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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.20ACL Anthology · ACL 2026

ClinQueryAgent: A Conversational Agent for Population Health Management

Joseph Spartacus Boyle, Anthony Michael Dranfield National Health Service, Mike O’Neil, Maria Liakata Queen Mary University London, Alison Q. Smithard

原文摘要

In this paper we introduce CLINQUERYAGENT, a system for translating natural language population health questions into executable database queries using agents with access to both local and external knowledge bases. Our novel architecture enables the use of powerful cloud-based language models whilst ensuring that no patient data leaves the secure environment. To combat inaccuracies over the course of longer dialogues due to context rot, information retrieval is delegated to a sub-agent. We deploy the system via a chat window embedded within an existing population health management platform where it has been used by 128 staff from 15 healthcare practices covering a total of 148,319 patients in the UK’s National Health Service (NHS). We evaluate the system’s capacity to autonomously handle a range of health informatics tasks on three datasets and via a beta-testing phase. Our results show that both analysts and clinicians are able to easily generate actionable information from patient health records using natural language requests requiring no programming expertise to verify. A public demo of the system is available to try: https://clinqueryagent.josephsboyle.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.21ACL Anthology · ACL 2026

Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation

Abdelrahman Abdallah, Bhawna Piryani Universität Innsbruck, Jamshid Mozafari Universität Innsbruck, Andreas Herzinger Universität Innsbruck, Jamie Holdcroft, Adam Jatowt

原文摘要

Building retrieval-augmented generation (RAG) systems often requires combining separate tools for retrieval, re-ranking, and generation, with incompatible data formats, evaluation pipelines, and deployment workflows. We present Rankify, an open-source Python toolkit that unifies these stages in a single modular framework. Rankify provides 42 benchmark datasets with pre-retrieved documents and pre-built indices, 15 retrievers (sparse, dense, and reasoning-augmented), and 24 re-ranking models spanning 41 pointwise, pairwise, and listwise variants. It also supports 6 RAG strategies across four inference backends (Hugging Face, vLLM, LiteLLM, and OpenAI), enabling consistent experimentation from local models to hosted APIs. A unified pipeline interface allows users to compose retrieve–rerank–generate workflows in a few lines of code, while an agentic assistant (RankifyAgent), aREST server (RankifyServer), and an interactive web playground support deployment and non-programmatic exploration. Across 200+ configurations on QA and BEIR/TREC benchmarks with six generator LLMs, re-ranking consistently improves downstream performance, yielding gains of 5–15 points in Exact Match and up to 8.5 points in RAGAS context precision across diverse retriever–generator combinations. Rankify is released under the Apache-2.0 License.

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.22ACL Anthology · ACL 2026

ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery

Shahar Levy, Eliya Habba, Reshef Mintz, Barak Raveh, Renana Keydar Hebrew University of Jerusalem, Gabriel Stanovsky Hebrew University of Jerusalem

原文摘要

Many disciplines pose natural-language research questions over large document collections whose answers typically requires structured evidence, traditionally obtained by manually designing an annotation schema and exhaustively labeling the corpus, a slow and error-prone process. We introduce ScheMatiQ, which leverages calls to a backbone LLM to take a question and a corpus to produce a schema and a grounded database, with a web interface that lets steer and revise the extraction. In collaboration with domain experts, we show that ScheMatiQ yields outputs that support real-world analysis in law and computational biology. We release ScheMatiQ as open source with a public web interface, and invite experts across disciplines to use it with their own data. All resources, including the website, source code, and demonstration video, are available at: www.ScheMatiQ-ai.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.23ACL Anthology · ACL 2026

GovScape: A Public Multimodal Search System for 70 Million Pages of Government PDFs

Ying-Hsiang Huang University of Washington, Claire Gong Department of Computer Science, University of Washington, Shreya Shaji, Alison R Yan, Leslie Harka University of Washington, Albert Du University of Washington, Anjali Shubha Gopal, Samuel J Klein Knowledge Futures 等 13 位作者

原文摘要

Efforts over the past three decades have produced web archives containing billions of webpage snapshots and petabytes of data. The End of Term Web Archive alone contains millions of PDFs produced by the federal government. While preservation with web archives has been successful, significant challenges for access and discoverability remain. In this paper, we introduce GovScape, a public search system that supports multimodal searches across 10,015,993 federal government PDFs from the 2020 End of Term crawl (70,958,487 total PDF pages) – to our knowledge, all renderable PDFs in the 2020 crawl that are 50 pages or under. GovScape supports four primary forms of search: in addition to providing (1) filter conditions over metadata facets including domain and crawl date and (2) exact text search against the PDF text, we provide (3) semantic text search and (4) visual search against the PDFs across individual pages, enabling users to structure queries such as “redacted documents” or “pie charts.” We detail GovScape’s search affordances, embedding pipeline, system architecture, and open source codebase. Significantly, the total estimated compute cost for GovScape’s pre-processing pipeline for 10 million PDFs was approximately 1,500, equivalent to 47,000 PDF pages per dollar spent on compute, demonstrating the potential for immediate scalability. We evaluate GovScape by (1) analyzing 1,679 search queries and (2) benchmarking vector and keyword index efficiency using these queries. GovScape can be found at https://www.govscape.net.

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.24ACL Anthology · ACL 2026

Bridging Kernel Drivers and Virtual Device Models with LLM-Powered Automation

Mingyu Wang, Bin Yu, Wenjian Lu, Zhi Wang, Gao Kefeng, Cheng Wen Xi'an University of Electronic Science and Technology, Xu Lu Xidian University, Cong Tian

原文摘要

Linux kernel device drivers are tightly coupled with hardware, making them difficult to execute and test without physical devices. This heavily limits automated code analysis and vulnerability discovery. While manual modeling is unscalable, Large Language Models (LLMs) offer a new approach to scale virtual device construction across the Linux driver ecosystem. In this paper, we present DevGen, an LLM-powered tool that generates QEMU-based virtual devices directly from Linux driver source code. DevGen combines static analysis to gather necessary context, guides the LLM through step-by-step prompting, and uses an automated self-correction loop driven by compilation and execution feedback. To further reduce errors, similar fixes are retrieved from a library of common modeling failures and incorporated into the repair prompt, which supports more targeted corrections in later iterations. The generated devices finally integrate with QEMU and Syzkaller, enabling driver fuzzing without physical hardware. DevGen is evaluated on 50 PCI/PCIe drivers from Linux 6.18 using three mainstream LLMs, and successfully generates usable models for 44 drivers. In these drivers, 24% of them achieve significant improvements in fuzzing coverage, and 7 previously unknown crashes are triggered with 1 CVE assigned. These results demonstrate the practical capability of LLMs to automate complex, system-level code generation tasks.

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.25ACL Anthology · ACL 2026

BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation

Yang Qi, Xiangyao Ma funstory.ai, Xiao Wang, Hao Wang Shanghai University, Rui Wang Shanghai Jiao Tong University

原文摘要

As global cross-lingual communication intensifies, language barriers in visually rich documents such as PDFs remain a practical bottleneck. Existing document translation pipelines face a tension between linguistic processing and layout preservation: text-oriented Computer-Assisted Translation (CAT) systems often discard structural metadata, while document parsers focus on extraction and do not support faithful re-rendering after translation. We introduce BabelDOC, an Intermediate Representation (IR)-based framework for layout-preserving PDF translation. BabelDOC decouples visual layout metadata from semantic content, enabling document-level translation operations such as terminology extraction, cross-page context handling, glossary-constrained generation, and formula placeholdering. The translated content is then re-anchored to the original layout through an adaptive typesetting engine. Experiments on a curated 200-page benchmark, together with human evaluation and multimodal LLM-as-a-judge evaluation, show that BabelDOC improves layout fidelity, visual aesthetics, and terminology consistency over representative baselines, while maintaining competitive translation precision. The open-source toolkit and its interactive downstream applications have garnered over 7.8k stars on GitHub https://github.com/funstory-ai/BabelDOC. A demonstration video is available at https://youtu.be/chwrlApH7a4.

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.26ACL Anthology · ACL 2026

JointCoder: Exploring Automated ICD Coding on Real-World Chinese EHRs with a Multi-Agent Framework

Kangjun Liu, Zhenyu Li Qilu University of Science and Technology, Jianlei Wang Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Hongjiao Guan, Ying Lian The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Guoqiang Chen The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Tao Xin The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Wenpeng Lu

原文摘要

Automated ICD coding is a critical task for standardizing clinical information from electronic health records (EHRs) and supporting downstream healthcare administration.However, existing automated ICD coding systems face several fundamental challenges. First, the majority of existing research focuses on English ICD tasks, with limited attention to Chinese-language clinical contexts due to the scarcity of publicly available Chinese ICD datasets. Second, most approaches primarily target disease coding, overlooking procedure coding as well as the multi-stage workflows followed in real-world clinical practice. Moreover, many recent methods rely heavily on closed-source large language models or substantial computational resources, which limits their scalability and deployability in clinical environments.To address these gaps, this paper proposes JointCoder, which includes a real-world Chinese ICD coding dataset and a multi-agent framework that reformulates automated ICD coding as a joint disease-procedure coding task. JointCoder explicitly models real-world clinical coding workflows through stage-wise agent collaboration.All agents are instantiated using locally deployed 1.7B-parameter models, enabling scalable and privacy-preserving deployment.Extensive experiments on real-world Chinese ICD coding datasets demonstrate JointCoder’s superiority over state-of-the-art baselines across all evaluation metrics.

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.27ACL Anthology · ACL 2026

Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects

Christopher Latimer, Nicolò Boschi University of Bologna, Andrew Neeser Washington Post, Chris Bartholomew Vectorize, Gaurav Srivastava, Xuan Wang Virginia Polytechnic Institute and State University, Naren Ramakrishnan Virginia Tech

原文摘要

We demonstrate Hindsight, a working memory system for AI agents that organizes long-term memory into four logical networks and exposes three core operations. The world, experience, observation, and opinion networks separate objective facts from subjective beliefs, giving developers visibility into what an agent knows versus what it believes. The retain, recall, and reflect operations handle ingestion, retrieval, and reasoning respectively, with a parallel pipeline that combines vector search, keyword matching, graph traversal, and temporal filtering, backed by PostgreSQL with pgvector. Unlike existing systems such as MemGPT, Zep, and Mem0, Hindsight is the only one that jointly provides fact-belief separation, temporal entity graphs, evolving opinions with confidence scores, and configurable behavioral profiles. On LongMemEval and LoCoMo, Hindsight with a 20B open-source model reaches 83.6% and 83.2% accuracy, outperforming full-context GPT-4o and all prior memory systems; with Gemini-3 Pro, LongMemEval accuracy reaches 91.4%. Our interactive demo lets users build memory graphs through multi-session conversations, inspect how memories are classified, and watch opinions form and change. The system is open-source under the MIT license, available as a Python package (pip install hindsight-all) and Docker image, with 13.3K GitHub stars and 763 forks to date, and in production use at Fortune 500 enterprises. Video demo: https://youtu.be/4M2wS-yEmVA.

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.28ACL Anthology · ACL 2026

Semantic XPath: Structured Agentic Memory Access for Conversational AI

Yifan Simon Liu, Ruifan Wu, Liam Gallagher, Jiazhou Liang, Armin Toroghi, Scott Sanner Department of Mechanical and Industrial Engineering, University of Toronto and Department of Computer Science

原文摘要

Conversational AI (ConvAI) agents increasingly maintain structured memory to support long-term, task-oriented interactions. In-context memory approaches append the growing history to the model input, which scales poorly under context-window limits. RAG-based methods retrieve request-relevant information, but most assume flat memory collections and ignore structure. We propose Semantic XPath, a tree-structured memory module to access and update structured conversational memory. Semantic XPath improves performance over flat-RAG baselines by 176.7% while using only 9.1% of the tokens required by in-context memory. We also introduce SemanticXPath Chat, an end-to-end ConvAI demo system that visualizes the structured memory and query execution details. Overall, this paper demonstrates a candidate for the next generation of long-term, task-oriented ConvAI systems built on structured memory.

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.29ACL Anthology · ACL 2026

Graph of Trace: Visualizing Execution Traces of Scientific Agents

Tianci Gao Renmin University of China, Haoxuan Li Tsinghua University, Jian He Li, Tianxiang Zhao, Shi Runze, Weiran Wang, Zezhao Wu, Lu Mi Tsinghua University

原文摘要

Scientific AI agents can autonomously carry out complex research workflows, yet these unfolded workflows often remains difficult for humans to inspect and review, limiting interpretable, controllable and effective human–AI collaboration. To address this challenge, we present a monitoring and visualization framework that records fine-grained execution events and organizes them into a directed graph that make agent workflows explicit as they proceed. The system records intermediate steps (e.g. tool calls and code executions), and renders them as real-time updated visual traces that expose workflow structure. This allows users to examine how results are produced, identify where failures emerge, and better understand agent behavior across different stages of the research process.We conduct an evaluation on complex research tasks with domain experts of interdisciplinary background in AI, neuroscience and biology. Experts report that structured traces visualization improves understanding of agent workflows, perceived interpretability, and usability for analysis and further interaction.

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.3ACL Anthology · ACL 2026

PROTEA: Offline Evaluation and Iterative Refinement for Multi-Agent LLM Workflows

Kazuki Kawamura Sony Group Corporation and Sony Computer Science Laboratories, Satoshi Waki Sony Group Corporation, Kei Tateno Sony Group Corporation

原文摘要

Multi-agent LLM workflows, which are AI systems composed of multiple role-specialized LLM calls, often outperform single prompts, but they are notoriously difficult to debug and refine. Failures can originate from subtle mistakes in intermediate artifacts that silently propagate downstream, forcing developers to read long traces and guess which agent to edit. We present PROTEA, a unified UI that closes the loop for offline, test-case–driven improvement of multi-agent workflows, enabling developers to efficiently diagnose and fix errors without manual inspection of long traces. PROTEA executes a workflow, scores intermediate artifacts with configurable evaluators, and overlays per-node states and rationales on the workflow graph to localize likely bottlenecks. To address the difficulty of preparing intermediate reference in complex systems, PROTEA performs backward node evaluation by inferring each node’s ideal expected output from terminal supervision and graph context, and comparing it with the observed node output. For a selected node, it proposes a targeted prompt patch as an editable diff, then automatically re-runs and re-evaluates the workflow to show before/after output diffs and score trajectories within the same interface. Using PROTEA, users can visually pinpoint system-wide bottlenecks at a glance, streamline remediation via semi-automated prompt patching, and immediately verify pre- and post-correction outcomes within a unified loop.

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.30ACL Anthology · ACL 2026

ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models

Jiasheng Zheng Institute of Software, Chinese Academy of Sciences, Xin Zheng, Boxi Cao Institute of Software, Chinese Academy of Sciences, Pengbo Wang, Zhengzhao Ma, Qiming Zhu, Jiazhen Jiang, Yaojie Lu Institute of Software, Chinese Academy of Sciences 等 11 位作者

原文摘要

Code sandboxes have emerged as a critical infrastructure for advancing the coding capabilities of large language models, providing verifiable feedback for both RL training and evaluation. However, existing systems fail to provide accurate verification and efficiency under high-concurrency workloads. We present ScaleBox, a high-fidelity and scalable system designed to address these limitations in large-scale code training. ScaleBox introduces automated special-judge generation and management, fine-grained parallel execution across test cases with seamless multi-node coordination, and a configuration-driven evaluation suite for reproducible benchmarking. A series of experiments demonstrates that ScaleBox significantly enhances code verification accuracy and efficiency. Our further RLVR experiments show that ScaleBox substantially improves both performance on LiveCodeBench and training stability, significantly outperforming heuristic-matching baselines. By providing a reliable and high-throughput infrastructure, ScaleBox facilitates more effective research and development in large-scale code training.

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.31ACL Anthology · ACL 2026

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis

Zhichao Shi UCAS, Cehao Yang The Hong Kong University of Science and Technology, Hao Zhou, Xiaojun Wu The Hong Kong University of Science and Technology and IDEA Institution, Huajie Li International Digital Economy Academy, International Digital Economy Academy, Xuhui Jiang International Digital Economy Academy, International Digital Economy Academy, Chengjin Xu International Digital Economy Academy, Yuanzhuo Wang Chinese Academy of Sciences 等 9 位作者

原文摘要

Synthetic data has emerged as a crucial solution to the data scarcity bottleneck in large language models (LLMs), particularly for specialized domains and low-resource languages. However, the broader adoption of existing synthetic data tools is severely hindered by convoluted workflows, fragmented data standards, and limited scalability across modalities.To address these limitations, we develop DataArc-SynData-Toolkit, an open-source framework featuring: (1) a configuration-driven, end-to-end pipeline equipped with an intuitive visual interface and simplified CLI for exceptional usability; (2) a unified, quality-controllable synthesis paradigm that standardizes multi-source data generation to ensure high reusability; and (3) a highly modular architecture designed for seamless multimodal, multilingual, and multi-task adaptation.We apply the toolkit in multiple application scenarios. Experimental results demonstrate that our toolkit achieves an optimal balance between generation efficiency and data quality. By offering an end-to-end and visually interactive pipeline, DataArc-SynData-Toolkit significantly lowers the technical barrier to synthetic data generation and subsequent model training, accelerating its practical deployment in real-world applications.

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.32ACL Anthology · ACL 2026

HuoziIME: An On-Device LLM-Enhanced Input Method for Deep Personalization

Baocai Shan Harbin Institute of Technology, Yuzhuang Xu, Wanxiang Che Harbin Institute of Technology

原文摘要

Mobile input method editors (IMEs) are the primary interface for text input, yet they remain constrained to manual typing and struggle to produce personalized text. While lightweight large language models (LLMs) make on-device auxiliary generation feasible, enabling deeply personalized, privacy-preserving, and real-time generative IMEs poses fundamental challenges. To this end, we present HUOZIIME, a personalized on-device IME powered by LLM. We endow HUOZIIME with initial human-like prediction ability by post-training a base LLM on synthesized personalization data. Notably, a hierarchical memory mechanism is designed to continually capture and leverage user-specific input history. Furthermore, we perform systemic optimizations tailored to on-device LLM-based IME deployment, ensuring efficient and responsive operation under mobile constraints. Experiments demonstrate efficient on-device execution and high-fidelity memory-driven personalization. Code and package are available at https://github.com/Shan-HIT/HuoziIME.

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.33ACL Anthology · ACL 2026

WIGVO: Real-Time Bidirectional Speech Translation over Legacy PSTN Calls via Dual-Session Echo Gating

Hyeong-seob Kim BrainCrew and WIGTN, Sang-Woo Son Soundmind, Hyun-woo Cho WIGTN and soundmind, Hyeonsang Kim WIGTN and Soundmind, Jinmo Kim WIGTN and Soundmind Inc

原文摘要

Real-time speech translation with large language models (LLMs) has become feasible in controlled wideband settings—mobile apps, web browsers, and end-to-end full-duplex systems pushing latency below 200 ms—where developers can assume client-side echo cancellation. However, deploying such systems over the Public Switched Telephone Network (PSTN) remains challenging due to narrowband G.711 audio, unpredictable round-trip delays, and absence of client-side signal processing. We present WIGVO (WIGTN Voice-Only), a server-side relay system that enables bidirectional LLM-based speech translation over ordinary telephone calls without requiring app installation or carrier integration. A central contribution is addressing what we term echo-induced self-reinforcing translation loops: synthesized speech echoing back through the PSTN gets re-ingested and repeatedly translated. WIGVO solves this through a dual-session architecture with deterministic silence injection and energy-based voice activity detection (VAD) gating. We evaluate WIGVO on 155 Korean–English PSTN calls (148 instrumented, 147 completed) across three communication modes—voice-to-voice (V2V), text-to-voice (T2V), and full-agent—observing 555 ms median caller-to-callee latency and 2,684 ms median callee-to-caller latency, zero echo-induced translation loops, COMET semantic adequacy of 0.71 (en→ko) and 0.62 (ko→en) against offline LLM references, and USD 0.28 per minute cost. The system is deployed at https://wigvo.wigtn.com, with a video walkthrough at https://youtu.be/4Uf6zMPOInY. Evaluation scripts and anonymized call logs are available in the open-source repository.

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.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