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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.34ACL Anthology · ACL 2026

MASEval: Extending Multi-Agent Evaluation from Models to Systems

Cornelius Emde, Alexander Rubinstein Eberhard-Karls-Universität Tübingen, Anmol Goel Technische Universität Darmstadt, Ahmed Heakl LG Corporation, Sangdoo Yun NAVER AI Lab and Seoul National University, Seong Joon Oh Korea Advanced Institute of Science & Technology and Parameter Lab, Martin Gubri Parameter Lab

原文摘要

The rapid adoption of LLM-based agentic systems has produced a rich ecosystem of frameworks (smolagents, LangGraph, AutoGen, CAMEL, LlamaIndex, i.a.). Yet many existing benchmarks are model-centric: they fix the agentic setup and do not compare other system components. We argue that implementation decisions substantially impact performance, including choices such as topology, orchestration logic, and error handling. MASEval addresses this evaluation gap with a Python library that treats the entire agentic system as the unit of analysis. Important design decisions such as harness and context engineering are first-class citizens. MASEval helps practitioners identify the best implementation for their use case and researchers systematically study agentic systems, opening new avenues for principled system design. Through the first systematic system-level comparison across 3 benchmarks, 3 models, and 3 frameworks, we find that, across models of comparable cost and capability, framework choice matters as much as model choice. MASEval is available under the MIT licence at https://github.com/maseval/MASEval.

cs.CL · cs.AI · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.35ACL Anthology · ACL 2026

MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing

Yang Liu, Jinxuan Cai, Yishen Li, Qi Meng Beijing University of Posts and Telecommunications, Zedi Liu, Xin Li Free Org., Chen Qian Shanghai Jiaotong University, Chuan Shi Beijing University of Post and Telecommunication 等 9 位作者

原文摘要

Large language model-based (LLM-based) multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration. MAS workflows can be naturally modeled as directed computation graphs, where nodes execute agents or sub-workflows and edges encode dependencies and message passing. However, implementing complex graph workflows in current frameworks still requires substantial manual effort, offers limited reuse, and makes it difficult to integrate heterogeneous external context sources. To overcome these limitations, we present MASFactory, a graph-centric framework for orchestrating LLM-based MAS. It introduces Vibe Graphing, a human-in-the-loop approach that compiles natural-language intent into an editable workflow specification and then into an executable graph. In addition, the framework provides reusable components, skill support, multimodal message handling, and pluggable context integration, as well as a visualizer for topology preview, runtime tracing, and human-in-the-loop interaction. We evaluate MASFactory on seven public benchmarks, validating both reproduction consistency for representative MAS methods and the effectiveness of Vibe Graphing. Our code (https://github.com/BUPT-GAMMA/MASFactory, licensed under Apache-2.0) and video demonstration (https://youtu.be/ANynzVfY32k) are publicly available.

cs.CL · cs.AI · cs.MA · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.36ACL Anthology · ACL 2026

FactSearch: An Interactive Agentic Fact Search System for Verifying Large Language Model Outputs

Meng Fang University of Liverpool and Eindhoven University of Technology, Harry Mackenzie University of Liverpool

原文摘要

Large language models (LLMs) frequently generate factually incorrect or unverifiable statements, motivating tool-augmented verification systems that combine model reasoning with external evidence retrieval. For factuality evaluation to be scientifically reliable, verification pipelines must be controllable and reproducible: retrieval configuration and reasoning behaviour should be explicitly configurable and stable across runs. In practice, many existing systems depend on commercial search APIs whose ranking policies and retrieval behaviours are opaque and externally controlled, introducing uncontrolled variability into evaluation. This makes it difficult to disentangle reasoning errors from retrieval effects. We present FactSearch, a reproducibility-oriented agentic fact search system for claim-level factuality verification, built on a locally aggregated open-source search infrastructure. FactSearch follows an agentic verification workflow: it decomposes model outputs into atomic factual claims, generates targeted search queries, retrieves supporting evidence via a self-hosted meta-search engine, and performs modular verification within a fully configurable pipeline. By treating retrieval infrastructure as a first-class component, the system enables systematic analysis of retrieval–reasoning interactions. An interactive web interface supports transparent inspection and practical deployment. The project is available at https://factsearch.github.io.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.37ACL Anthology · ACL 2026

Potato 2.0: A Comprehensive Annotation Platform with AI-in-the-Loop Support

David Jurgens University of Michigan - Ann Arbor, Michael Chen, Lina Iyer

原文摘要

Annotated data remains essential for training and evaluating NLP systems. Large language models have broadened the kinds of data researchers need, including multimodal and agentic system data. Here, we introduce Potato 2.0, a major update to our open source annotation platform designed for easy deployment, customization, and fully reproducible and shareable annotation designs. Potato offers broad support for many types of annotations in NLP, including 39 different types of annotation tasks, support for text, audio, image, and video modalities, or mixtures thereof. Potato 2.0 includes robust support for labeling agentic system outputs through reading common trace formats, or live interaction and annotation with agents in multiple settings, such as chatting, web-browsing, and coding. Potato also includes multiple AI-assistance features to help annotators more easily label data. Finally, Potato introduces a new agentic AI-in-the-loop workflow where a single human annotator collaborates with an LLM through iterative prompt refinement, uncertainty-driven instance selection, and progressive autonomy—enabling efficient dataset creation without a large annotation team.

cs.CL · cs.AI · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.38ACL Anthology · ACL 2026

mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models

Jakub Muszyński CERN, Paweł Pozorski Warsaw University of Technology, Maria Ganzha Warsaw University of Technology

原文摘要

We present mllm-shap, an open-sourcePython platform for researchers and ML practitioners that extends Shapley value (SV)explainability from text-only large languagemodels to multimodal LLMs (MLLMs) thatjointly process text and audio. Buildingon the token-level SV framework introducedby TokenSHAP, mllm-shap addresses threechallenges absent in the text-only setting:(1) modality-aware coalition masking thathandles the coexistence of text tokens anddense audio encoder frames within a single input, (2) multi-turn conversation tracking withper-token role and modality metadata, and(3) audio token grouping via phonetic alignment that reduces the coalition space by 10–50 times. The platform ships as a pip-installablepackage implementing five SV estimation strategies – including a Complementary Contributions estimator with Neyman-optimal allocation that outperforms Monte Carlo baselines – together with an interactive web GUIfor real-time attribution visualization. Toour knowledge, mllm-shap is the first publicly available framework for complete, reproducible SV-based explainability of text-audioMLLMs. The package is MIT-licensed withfull source code on GitHub and a demonstration video included as supplementary material.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.39ACL Anthology · ACL 2026

PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf

Jiarui Liu, Terry Jingchen Zhang Vector Institute, Ryan Faulkner DeepMind, Xuanqiang Angelo Huang, Vilém Zouhar Department of Computer Science, ETHZ - ETH Zurich, Dominik Glandorf EPFL - EPF Lausanne, Isabel Dahlgren, Rishit Dagli University of Toronto 等 21 位作者

原文摘要

Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because reviewing research papers is labor-intensive. Emerging AI-powered writing assistants largely focus on grammar fixes or simulating peer review with final scores, yet they fall short of providing concrete, actionable suggestions that help students improve their papers during drafting. We present PaperMentor, a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors. PaperMentor integrates an expert skill library carefully curated from established researchers’ writing advice with 12 specialized agents covering different aspects of paper writing, such as formatting compliance, phrasing accuracy, and terminology consistency. In a user study (n=14), 90.6% of the generated comments were rated actionable and 67.5% were rated valid, significantly outperforming a GPT-5.2 baseline without the skill library. We release PaperMentor as open source for public use.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.4ACL Anthology · ACL 2026

SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding

Runcong Zhao, Qinglin Zhu King's College London, University of London, Hainiu Xu, Bin Liang The Chinese University of Hong Kong, Lin Gui King's College London, University of London, Yulan He King's College London, University of London

原文摘要

Understanding character relationships is essential for interpreting complex narratives and conducting socially grounded AI research. However, manual annotation is time-consuming and low in coverage, while large language models (LLMs) often produce hallucinated or logically inconsistent outputs. We present SymbolicThought, a human-in-the-loop framework that combines LLM-based extraction with symbolic reasoning. The system constructs editable character relationship graphs, refines them using seven types of logical constraints, and enables real-time validation and conflict resolution through an interactive interface. To support logical supervision and explainable social analysis, we release a dataset of 160 interpersonal relationships with corresponding logical structures. Experiments show that SymbolicThought improves annotation accuracy and consistency while significantly reducing time cost, offering a practical tool for narrative understanding, explainable AI, and LLM evaluation. The source code and dataset are publicly available on GitHub (https://github.com/BLPXSPG/SymbolicThought).

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.40ACL Anthology · ACL 2026

Gardener: An Agentic AI System for Single-Cell RNA Sequence Analysis

Junhan Liu, Zhenke Liu Brown University, Yongcheng Shi, Peilin Yu Apple, Minxing Zhang, Jiapeng Zhang University of Southern California

原文摘要

Gardener is an interactive agentic system for single-cell RNA-seq (scRNA-seq) analysis that enables expert-steered, iterative workflows under strict data-residency requirements. Existing large language model (LLM)-based analysis agents commonly encode workflow progress as implicit conversational state and rely on cloud-centric execution, which hinders traceability and auditability and complicates keeping sensitive expression data on-device. Gardener grounds cloud-side reasoning in a local, on-device scientific engine and an Experiment Management Kernel (EMK) that externalizes analysis progress as persistent, immutable snapshots linked by lineage. This explicit state representation supports rollback, branching, and comparison of alternative analysis paths while reusing prior computation. Gardener enforces data isolation by design: cloud-hosted LLMs operate only on snapshot identifiers and sanitized summaries, while raw expression matrices and local artifacts remain on the user’s device. A local graphical user interface (GUI) provides human-in-the-loop steering and inspection of workflow state and outputs. Gardener is released as an open-source desktop application for macOS and Windows under the Apache License 2.0.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

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

TokCollate: A Comprehensive Tool for Tokenizer Evaluation and Visualization across Languages

Dušan Variš Charles University Prague, Abishek Stephen, Jindřich Libovický Charles University Prague

原文摘要

Tokenization quality varies significantly across languages, contributing to disparities in LLM performance and cost for speakers of less-resourced languages – a phenomenon known as the “token premium” problem. Despite growing research interest, no existing tool provides a comprehensive intrinsic evaluation of tokenizers paired with interactive visualization. We present TokCollate (pronounced similarly to chocolate), a Python-based evaluation framework combined with a JavaScript visualization interface that addresses this gap. TokCollate implements a wide range of intrinsic metrics, including monolingual measures such as average token length and Rényi/Shannon efficiency, and cross-lingual measures such as vocabulary overlap, Jensen-Shannon divergence, alignment-based Eflomal scores, and length ratios. It further enables analysis across language groups defined by genealogical families, scripts, geographic regions, speaker populations, and estimated data availability. TokCollate is open-source under the MIT license and available on GitHub.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.42ACL Anthology · ACL 2026

A System for Dynamically Tracking Content Moderation on Reddit

George Arthur Baker University of Chicago, Bharadwaj Kadiyala University of Utah

原文摘要

Recent work in natural language processing, human-computer interaction, and computational social science takes interest in the study of decentralized content moderation, in which individual communities largely determine their own norms, rules, and enforcement thereof. A key challenge to this body of work is that, once moderated, content and related variables become difficult or impossible to recover; previous work often relied on 3rd-party historical data sources, but recent world events, legal disputes, and policy shifts have significantly disrupted these services, practically disabling their research use-cases. As a result, in order to conduct new research and reproduce previous results, researchers must record content as it’s created, and monitor variables of interest over time. In this paper we present and publicly release a software system for the dynamic monitoring of Reddit posts, communities, and moderation actions, to enable scalable and reproducible research on decentralized platform governance and content moderation. To the authors’ knowledge, at the time of publication this system is the only available solution for general-purpose, real-time, policy-compliant longitudinal data collection on Reddit. Furthermore, the system’s integration with the official Reddit API enables the collection of authentication-gated data such as community engagement metrics and moderation team information, which was unavailable in previous historical data sources.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.43ACL Anthology · ACL 2026

AI Steerability 360: A Toolkit for Steering Large Language Models

Erik Miehling IBM Research, Karthikeyan Natesan Ramamurthy International Business Machines, Praveen Venkateswaran International Business Machines, Ching-Yun Ko International Business Machines, Pierre Dognin International Business Machines, Moninder Singh International Business Machines, Tejaswini Pedapati, Avinash Balakrishnan International Business Machines 等 13 位作者

原文摘要

The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. Steering abstractions are designed around four model control surfaces: input (modification of the prompt), structural (modification of the model’s weights or architecture), state (modification of the model’s activations and attentions), and output (modification of the decoding or generation process). Steering methods exert control on the model through a common interface, termed a steering pipeline, which additionally allows for the composition of multiple steering methods. Comprehensive evaluation and comparison of steering methods/pipelines is facilitated by use case classes (for defining tasks) and a benchmark class (for performance comparison on a given task). The functionality provided by the toolkit significantly lowers the barrier to developing and comprehensively evaluating steering methods. The toolkit is Hugging Face native and is released under an Apache 2.0 license at https://github.com/IBM/AISteer360.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

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

Media-to-Insights: A Multi-Agent AI System for Continuous Media Monitoring, Analysis, and Reporting

Ashraf Hatim Elneima, Ozan Yilmaz aiXplain, Hadi Nasrallah, Sanjika Hewavitharana eBay Inc., Mohamed Al-Badrashiny aiXplain, Hassan Sawaf aiXplain

原文摘要

Continuous monitoring of high-volume media streams requires systems that go beyond keyword alerts to deliver structured, actionable intelligence. We present a multi-agent media monitoring system that processes streaming articles through three stages: (1) a Matching Agent that uses a hybrid keyword-then-semantic matching approach, reducing agent invocations by 2̃0% (2) a batched multi-agent feature extraction, reducing core feature-extraction calls from 7 to 2 per article - a 71% reduction - with bounded quality tradeoffs; and (3) a Report Generation Agent that uses deterministic deduplication and density-based clustering. Four autonomous life-cycle agents manage the evolution of watchers.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.45ACL Anthology · ACL 2026

LinkNav: Surfacing Interconnected Information in Scientific Articles

Sebastian Antony Joseph University of Texas at Austin, Jennifer Healey Adobe Systems, Junyi Jessy Li University of Texas at Austin, Ani Nenkova Adobe Research

原文摘要

We present LinkNav, an enhanced experience for reading academic papers which makes explicit connections between related but non-adjacent passages. To create the experience, we instruct a language model to generate questions that may arise while reading a passage and then search for answer-bearing passages elsewhere in the document, forming intra-document connections when answers are found. We confirm that these building blocks work well to power the experience, with an answer detection pipeline that works with high precision, resulting in a reasonable number of such connections being made for a document. On a dataset of academic papers, we find that connected segments are on average ten segments away from each other, making explicit connections that a reader may have otherwise missed.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.46ACL Anthology · ACL 2026

FourCorners: Grounded Thai Legal Research over a Temporal Knowledge Graph

Pawitsapak Akarajaradwong Chulalongkorn University, Thitiwat Nopparatbundit VISAI AI, Treephop Saeteng VISAI AI Co., Ltd., Kasidit Phoncharoen Vidyasirimedhi Institute of Science and Technology, Sarana Nutanong, Chompakorn Chaksangchaichot Chulalongkorn University and Vidyasirimedhi Institute of Science and Technology

原文摘要

Legal practitioners in Thailand must navigate fragmented government websites to research over 3,800 active laws and 87,000 Supreme Court decisions, with no unified tool for cross-referencing, version tracking, or structural navigation. We present FourCorners, a deployed platform that addresses five practitioner pain points through three modules built on a temporal legal knowledge graph covering 552K nodes and 6.3M edges: (1) an AI legal assistant that performs grounded generation via structured graph retrieval, streaming verified source content inline with responses; (2) an interactive law reader that translates graph structure into navigation and comparison features; and (3) a court decision explorer with version-aware citations produced by temporal entity resolution across 87,394 rulings. The system discovers implicit cross-corpus relationships through co-citation analysis of Supreme Court decisions. Interviews with 20 legal professionals reveal that inline source verification fundamentally changes how practitioners interact with AI-generated legal content, and that cross-corpus enrichment surfaces legal relationships that existing tools leave invisible.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.47ACL Anthology · ACL 2026

rosaOS: Agentic Operating System for Embodied LLMs

Yijun Ge University of Waterloo, Kushaldeep Mujral, Karthik Nambiar, Jimmy Lin University of Waterloo

原文摘要

We present rosaOS, an open-source agentic operating system for embodied LLMs: interactive, LLM-driven agents coordinate various software tools and physical devices through a desktop companion, the Reachy Mini robot. Existing LLM–robotic systems are generally built as a tight, intertwined stack, making it difficult to switch hardware, add extra capabilities, or expand to multiple devices without bespoke integration. Our system aims to provide a classic OS-inspired architecture where an agentic kernel manages all task execution and mediates device access, while process agents invoke tools to perform actions. We adopt industry-standard interfaces with MCP for agentic tool-calling and ROS for robot interactions, and demonstrate rosaOS on a multi-device setup including a quadruped robot, a wheeled mobile robot, and a smart lamp, all controlled through interactions with the Reachy Mini. By incorporating MCP extensibility with ROS hardware interoperability, rosaOS enables a plug-and-play ecosystem for building embodied agentic systems. Our OS is available at rosaos.ai.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.48ACL Anthology · ACL 2026

CritiSense: Critical Digital Literacy and Resilience Against Misinformation

Firoj Alam Qatar Computing Research Institute, Fatema Ahmad Hamad Bin Khalifa University, Ali Ezzat Shahroor Hamad Bin Khalifa University, Mohamed Bayan Kmainasi University of Qatar, Elisa Sartori, Giovanni Da San Martino University of Padua, Abul Hasnat, Raian Ali Hamad Bin Khalifa University

原文摘要

Misinformation on social media undermines informed decision-making and public trust. Prebunking offers a proactive complement by helping users recognize manipulation tactics before they encounter them in the wild. We present CritiSense, a mobile media-literacy app that builds these skills through short, interactive challenges with instant feedback. It is the first multilingual (supporting nine languages) and modular platform, designed for rapid updates across topics and domains. We report a usability study with 93 users: 83.9% expressed overall satisfaction and 90.1% rated the app as easy to use. Qualitative feedback indicates that CritiSense helps improve digital literacy skills. Overall, it provides a multilingual prebunking platform and a testbed for measuring the impact of microlearning on misinformation resilience. Over 6 months, we have reached 500+ active users. It is freely available on the Apple App Store and Google [Play Store](https://play.google.com/store/apps/details?id=com.critisense hl=en).

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.49ACL Anthology · ACL 2026

MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval

Shuheng Chen, Namratha Patil, Haonan Pan, Angel Hsing-Chi Hwang University of Southern California, Yao Du University of Southern California, Ruishan Liu University of Southern California, Jieyu Zhao University of Southern California

原文摘要

Clinical decision-making requires synthesizing heterogeneous evidence, including patient histories, clinical guidelines, and trajectories of comparable cases. While large language models (LLMs) offer strong reasoning capabilities, they remain prone to hallucinations and struggle to integrate long, structured medical documents. We present MED-COPILOT, an interactive research prototype for evidence-aware clinical reasoning, designed to help clinicians and medical trainees inspect guideline-level and patient-level evidence. MED-COPILOT combines guideline-grounded GraphRAG retrieval with hybrid semantic-keyword similar-patient retrieval to support transparent and evidence-aware clinical reasoning. The system builds a structured knowledge graph from WHO and NICE guidelines, applies community-level summarization for efficient retrieval, and maintains a 36,000-case similar-patient database derived from SOAP-normalized MIMIC-IV notes and Synthea-generated records.We evaluate our framework on clinical note completion and medical question answering, and demonstrate that it consistently outperforms parametric LLM baselines and standard RAG, improving generation fidelity and benchmark QA accuracy. The full system is available at https://huggingface.co/spaces/shuhengc/MED-COPILOT, enabling users to inspect retrieved evidence, visualize token-level similarity contributions, and conduct guided follow-up analysis. Our results suggest a practical and interpretable approach to integrating structured guideline knowledge with patient-level analogical evidence for clinical LLMs.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.5ACL Anthology · ACL 2026

LiTS: A Modular Framework for LLM Tree Search

Xinzhe Li Royal Melbourne Institute of Technology, Yaguang Tao

原文摘要

LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components—Policy, Transition, and RewardModel—that plug into algorithms like MCTS and BFS. A decorator-based registry enables domain experts to extend to new domains by registering components, and algorithmic researchers to implement custom search algorithms. We demonstrate composability on MATH500 (language reasoning), Crosswords (environment planning), and MapEval (tool use), showing that components and algorithms are orthogonal: components are reusable across algorithms within each task type, and algorithms work across all components and domains. We also report a mode-collapse finding: in infinite action spaces, LLM policy diversity—not reward quality—is the bottleneck for effective tree search. A demonstration video is available at https://youtu.be/nRGX43YrR3I. The package is released under the Apache 2.0 license at https://github.com/xinzhel/lits-llm, including installation instructions and runnable examples that enable users to reproduce the demonstrated workflows.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

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

PathBuilder: A Quality-Controlled LLM System for Personalized Learning Pathways

Jasper Meynard Arana Adamson University, John Andrew Mañacop, John Allen Manacop Asian Institute of Management, Roy Andrew Garcia Makarius Smart Learning, Keith Rick Piniera Asian Institute of Management, Kristine Ann M. Carandang Professional Regulation Commission, Asian Institute of Management and Asian Institute of Management, Ethan Robert Casin, Christian Alis Asian Institute of Management 等 9 位作者

原文摘要

Large language models (LLMs) enable scalable content generation for personalized learning, but reliability and pedagogical alignment remain open challenges. We present PathBuilder, a web-based system that integrates expert-validated assessment, retrieval-augmented generation (RAG), and an LLM-as-a-Judge validation loop within a closed instructional pipeline. The system uses a 17,758-item curriculum-aligned question bank, including 1,018 expert-approved LLM-generated items, to construct diagnostic and post-tests for fine-grained learner profiling. In a real-world deployment with 179 registered users (75 matched learners), PathBuilder achieved a mean absolute gain of 37.9 percentage points, Hake’s normalized gain of 0.760, and a large effect size (Cohen’s d = 0.98). A controlled study of the judge mechanism showed consistent high-quality instructional outputs with a 100% threshold pass rate. These results demonstrate that structured curriculum alignment combined with retrieval grounding and automated validation can support reliable LLM-based personalization in deployed learning systems. A live demonstration of PathBuilder is available at https://demo.pathbuilderedu.com.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.51ACL Anthology · ACL 2026

AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge

Karen Zhou University of Chicago, Chenhao Tan University of Chicago

原文摘要

Checklists have emerged as a popular approach for interpretable and fine-grained evaluation, particularly with LLM-as-a-Judge. Beyond evaluation, these structured criteria can serve as signals for model alignment, reinforcement learning, and self-correction. To support these use cases, we present AutoChecklist, an open-source library that unifies checklist-based evaluation into composable pipelines. At its core is a taxonomy of five checklist generation abstractions, each encoding a distinct strategy for deriving evaluation criteria. A modular \textit{Generator $\rightarrow$ Refiner $\rightarrow$ Scorer} pipeline connects any generator with a unified scorer, and new configurations can be registered via prompt templates alone. The library ships with ten built-in pipelines implementing published approaches and supports multiple LLM providers (OpenAI, OpenRouter, vLLM). Beyond the Python API, the library includes a CLI for off-the-shelf evaluation and a web interface for interactive exploration. Validation experiments confirm that these checklist methods significantly align with human preferences and quality ratings, and a case study on ICLR peer review rebuttals demonstrates flexible domain adaptation. AutoChecklist is publicly available at https://github.com/ChicagoHAI/AutoChecklist.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.52ACL Anthology · ACL 2026

JobMatchAI - An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI

Mayank Vyas, Abhijit Chakraborty Arizona State University and MongoDB, Vivek Gupta Arizona State University

原文摘要

Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most systems act as keyword filters, failing to handle skill synonyms and nonlinear careers, resulting in missed candidates and opaque match scores. We introduce JobMatchAI, a production-ready system integrating Transformer embeddings, skill knowledge graphs, and interpretable reranking. Our system optimizes utility across skill fit, experience, location, salary, and company preferences, providing factor-wise explanations through resume-driven search workflows. We release JobSearch-XS benchmark and a hybrid retrieval stack combining BM25, knowledge graph and semantic components to evaluate skill generalization. We assess system performance on JobSearch-XS across retrieval tasks, provide a demo video, a hosted website and installable package.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.53ACL Anthology · ACL 2026

Qayyem: A Real-time Platform for Scoring Proficiency of Arabic Essays

Hoor Tamer Elbahnasawi University of Qatar, Marwan Sayed, Sohaila Eltanbouly University of Qatar, Fatima Zahra Brahamia, Tamer Elsayed Qatar University

原文摘要

Over the past years, Automated Essay Scoring (AES) systems have gained increasing attention as scalable and consistent solutions for assessing proficiency of student writing. Despite recent progress, support for Arabic AES remains limited due to linguistic complexity and scarcity of large publicly-available annotated datasets. In this work, we present Qayyem, a Web-based platform designed to support Arabic AES by providing an integrated workflow for assignment creation, batch essay upload, scoring configuration, and per-trait essay evaluation. Qayyem abstracts the technical complexity of interacting with scoring server APIs, allowing instructors to access advanced scoring services through a user-friendly interface. The platform deploys a number of state-of-the-art Arabic essay scoring models with different effectiveness and efficiency figures.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.54ACL Anthology · ACL 2026

PhyVer: Physics-Grounded Material Claim Verification with Multi-Fidelity Physical Evidence

Jianpeng Chen, Wangzhi Zhan Virginia Polytechnic Institute and State University, Haohui Wang, Brian Mayer Virginia Polytechnic Institute and State University, Dongqi Fu Meta, Dawei Zhou Virginia Polytechnic Institute and State University

原文摘要

Material claims in papers, patents, etc., often involve physical feasibility (e.g., stability under conditions, property consistency), not just textual feasibility. Yet most claim verifiers operate over language, therefore producing ungrounded judgments. On the other hand, directfirst-principles verification (e.g., density functional theory, DFT) is inflexible and hard to invoke from underspecified free-form claims.Therefore, we introduce PhyVer, a physics-grounded material claim verification system that bridges this gap by translating claimsinto multi-fidelity physical evidence and interpretable verdicts. To support human-in-the-loop inspection, we present an interactive web interface that visualizes the instantiated structure, optimization trajectories, DFT summaries, and the final decision. On expert-labeled claims, PhyVer improves agreement with experts over text-only GPT-5.1, reducing MAE from 1.54 to 1.20 and Signed MAE from0.95 to 0.82, and increasing Accuracy@±1 from 50% to 70%.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.55ACL Anthology · ACL 2026

pAtChWoRK: Patching the Pieces of Public Procurement Documents

Lorena Calvo-Bartolomé Bocconi University, Saúl Blanco Fortes, Erick Cedeño, Jerónimo Arenas-García Universidad Carlos III de Madrid

原文摘要

Public procurement data is legally open, yet practically locked inside thousands of unstructured PDFs and inconsistent portal metadata. pAtChWoRK starts with these fragmented, unstructured sources then leverages a hybrid pipeline (traditional NLP with LLM-based technologies) to restructure this information into a navigable knowledge base. Specifically, pAtChWoRK corrects manual classification errors, extracts complex unstructured fields such as award and solvency criteria and tenders’ objectives, and assists users in easily navigating the tender landscape. This unified process enables more effective handling of the transparency bottlenecks that hinder competition and oversight in public administration. A user study with practitioners across different procurement

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.56ACL Anthology · ACL 2026

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models

Qundong Shi ModelBest, Jie Zhou, Biyuan Lin, Junbo Cui ModelBest, Guoyang Zeng ModelBest, Yixuan Zhou Tsinghua University, Ziyang Wang, Xin Liu 等 11 位作者

原文摘要

The development of audio foundation models has accelerated rapidly since the emergence of GPT-4o. However, the lack of comprehensive evaluation has become a critical bottleneck for further progress in the field, particularly in audio generation. Current audio evaluation faces three major challenges: (1) audio evaluation lacks a unified framework, with datasets and code scattered across various sources;(2) audio codec, as a key component of audio foundation models, lacks a widely accepted and holistic evaluation methodology; (3) existing speech benchmarks are heavily reliant on English, making it challenging to objectively assess models’ performance on Chinese.We introduce UltraEval-Audio, a unified framework addressing these challenges through a modular architecture supporting 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards. For audio codec, we propose a three-dimensional evaluation scheme covering semantic accuracy, timbre fidelity, and acoustic quality. For Chinese evaluation, we introduce two new benchmarks: SpeechCMMLU and SpeechHSK. Our code, benchmarks, and leaderboards are available at https://github.com/OpenBMB/UltraEval-Audio.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.57ACL Anthology · ACL 2026

Paper2Web: Let’s Make Your Paper Alive!

Yuhang Chen, Tianpeng Lv Huazhong University of Science and Technology, Yao Wan Huazhong University of Science and Technology, Philip S. Yu University of Illinois Chicago, Dongping Chen

原文摘要

Academic project websites can more effectively disseminate research when they clearly present core content and enable intuitive navigation and interaction. However, current approaches such as direct Large Language Model (LLM) generation, templates, or direct HTML conversion struggle to produce layout-aware, interactive sites, and a comprehensive evaluation suite for this task has been lacking. In this paper, we introduce Paper2Web, a benchmark dataset and multi-dimensional evaluation framework for assessing academic webpage generation. It incorporates rule-based metrics like Connectivity, Completeness and human-verified LLM-as-a-Judge (covering interactivity, aesthetics, and informativeness), and PaperQuiz, which measures paper-level knowledge retention. We further present PWAgent, an autonomous pipeline that converts scientific papers into interactive and multimedia-rich academic homepages. The agent iteratively refines both content and layout through MCP tools that enhance emphasis, balance, and presentation quality. Our experiments show that PWAgent consistently outperforms end-to-end baselines like template-based webpages and arXiv/alphaXiv versions by a large margin while maintaining low cost, achieving the Pareto-front in academic webpage generation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

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

OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction

Junxing Hu JD.com, Tianlong Li, Lei Yu, Ai Han JD.com

原文摘要

Deploying production-ready multi-agent systems (MAS) in complex industrial environments remains challenging due to limitations in scalability, observability, and autonomous evolution. We present OxyGent, an open-source framework driven by two core novelties: a unified Oxy abstraction and the OxyBank evolution engine. The unified abstraction encapsulates agents, tools, LLMs, and reasoning flows as pluggable atomic components, enabling Lego-like scalable system composition and non-intrusive monitoring. To enhance observability, OxyGent introduces permission-driven dynamic planning that replaces rigid workflows with execution graphs generated at runtime, providing adaptive visualizations. Furthermore, to support continuous evolution, OxyBank serves as an AI asset management platform that drives automated data backflow, annotation, and joint evolution. Empirical evaluations and real-world case studies show that OxyGent provides a robust and scalable foundation for MAS. OxyGent is fully open-sourced under the Apache License 2.0 at https://github.com/jd-opensource/OxyGent.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.59ACL Anthology · ACL 2026

EduCoder: An Open-Source Annotation System for Education Transcript Data

Saad Ashraf, Jim Malamut Stanford University, Vishal Kumar, Guanzhong Pan, HyunJi Nam Stanford University, Mei Tan, Lucía Langlois, Liliana Carolina Santos-Deonizio 等 10 位作者

原文摘要

We present EduCoder, an open-source web platform designed for annotating classroom conversation transcripts. Existing annotation tools do not support the team-based workflows or access to instructional context that education discourse research requires. EduCoder addresses these gaps by combining transcript text, synchronized video, and instructional materials within a single workspace. The platform supports scoping annotation to specific portions of a lesson, coordinating work across annotation teams, and optionally integrating LLM-generated annotations with structured human–LLM comparison. EduCoder is freely accessible at https://edu-coder.com.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.6ACL Anthology · ACL 2026

AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling

Zhen Lin Hefei University of Technology, Qiujie Xie Westlake University, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding Westlake University, Ke Sun 等 13 位作者

原文摘要

High-quality scientific illustrations are essential for communicating complex scientific and technical concepts, yet existing automated systems remain limited in editability, stylistic controllability, and efficiency. We present AutoFigure-Edit, an end-to-end system that generates fully editable scientific illustrations from long-form scientific text while enabling flexible style adaptation through user-provided reference images. By combining long-context understanding, reference-guided styling, and native SVG editing, it enables efficient creation and refinement of high-quality scientific illustrations. To facilitate further progress in this field, we release the video at https://youtu.be/10IH8SyJjAQ, the full codebase at https://github.com/ResearAI/AutoFigure-Edit and provide a live demo for easy access and interactive use at https://autofigure.cc/.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.60ACL Anthology · ACL 2026

CPTCoder: A Reliable LLM System for Medical Procedure Code Prediction

Benlu Wang, Ziyao Shangguan Yale University, Kyle Tegtmeyer, Zhenyu Zhang, Sophie Chheang, Arman Cohan Yale University and Allen Institute for Artificial Intelligence

原文摘要

We present CPTCoder, a human-in-the-loop system that predicts standardized medical procedure codes from clinical text. Clinical procedure coding is an extreme multi-label classification problem over a long-tailed space of short numeric identifiers, where a single-digit difference denotes an entirely different procedure. CPTCoder adapts an instruction-tuned LLM with a code-aware vocabulary and constrained decoding that guarantees all outputs are valid codes. To support human review, we derive per-code posterior inclusion probabilities from n-best reweighting, producing interpretable confidence scores that rank predictions and flag uncertain cases. A post-decoding constraint repair step enforces mutual-exclusion rules between conflicting codes. To enable reproducible research in this underexplored setting, we release MIMIC-CPT, a PhysioNet-accessible benchmark of 37,885 expert-cleaned report–code pairs with a deliberately hardened test split: 88% of test examples contain label combinations unseen during training, and over a third include codes with five or fewer training occurrences. We additionally provide 413,085 weakly aligned pairs and evaluate on a separate live dataset from a hospital, which includes out-of-domain radiology reports with billing-expert-verified labels. CPTCoder achieves 0.61 and 0.51 micro-F1 on the hardened MIMIC split and Hospital-298 respectively, outperforming the strongest baseline by 12 and 5 absolute points while reducing digit-level near-miss errors.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表