摘要

AI agents powered by LLMs plan, reason and use tools well, but remain brittle to adapt and evolve because they fail to abstract the mechanisms underlying problem-solving. Reasoning, memory, and decision-making remain fragmented, leaving the systematic reuse of experience for adaptation at scale out of reach. In this paper, we introduce EvA (Evolving Agents), a framework to improve evolution in agentic reasoning that leverages quasi-symbolic abstractions—semi-structured dynamic representations that furnish constructs to distil and reuse reasoning mechanisms. They are learned and refined through experience, and when instantiated, deliver meta-states for organising reasoning. EvA orchestrates this mechanism via a Perceptor modelling observation and action, an Actor conditioning its policy on these abstractions, and a Controller overseeing structure to explore paths or initiate rollbacks. As experience accumulates, EvA refines both its abstractions and the modules that construct and instantiate them by learning past trajectories into reusable mechanisms. Our initial analysis shows that EvA improves accuracy and adapts under mid-episode distribution shifts that cause agents to plateau on complex reasoning and interactive planning tasks. These results position EvA as a step towards adaptive reasoning, memory, and meta-control.

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.767ACL Anthology · ACL 2026

Analyzing and Internalizing Complex Policy Documents for LLM Agents

Jiateng Liu, Zhenhailong Wang, Xiaojiang Huang Amazon, Yingjie Li Amazon, Xiang Li Amazon, Chenlei Guo Amazon, Xing Fan, Ruhi Sarikaya 等 9 位作者

原文摘要

Large language model agents rely on in-context policy documents encoding diverse business rules. As businesses scale, these documents grow, creating substantial computational overhead and motivating internalization methods that embed policy into model priors. Prior work focuses on generic prompts, but we find agentic policies span multiple complexity levels and demand heavier reasoning, posing greater challenges. We introduce an agentic benchmark generator with Controllable Complexity in agent policy across four levels, enabling systematic evaluation of agents under increasing complexity and providing a testbed for policy internalization. Our analysis shows that workflow-governing policy specifications are the hardest to reason over, and that SFT on gold trajectories with chain-of-thought is data-hungry and struggles at high complexity. We propose Category-Aware Policy Continued Pretraining, an automated pipeline that analyzes policies, extracts key specifications, categorizes them into factual, behavioral, and conditional types, and isolates those driving workflow complexity. This enables targeted “therapy” by synthesizing specialized training data for each type and improving internalization via an autoregressive pretraining loss. Extensive experiments show our synthetic data and objective consistently improve performance. Combined with SFT, our method outperforms the baseline across different settings, especially in data-sparse and high-complexity regimes, with gains up to 41% and 22% on Qwen-3-32B. Overall, we achieve 97.3% prompt reduction on our benchmark, and on \tau-Bench we further improve performance while reducing prompt requirements with very limited SFT data.

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.826ACL Anthology · ACL 2026

Your Reasoning Benchmark May Not Test Reasoning: Revealing Perception Bottleneck in Abstract Reasoning Benchmarks

Xinhe Wang, Jin Huang, Xingjian Zhang University of Michigan - Ann Arbor, Tianhao Wang University of California, San Diego, Jiaqi W. Ma

原文摘要

Reasoning benchmarks such as the Abstraction and Reasoning Corpus (ARC) and ARC-AGI are widely used to assess progress in artificial intelligence and are often interpreted as probes of core, so-called “fluid” reasoning abilities. Despite their apparent simplicity for humans, these tasks remain challenging for frontier vision-language models (VLMs), a gap commonly attributed to deficiencies in machine reasoning. We challenge this interpretation and hypothesize that the gap arises primarily from limitations in visual perception rather than from shortcomings in inductive reasoning.To verify this hypothesis, we introduce a two-stage experimental pipeline that explicitly separates perception and reasoning. In the perception stage, each image is independently converted into a natural-language description, while in the reasoning stage a model induces and applies rules using these descriptions. This design prevents leakage of cross-image inductive signals and isolates reasoning from perception bottlenecks. Across three ARC-style datasets, Mini-ARC, ACRE, and Bongard-LOGO, we show that the perception capability is the dominant factor underlying the observed performance gap by comparing the two-stage pipeline with against standard end-to-end one-stage evaluation. Manual inspection of reasoning traces in the VLM outputs further reveals that approximately 80 percent of model failures stem from perception errors. Together, these results demonstrate that ARC-style benchmarks conflate perceptual and reasoning challenges and that observed performance gaps may overstate deficiencies in machine reasoning. Our findings underscore the need for evaluation protocols that disentangle perception from reasoning when assessing progress in machine intelligence.

cs.CL · cs.AI · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.843ACL Anthology · ACL 2026

ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering

Rachneet Kaur J.P. Morgan Chase, Nishan Srishankar J.P. Morgan Chase, Fidelity AI COE, Worcester Polytechnic Institute, Honda Research Institute and NASA/SETI Institute, Zhen Zeng, Sumitra Ganesh J.P. Morgan Chase

原文摘要

Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts—those requiring precise visual interpretation rather than relying on textual shortcuts. To address this, we introduce ChartAgent, a novel agentic framework that explicitly performs visual reasoning directly within the chart’s spatial domain. Unlike textual chain-of-thought reasoning, ChartAgent iteratively decomposes queries into visual subtasks and actively manipulates and interacts with chart images through specialized actions such as drawing annotations, cropping regions (e.g., segmenting pie slices, isolating bars), and localizing axes, using a library of chart-specific vision tools to fulfill each subtask. This iterative reasoning process closely mirrors human cognitive strategies for chart comprehension. ChartAgent achieves state-of-the-art accuracy on the ChartBench and ChartX benchmarks, surpassing prior methods by up to 16.07% absolute gain overall and 17.31% on unannotated, numerically intensive queries. Furthermore, our analyses show that ChartAgent is (a) effective across diverse chart types, (b) achieves the highest scores across varying visual and reasoning complexity levels, and (c) serves as a plug-and-play framework that boosts performance across diverse underlying LLMs. Our work is among the first to demonstrate visually grounded reasoning for chart understanding using tool-augmented multimodal agents.

cs.CL · cs.AI · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.911ACL Anthology · ACL 2026

IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking

Zechen Sun, Yuyang Sun, Zecheng Tang Soochow University, Juntao Li, Wenpeng Hu Academy of Military Science, Wenliang Chen Soochow University, China, Zhunchen Luo, Guotong Geng AMS 等 9 位作者

原文摘要

Generating coherent and controllable long-form content remains a persistent challenge for Large Language Models (LLMs). While reasoning-enhanced models have demonstrated success in logic-intensive domains, our evaluation reveals that they suffer from a severe length collapse in open-ended writing, where performance degrades sharply as target lengths exceed 2,000 words. We attribute this failure to the limitation of static hierarchical planning, which struggles to provide dynamic guidance over extended contexts. To bridge this gap, we introduce the Interleaved Structural Chain-of-Thought (IS-CoT) framework. Unlike external agentic workflows, IS-CoT embeds a dynamic Plan-Write-Reflect cycle into the generation process, enabling continuous strategy adaptation and global alignment without additional assistance. Based on this framework, we construct a high-quality dataset of interleaved reasoning traces via a multi-teacher pipeline and train IS-Writer-8B. Experiments demonstrate that IS-Writer-8B achieves state-of-the-art performance on challenging long-form benchmarks (e.g., +3.08 vs. DeepSeek-V3.2 on LongBench-Write), exhibiting robust length compliance and coherence competitive with significantly larger proprietary models.

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.954ACL Anthology · ACL 2026

Visual Inception: Compromising Long-term Planning in Agentic Recommenders via Multimodal Memory Poisoning

Jiachen Qian

原文摘要

The evolution from static ranking models to Agentic Recommender Systems (Agentic RecSys) empowers AI agents to maintain long-term user profiles and autonomously plan service tasks. While this paradigm shift enhances personalization, it introduces a vulnerability: reliance on Long-term Memory (LTM). In this paper, we uncover a threat termed “Visual Inception.” Unlike traditional adversarial attacks that seek immediate misclassification, Visual Inception injects triggers into user-uploaded images (e.g., lifestyle photos) that act as “sleeper agents” within the system’s memory. When retrieved during future planning, these poisoned memories hijack the agent’s reasoning chain, steering it toward adversary-defined goals (e.g., promoting high-margin products) without prompt injection. To mitigate this, we propose CognitiveGuard, a dual-process defense framework inspired by human cognition. It consists of a System 1 Perceptual Sanitizer (diffusion-based purification) to cleanse sensory inputs and a System 2 Reasoning Verifier (counterfactual consistency checks) to detect anomalies in memory-driven planning. Extensive experiments on a mock e-commerce agent environment demonstrate that Visual Inception achieves about 85% Goal-Hit Rate (GHR), while CognitiveGuard reduces this risk to around 10% with configurable latency trade-offs (about 1.5s in lite mode to about 6.5s for full sequential verification), without quality degradation under our setup.Latency reporting uses separate accounting: query-time overhead excludes one-time upload-time preprocessing.

cs.CL · cs.AI · cs.CV

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

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.966ACL Anthology · ACL 2026

Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression

Miao Lu Stanford University, Weiwei Sun Carnegie Mellon University, Weihua Du Carnegie Mellon University, Zhan Ling ByteDance Inc., Xuesong Yao ByteDance Inc., Kang Liu ByteDance Inc., Jiecao Chen ByteDance Inc.

原文摘要

We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing multi-turn RL pipelines suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. In this work, to address these challenges, we introduce summarization-based context management to training. In specific, it periodically compresses the tool using history by LLM-generated summaries that retain task-relevant information to keep a compact context while enabling the agent to scale beyond the fixed context window. Building on this formulation, we derive a policy gradient representation that seamlessly enables standard LLM RL infrastructures to optimize both tool-use behaviors as well as summarization strategies in an end-to-end fashion. We instantiate this framework with SUmmarization augmented Policy Optimization (SUPO), an LLM RL algorithm that enables long-horizon training beyond a fixed context limit. Experiments on interactive function calling and searching tasks demonstrate that SUPO significantly improves the success rate while maintaining the same or even lower working context length compared to baselines. We also demonstrate that for complex searching tasks SUPO can further improve the evaluation performance when scaling test-time maximum round of summarization beyond that of training time.

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.971ACL Anthology · ACL 2026

FlowSearch: Advancing Deep Research with Dynamic Structured Knowledge Flow

Yusong Hu, Runmin Ma, Yue Fan Shanghai Artificial Intelligence Laboratory, Jinxin Shi, Zongsheng Cao Independent Researcher, Yuhao Zhou, Jiakang Yuan Shanghai Artificial Intelligence Laboratory and Fudan University, Shuaiyu Zhang 等 14 位作者

原文摘要

Deep research is an inherently challenging task that demands both breadth and depth of thinking. It involves navigating diverse knowledge spaces and reasoning over complex, multi-step dependencies, which presents substantial challenges for agentic systems. To address this, we propose FlowSearch, a multi-agent framework that actively constructs and evolves a dynamic structured knowledge flow to drive subtask execution and reasoning. FlowSearch is capable of strategically planning and expanding the knowledge flow to enable parallel exploration and hierarchical task decomposition, while also adjusting the knowledge flow in real time based on feedback from intermediate reasoning outcomes and insights. FlowSearch achieves competitive performance on both general and scientific benchmarks, including GAIA, HLE, GPQA and TRQA, demonstrating its effectiveness in multi-disciplinary research scenarios and its potential to advance scientific discovery. The code will be available.

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.981ACL Anthology · ACL 2026

Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents

Yi Yu Wuhan University, Liuyi Yao Alibaba Group, Yuexiang Xie Alibaba Group, Qingquan Tan Wuhan University, Jiaqi Feng Wuhan University, Yaliang Li Alibaba Group, Libing Wu Wuhan University

原文摘要

Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory management critical. Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components, relying on heuristics or auxiliary controllers, which limits adaptability and end-to-end optimization. In this paper, we propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent’s policy. AgeMem exposes memory operations as tool-based actions, enabling the LLM agent to autonomously decide what and when to store, retrieve, update, summarize, or discard information. To train such unified behaviors, we propose a three-stage progressive reinforcement learning strategy and design a step-wise GRPO to address sparse and discontinuous rewards induced by memory operations. Experiments on five long-horizon benchmarks demonstrate that AgeMem consistently outperforms strong memory-augmented baselines across multiple LLM backbones, achieving improved task performance, higher-quality long-term memory, and more efficient context usage.

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.991ACL Anthology · ACL 2026

Gold-Medal-Level Olympiad Geometry Solving with Efficient Heuristic Auxiliary Constructions

Boyan Duan, Xiao Liang, Shuai Lu JD.com, Yaoxiang Wang, Yelong Shen, Kai-Wei Chang University of California, Los Angeles and Amazon, Ying Nian Wu, Mao Yang 等 10 位作者

原文摘要

Automated theorem proving in Euclidean geometry, particularly for International Mathematical Olympiad (IMO) level problems, remains a major challenge and an important research focus in Artificial Intelligence. In this paper, we present a highly efficient method for geometry theorem proving that runs entirely on CPUs without relying on neural network–based inference. Our initial study shows that a simple random strategy for adding auxiliary points can achieve ”silver-medal” level human performance on IMO. Building on this, we propose HAGeo, a Heuristic-based method for adding Auxiliary points in Geometric deduction that solves 28 of 30 problems on the IMO-30 benchmark, achieving “gold-medal” level performance and surpassing AlphaGeometry, a competitive neural network–based approach, by a notable margin. To evaluate our method and existing approaches more comprehensively, we further construct HAGeo, a benchmark consisting of 409 geometry problems with human-assessed difficulty levels. Compared with the widely used IMO-30, our benchmark poses greater challenges and provides a more precise evaluation, setting a higher bar for geometry theorem proving.

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-short.14ACL Anthology · ACL 2026

Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization

Gongbo Zhang Columbia University, Yifan Peng Weill Cornell Medicine, Cornell University, Chunhua Weng Columbia University

原文摘要

Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language model (LLM) outputs by grounding generation in external knowledge. Recent agentic RAG systems extend this paradigm by introducing critic agents that evaluate model responses and iteratively refine outputs. However, most prior work implicitly assumes reliable critic feedback and focuses on planning strategies, while paying limited attention to the robustness of the error correction process itself, which is often hindered by misaligned error categories and ineffective or incorrect corrections. We hypothesize that RAG performance can be improved without explicit error categorization. To this end, we propose RePAIR, a response–action learning paradigm that directly maps flawed RAG outputs to error-mitigating action plans without relying on fine-grained error taxonomies or explicit critic supervision. Across multiple benchmarks, RePAIR consistently improves agentic RAG performance.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-short.15ACL Anthology · ACL 2026

Deep Kernel Fusion for Transformers

Zixi Zhang, Zhiwen Mo, Yiren Zhao Imperial College London, Robert D. Mullins

原文摘要

Agentic LLM inference with long contexts is increasingly limited by memory bandwidth rather than compute. In this setting, SwiGLU MLP blocks, whose large weights exceed cache capacity, become a major yet under-optimized bottleneck in the Transformer architecture. We propose DeepFusionKernel, a deeply fused kernel that cuts HBM traffic and boosts cache reuse, delivering up to 13.2% speedup on H100 and 9.7% on A100 over SGLang. Integrated with SGLang and paired with a kernel scheduler, DeepFusionKernel ensures consistent accelerations across generation lengths, while remaining adaptable to diverse models, inference configurations, and hardware platforms.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-short.44ACL Anthology · ACL 2026

Neuro-Symbolic Agentic Reinforcement Learning for Long-Term Original Character Companionship and Interaction

Zhenhan Huang

原文摘要

As human-agent interaction (HAI) evolves toward long-term social companionship, users expect Original Character (OC) agents to maintain a consistent persona, manage shared memories, and adapt to ever-changing preferences. However, LLM-based agents optimized by prompting or SFT exhibit a generalization gap: they behave as myopic instruction followers, leading to cascading errors in multi-turn interactions. For the agents to learn trajectory-level value functions that enable farsighted decision-making, we propose the NSARL framework, which formalizes OC companion agents’ interactions as a POMDP and decomposes the agent into three sub-policies (Router, Memory, and Persona), optimized via closed-loop RL from AI feedback (RLAIF) with verifiable rewards in a graph-constrained action space. Our preliminary experiments indicate a trade-off: SFT yields stronger persona generation, while NSARL improves structural logic, through conservative strategies (e.g., over-routing) that increase workflow completeness, advocating for a hybrid deployment strategy.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-short.63ACL Anthology · ACL 2026

Dialogue is the Plan: From Interface to Joint Action in Agentic AI

Mert Inan Northeastern University, Malihe Alikhani Northeastern University, Anthony Sicilia West Virginia University

原文摘要

Large Language Model agents can seeminglyplan and act, yet their language use is oftentreated primarily as an interface for instructingactions and reporting results. We argue that thisframing is one important cause of recurrent coordination failures in human-facing and multiagent settings, including ungrounded assumptions, silent goal misalignment, brittle protocoladherence, and failures to maintain or updateshared dialogue state over time, a limitation previously linked to the absence of explicit common ground tracking in collaborative systems(Geib et al., 2022). Drawing from classical dialogue system research on joint action, commonground, grounding, repair, and incremental processing, we re-frame dialogue as part of theplanning loop itself (rather than its output). Wedistill this re-framing into concrete implicationsfor agentic architecture and evaluation, including explicit representations of shared commitments, clarification as a first class action available to the policy, and process metrics that approximate grounding behavior, repair, and commitment formation rather than task completionalone. We lastly discuss how dialogue-centeredrequirements can inform standards and governance for safe deployment of agentic systems.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.41ACL Anthology · ACL 2026

Test-Time Strategies for More Efficient and Accurate Agentic RAG

Abhinav Sharma, Brian Zhang, Deepti Guntur University of Massachusetts at Amherst, Zhiyang Zuo MavenBio, Shreyas Chaudhari University of Massachusetts at Amherst, Wenlong Zhao University of Massachusetts at Amherst, Franck Dernoncourt Adobe Research, Puneet Mathur Adobe Systems 等 10 位作者

原文摘要

Retrieval-Augmented Generation (RAG) systems face challenges with complex, multi-hop questions, and iterative agentic frameworks such as Search-R1 (Jin et al., 2025) have been proposed to address these complexities. However, such approaches can introduce inefficiencies, including repetitive retrieval of previously processed information and challenges in contextualizing retrieved results effectively within the current generation prompt. Such issues can lead to unnecessary retrieval turns, suboptimal reasoning, inaccurate answers, and increased token consumption. In this paper, we investigate test-time modifications to Search-R1’s open-source Qwen2.5-7B pipeline to mitigate these identified shortcomings. Specifically, we explore the integration of two components and their combination: a contextualization module to better integrate relevant information from retrieved documents into reasoning, and a de-duplication module that replaces previously retrieved documents with the next most relevant ones. We evaluate our approaches using the HotpotQA (Yang et al., 2018) and the Natural Questions (Kwiatkowski et al., 2019) datasets, reporting the exact match (EM) score, an LLM-as-a-Judge assessment of answer correctness, and the average number of turns. Our best-performing variant (contextualization) achieves a 5.6% increase in EM score and reduces the average number of turns by 10.5% compared to the Search-R1 baseline. While contextualization itself introduces additional LLM calls, our results demonstrate improved answer accuracy and reduced retrieval load.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.50ACL Anthology · ACL 2026

A11y-Compressor: A Framework for Enhancing the Efficiency of GUI Agent Observations through Visual Context Reconstruction and Redundancy Reduction

Michito Takeshita Hosei University, Takuro Kawada Hosei University, Takumi Ohashi Hosei University, Shunsuke Kitada LY Corp., Hitoshi Iyatomi Hosei University

原文摘要

AI agents that interact with graphical user interfaces (GUIs) require effective observation representations for reliable grounding.The accessibility tree is a commonly used text-based format that encodes UI element attributes, but it suffers from redundancy and lacks structural information such as spatial relationships among elements.We propose A11y-Compressor, a framework that transforms linearized accessibility trees into compact and structured representations.Our implementation, Compressed-a11y, applies a lightweight and structured transformation pipeline with modal detection, redundancy reduction, and semantic structuring.Experiments on the OSWorld benchmark show that Compressed-a11y reduces input tokens to 22% of the original while improving task success rates by 5.1 percentage points on average.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.58ACL Anthology · ACL 2026

CBAL: Context-Based Agentic Learning for Speaker Diarization Segmentation Refinement

Odwitiyo Dutta, Dinesh K Vishwakarma Delhi Technological University (Delhi College of Engineering),

原文摘要

Speaker diarization systems produce segmentation errors, such as false splits and boundary misplacements, that degrade transcript readability and downstream applications. We present CBAL (Context-Based Agentic Learning), a post-processing framework that refines segmentation boundaries in diarized scripts through targeted error correction. CBAL detects potential segmentation errors using acoustic and temporal heuristics and employs a lightweight LLM agent to reason about merge decisions, validating corrections through uncertainty-aware filtering with signal-based constraints. CBAL achieves 93.4% accuracy across 359 applied merges and reduces segment count by 6.1%. We demonstrate that our framework identifies and corrects high-confidence errors while maintaining 0% degradation in terms of concatenated minimum-permutation Word Error Rate (cpWER). An ablation study confirms that each component contributes non-redundantly, demonstrating the viability of interpretable refinement frameworks that use the strengths of acoustic models and language understanding without requiring end-to-end retraining.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.78ACL Anthology · ACL 2026

Thesis Proposal: When Does an Agent Know It Is Lost? Confidence Trajectory Analysis for Tool-Using LLMs

Zhenjiang Mao University of Florida

原文摘要

Large language model (LLM) agents that invoke external tools must make sequences of interdependent decisions, yet existing uncertainty quantification (UQ) methods treat each step in isolation, ignoring how confidence evolves and compounds across a full task trajectory.We propose a framework for trajectory-level confidence analysis in the tool-use agent setting. The thesis pursues three aims: (1) estimating action-level confidence by adapting step-wise UQ to the heterogeneous think-act-observe cycles of tool-using agents; (2) aggregating the diverse action space into semantically coherent action types to enable meaningful trajectory-level analysis; and (3) discovering temporal patterns in the resulting confidence trajectories that reliably predict task success or failure.We ground the work in standard tool-use benchmarks and expect the framework to expose early warning signals for agent failure and offer interpretable diagnostic tools for understanding when and why LLM agents lose confidence, with improved calibration of multi-step agentic pipelines as a secondary benefit.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-srw.90ACL Anthology · ACL 2026

LLM-Based Zero-Shot Soft Labeling for Anticipating Disagreement in Negotiation Dialogues

Ken Watanabe Tokyo University of Agriculture and Technology, Tokyo Institute of Technology, Katsuhide Fujita Tokyo University of Agriculture and Technology

原文摘要

Negotiation involves complex emotional and strategic dynamics that pose challenges for AI agents in negotiation dialogues. This paper proposes a zero-shot soft-labeling method using an large language model-based embedding model and verifies its performance on negotiation dialogues. Furthermore, it examines the performance of predictive model training on rule-based annotated hard and soft labels obtained by the proposed method for the task of predicting whether agreement will be reached from partial dialogues, namely, final disagreement anticipation in negotiation mid-dialogues (FDANMD). Soft labeling obtained by the proposed method showed a maximum HIT@3 score of 0.87 against rule-based annotated hard labels, whereas failure cases also demonstrated the limitations of rule-based annotation. Furthermore, using ROC AUC, evaluations of FDANMD across three datasets (CB, DN, and JI) with negotiation progress rates of 0.25, 0.5, and 1.0 revealed that soft labeling is particularly effective at low negotiation progress rates and also offers superior performance on individual datasets and unseen datasets for models trained on multiple datasets. These results motivate the use of soft labeling to incorporate the complexity of negotiation dialogues into intermediate representations and support the generalizability of zero-shot soft labeling and generalizable predictors across a wide range of negotiations beyond known domains.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表

版本更新 · v2arXiv:2502.18864Google Research

Co-Scientist：加速科学发现的多智能体AI系统

Accelerating scientific discovery with Co-Scientist

Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Petar Sirkovic, Artiom Myaskovsky, Grzegorz Glowaty, Felix Weissenberger 等 51 位作者

AI 速读 基于摘要

基于Gemini的多智能体系统Co-Scientist通过生成、批判和进化假设，在生物医学领域辅助科学家发现新知识。

cs.AI · cs.CL · cs.HC · cs.LG · q-bio.OT

arXiv ↗PDF ↗
查看原文摘要

Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and prior scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system's design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. While general purpose, we focus the validation in three biomedical applications: drug repurposing, novel target discovery, and explaining mechanisms of anti-microbial resistance. Specifically, Co-Scientist helped identify new drug repurposing candidates and synergistic combination therapies for acute myeloid leukemia, which were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI empowered scientists.