Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding (2026.acl-demo)

Challenge:	Multi-agent LLM workflows are notoriously difficult to debug and refine.
Approach:	They propose a unified UI that closes the loop for offline, test-case–driven improvement of multi-agent LLM workflows.
Outcome:	PROTEA performs backward node evaluation and proposes a targeted prompt patch as an editable diff.

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Runcong Zhao, Qinglin Zhu, Hainiu Xu, Bin Liang, Lin Gui, Yulan He

LiTS: A Modular Framework for LLM Tree Search (2026.acl-demo)

Challenge:	Existing methods for constructing character relationships from plain text are time-consuming and low in coverage.
Approach:	They propose a human-in-the-loop framework that combines LLM-based extraction with symbolic reasoning.
Outcome:	The proposed framework improves annotation accuracy and consistency while significantly reducing time cost.

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Xinzhe Li, Yaguang Tao

AutoFigure-Edit: Generating Editable Scientific Illustrations via Reference-Guided Styling (2026.acl-demo)

Challenge:	Existing tree search methods are task-specific and require substantial reimplementation effort when adapting to new domains.
Approach:	They propose a Python framework for LLM reasoning via tree search that decomposes tree search into three reusable components that plug into algorithms like MCTS and BFS.
Outcome:	The proposed framework decomposes tree search into three reusable components that plug into algorithms like MCTS and BFS.

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Zhen Lin, Qiujie Xie, Minjun Zhu, Shichen Li, QiYao Sun, Enhao Gu, Yiran Ding, Ke Sun, Fang Guo, Panzhong Lu, Zhiyuan Ning, Yixuan Weng, Yue Zhang

Green & Sustainable NLP

Challenge:	Existing automated systems for scientific illustrations are limited in editability, stylistic controllability, and efficiency.
Approach:	They propose 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.
Outcome:	The proposed system generates fully editable scientific illustrations from long-form scientific texts while enabling flexible style adaptation through user-provided reference images.

QFinZero: A Unified Financial Toolchain for LLM-Based Trading Agents (2026.acl-demo)

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Haochen Luo, Yifan LI, Ho Tin Ko, An Binh Minh, Junjie Xu, Tang Pok Hin, Wang Chak Wong, Gao Yuan, Zhengzhao Lai, Yuan Zhang, Chen Liu

dLLM: Simple Diffusion Language Modeling (2026.acl-demo)

Challenge:	Existing trading systems rely on fragmented and task-specific APIs, resulting in inconsistent schemas and limited reproducibility.
Approach:	They propose a unified trading environment for large language model (LLM) agents that standardizes three core capabilities . they argue that such a standardized trading environment is essential for scalable research on LLM-based financial agents.
Outcome:	The proposed trading environment reduces engineering overhead and supports reproducible evaluation through comprehensive logging and deterministic replay.

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Zhanhui Zhou, Lingjie Chen, Hanghang Tong, Dawn Song

Fast-MIA: Efficient and Scalable Membership Inference for LLMs (2026.acl-demo)

Challenge:	diffusion language models (DLMs) are evolving rapidly but many lack transparent implementations or are scattered across codebases.
Approach:	They propose an open-source framework that unifies diffusion language modeling components while remaining flexible enough to support new methods and architectures.
Outcome:	dLLM unifies the core components of diffusion language modeling and makes them easy to customize for new designs.

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Hiromu Takahashi, Shotaro Ishihara

CodeDistiller: Automatically Generating Code Libraries for Scientific Coding Agents (2026.acl-demo)

Challenge:	a library for evaluating membership inference attacks against large language models (LLMs) has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs.
Approach:	They propose a Python library for efficiently evaluating membership inference attacks against large language models (LLMs) they use a high-throughput batch inference via vLLM and a cross-method caching architecture that computes intermediate results once and shares them across methods.
Outcome:	The proposed library performs a 5 speedup in inference and a cross-method caching architecture that computes intermediate results once and shares them across methods.

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Peter Jansen, Samiah Hassan, Pragnya Narasimha

Programming Languages in NLP

Challenge:	Automated Scientific Discovery (ASD) systems rely on parametric knowledge to generate and run code-based experiments.
Approach:	They propose a system that distills large collections of scientific Github repositories into a vetted library of working domain-specific code examples.
Outcome:	The proposed system produces more accurate, complete, and scientifically sound experiments than an agent with only general materials-science code examples.

IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation (2026.acl-demo)

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Md Mofijul Islam, Md Sirajus Salekin, Joe King, Priyashree Roy, Vamsi Thilak Gudi, Spencer Romo, Akhil Nooney, Bob Strahan, Boyi Xie, Diego A. Socolinsky

Information Extraction & Text Mining

Challenge:	Large Language Models (LLMs) are inadequate for extracting structured insights from unstructured documents.
Approach:	They propose a framework enabling agentic AI for end-to-end document intelligence with four key components: DocSplit, configurable Extraction Module, and Rule Validation Module.
Outcome:	The proposed framework achieves 98% classification accuracy, 80% reduced processing latency, and 77% lower operational costs over legacy baselines.

AInterviewer: A Platform for Designing and Conducting AI-led Qualitative Interviews (2026.acl-demo)

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Tobias Gårdhus, Nikolas Vitsakis, Fie Lejre Frederiksen, Anna Rogers, Hjalmar Bang Carlsen

AutoForest: Automatically Generating Forest Plots from Biomedical Studies with End-to-End Evidence Extraction and Synthesis (2026.acl-demo)

Challenge:	Existing systems rely on proprietary LLMs, which compromise reproducibility and data security.
Approach:	They propose a platform that combines controlled question administration of survey software with the flexibility of LLMs.
Outcome:	AInterviewer combines controlled question administration of survey software with flexibility of LLMs.

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Massimiliano Pronesti, Angelo Miculescu, Mohsin Kapdi, Paul Flanagan, Oisín Redmond, Joao H Bettencourt-Silva, Gurdeep Singh Mannu, Spiros Denaxas, Rui Providencia, Anya Belz, Yufang Hou

Information Extraction & Text Mining

Challenge:	Existing systems that generate publication-ready forest plots from biomedical papers are fragmented and time-consuming.
Approach:	They propose a system that generates publication-ready forest plots directly from biomedical papers . autoforest automatically suggests ICO elements, extracts outcome data and performs statistical synthesis . authors demonstrate how the system can accelerate evidence synthesis and lower the barrier to conducting meta-analyses .
Outcome:	The proposed system accelerates evidence synthesis and lowers the barrier to meta-analyses.

Dash-M5H: An Interactive Dashboard for Multi-Modal, Multi-Model Mental Health Assessment (2026.acl-demo)

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Raymond Alavo, Xinyuan Zhang, Gemza Ademaj, Junhui Cai, Hyeokhyen Kwon, Robert Cotes, Gari D. Clifford, Ahmed Abbasi

MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models (2026.acl-demo)

Challenge:	Dash-M5H integrates transcript text, audio, and facial behavior with a clinically grounded VLM prediction pipeline that produces DSM-5-aligned depression predictions.
Approach:	They propose a dashboard that integrates multimodal behavioral data with multi-model signal outputs of recorded clinical interviews.
Outcome:	Dash-M5H is an interactive dashboard for *multi-modal, multi-model mental health assessment that integrates transcript text, audio, and facial behavior with a clinically grounded VLM prediction pipeline.

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Ahmad Chamma, Omar El Herraoui, Guokan Shang

Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Tasks (2026.acl-demo)

Challenge:	MixtureKit is a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned checkpoints.
Approach:	They propose a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned checkpoints.
Outcome:	Experiments on multilingual code-switched (Arabic–Latin) show that BTX models built with MixtureKit outperform dense baselines across multiple benchmarks.

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Yihan Wang, Peiyu Liu, Runyu Chen, Jiaxing Pu, Wei Xu

Programming Languages in NLP

Challenge:	Existing methods are designed for specific settings, each with its own set of challenges.
Approach:	They propose a unified, modular, and extensive Text-to-SQL framework . it proposes a universal execution paradigm and a multi-actor collaboration mechanism .
Outcome:	Squrve proposes a unified, modular, and extensive Text-to-SQL framework . the framework outperforms existing methods on widely adopted benchmarks .

RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)

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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang

ArcLight: A Lightweight LLM Inference Architecture for Many-Core CPUs (2026.acl-demo)

Challenge:	Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks.
Approach:	They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions.
Outcome:	The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks.

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Yuzhuang Xu, Xu Han, Yuxuan Li, Wanxiang Che

DialogGuard: Multi-Agent Psychosocial Safety Evaluation Interface of Sensitive LLM Responses (2026.acl-demo)

Challenge:	Existing frameworks for large language model (LLM) inference on CPUs overlook overhead of cross-NUMA memory access.
Approach:	They propose a lightweight LLM inference architecture designed from the ground up for many-core CPUs.
Outcome:	Experimental results show that ArcLight surpasses the performance ceiling of mainstream frameworks, achieving up to 46% higher inference throughput.

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Han Luo, Guy Laban

Dialogue Systems & Conversational Agents

Challenge:	Existing tools do not surface subtler psychosocial harms, nor provide explainable rationales that practitioners need.
Approach:	They propose an open-source system that lets practitioners inspect, stress-test, and create audit trails for prompted LLM agents across five psychosocial safety dimensions.
Outcome:	The open-source DialogGuard system lets practitioners inspect, stress-test, and create audit trails for prompted LLM agents across five psychosocial safety dimensions.

ClinQueryAgent: A Conversational Agent for Population Health Management (2026.acl-demo)

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Joseph Spartacus Boyle, Anthony Michael Dranfield, Mike O’Neil, Maria Liakata, Alison Q. Smithard

Natural Language Interfaces

Challenge:	In 2014 there were 2.3B SNOMED CT codes recorded in English healthcare practices. By 2024, this number had grown almost threefold to 6.1B codes: approximately 100 codes per person each year.
Approach:	They introduce a system for translating natural language population health questions into executable database queries using agents with access to both local and external knowledge bases.
Outcome:	The proposed system is able to handle a range of health informatics tasks on three datasets and via a beta-testing phase.

Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation (2026.acl-demo)

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

ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery (2026.acl-demo)

Challenge:	Rankify unifies retrieval-augmented generation (RAG) and retrieval based question answering systems.
Approach:	They propose an open-source Python toolkit that unifies retrieval-augmented generation in a single modular framework.
Outcome:	The proposed framework unifies retrieval-augmented generation (RAG) tools in a single modular framework.

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Shahar Levy, Eliya Habba, Reshef Mintz, Barak Raveh, Renana Keydar, Gabriel Stanovsky

GovScape: A Public Multimodal Search System for 70 Million Pages of Government PDFs (2026.acl-demo)

Challenge:	a new approach to natural-language research questions requires manual effort to generate an annotation schema and label the corpus.
Approach:	They propose a natural-language search tool that takes a question and a corpus to produce a schema and db with a web interface that lets steer and revise the extraction.
Outcome:	The proposed model yields outputs that support real-world analysis in law and computational biology.

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Ying-Hsiang Huang, Claire Gong, Shreya Shaji, Alison R Yan, Leslie Harka, Albert Du, Anjali Shubha Gopal, Samuel J Klein, Shannon Zejiang Shen, Mark E. Phillips, Trevor Owens, Kyle Deeds, Benjamin Charles Germain Lee

Bridging Kernel Drivers and Virtual Device Models with LLM-Powered Automation (2026.acl-demo)

Challenge:	Efforts over the past three decades have produced web archives containing billions of webpage snapshots and petabytes of data.
Approach:	They propose a public search system that supports multimodal searches across 10,015,993 federal government PDFs from the 2020 End of Term crawl.
Outcome:	The proposed system supports multimodal searches across 10,015,993 federal government PDFs from the 2020 End of Term crawl (70,958,487 total PDF pages) significant 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.

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Mingyu Wang, Bin Yu, Wenjian Lu, Zhi Wang, Gao Kefeng, Cheng Wen, Xu Lu, Cong Tian

BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation (2026.acl-demo)

Challenge:	Linux kernel device drivers are tightly coupled with hardware, making them difficult to execute and test without physical devices.
Approach:	They present a tool that generates QEMU-based virtual devices directly from Linux driver source code.
Outcome:	The proposed tool generates QEMU-based virtual devices directly from Linux driver source code.

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Yang Qi, Xiangyao Ma, Xiao Wang, Hao Wang, Rui Wang

Multilinguality

Challenge:	Existing document translation pipelines face a tension between linguistic processing and layout preservation.
Approach:	They propose a framework for layout-preserving PDF translation that decouples visual layout metadata from semantic content.
Outcome:	The proposed framework improves layout fidelity, visual aesthetics, and terminology consistency over representative baselines while maintaining competitive translation precision.

JointCoder: Exploring Automated ICD Coding on Real-World Chinese EHRs with a Multi-Agent Framework (2026.acl-demo)

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Kangjun Liu, Zhenyu Li, Jianlei Wang, Hongjiao Guan, Ying Lian, Guoqiang Chen, Tao Xin, Wenpeng Lu

Challenge:	Existing automated ICD coding systems face several fundamental challenges due to the limited availability of publicly available Chinese ICD datasets.
Approach:	They propose to use a Chinese ICD coding dataset and a multi-agent framework to reformulate ICD as a joint disease-procedure coding task.
Outcome:	The proposed system outperforms state-of-the-art methods on real-world Chinese ICD coding datasets and 1.7B-parameter models.

Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects (2026.acl-demo)

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Christopher Latimer, Nicolò Boschi, Andrew Neeser, Chris Bartholomew, Gaurav Srivastava, Xuan Wang, Naren Ramakrishnan

Structured Data in NLP

Challenge:	Hindsight organizes long-term memory into four logical networks and exposes three core operations.
Approach:	Hindsight organizes long-term memory into four logical networks and exposes three core operations.
Outcome:	Hindsight is a working memory system for AI agents that separates facts from beliefs . the system outperforms existing models on LongMemEval and LoCoMo with 83.6% accuracy .

Semantic XPath: Structured Agentic Memory Access for Conversational AI (2026.acl-demo)

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Yifan Simon Liu, Ruifan Wu, Liam Gallagher, Jiazhou Liang, Armin Toroghi, Scott Sanner

Natural Language Interfaces

Challenge:	Early ConvAI agents rely on an in-context approach that appends the growing conversation history to the model input, but this approach scales poorly under context-window limits.
Approach:	They propose a tree-structured memory module to access and update structured conversational memory.
Outcome:	The proposed system improves over flat-RAG baselines while using only 9.1% of the tokens required by in-context memory.

Graph of Trace: Visualizing Execution Traces of Scientific Agents (2026.acl-demo)

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Tianci Gao, Haoxuan Li, Jian He Li, Tianxiang Zhao, Shi Runze, Weiran Wang, Zezhao Wu, Lu Mi

ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models (2026.acl-demo)

Challenge:	Scientific AI agents can perform complex research tasks, but these unfolded workflows are difficult for humans to inspect and review, limiting interpretable, controllable and effective human–AI collaboration.
Approach:	They propose a monitoring and visualization framework that records fine-grained execution events and organizes them into a directed graph that makes agent workflows explicit as they proceed.
Outcome:	The proposed framework records intermediate steps (e.g. tool calls and code executions) and renders them as real-time updated visual traces that expose workflow structure.

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Jiasheng Zheng, Xin Zheng, Boxi Cao, Pengbo Wang, Zhengzhao Ma, Qiming Zhu, Jiazhen Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

DataArc-SynData-Toolkit: A Unified Closed-Loop Framework for Multi-Path, Multimodal, and Multilingual Data Synthesis (2026.acl-demo)

Challenge:	Existing code sandboxes fail to provide accurate verification and efficiency under high-concurrency workloads.
Approach:	They propose a high-fidelity code verification system that provides sandbox feedback for RL training and evaluation.
Outcome:	The proposed system outperforms heuristic-matching baselines on LiveCodeBench and training stability on high-concurrency workloads.

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Zhichao Shi, Cehao Yang, Hao Zhou, Xiaojun Wu, Huajie Li, Xuhui Jiang, Chengjin Xu, Yuanzhuo Wang, Jian Guo

HuoziIME: An On-Device LLM-Enhanced Input Method for Deep Personalization (2026.acl-demo)

Challenge:	Existing synthetic data tools are limited by convoluted workflows, fragmented data standards, and limited scalability across modalities.
Approach:	They develop an open-source framework that aims to reduce the technical barrier to synthetic data generation and subsequent model training.
Outcome:	The proposed framework achieves an optimal balance between generation efficiency and data quality.

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Baocai Shan, Yuzhuang Xu, Wanxiang Che

WIGVO: Real-Time Bidirectional Speech Translation over Legacy PSTN Calls via Dual-Session Echo Gating (2026.acl-demo)

Challenge:	Mobile input method editors (IMEs) are the primary interface for text input, yet they are constrained to manual typing and struggle to produce personalized text.
Approach:	They propose a personalized on-device IME powered by large language models . they endow HUOZIIME with initial human-like prediction ability .
Outcome:	The proposed IME has initial human-like prediction ability and is optimized for on-device deployment.

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Hyeong-seob Kim, Sang-Woo Son, Hyun-woo Cho, Hyeonsang Kim, Jinmo Kim

MASEval: Extending Multi-Agent Evaluation from Models to Systems (2026.acl-demo)

Challenge:	Existing systems assume wideband audio and client-side acoustic echo cancellation (AEC) . a system that enables bidirectional speech translation over the PSTN requires app installation or carrier integration.
Approach:	They propose a server-side relay system that enables bidirectional LLM-based speech translation over ordinary telephone calls without app installation or carrier integration.
Outcome:	The proposed system is deployed over the public switched telephone network (PSTN) it observes 555 ms median latency, 2,684 mS median translation loops, and COMET semantic adequacy of 0.71 (enko) and 0.62 (koen)

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Cornelius Emde, Alexander Rubinstein, Anmol Goel, Ahmed Heakl, Sangdoo Yun, Seong Joon Oh, Martin Gubri

Challenge:	MASEval provides a framework-agnostic, system-level comparison across any agent framework and benchmark.
Approach:	They propose a Python library that treats the entire agentic system as the unit of analysis.
Outcome:	The proposed framework treats the entire agentic system as the unit of analysis.

MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing (2026.acl-demo)

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Yang Liu, Jinxuan Cai, Yishen Li, Qi Meng, Zedi Liu, Xin Li, Chen Qian, Chuan Shi, Cheng Yang

Structured Data in NLP

Challenge:	Large language model-based multi-agent systems (MAS) are increasingly used to extend agentic problem solving via role specialization and collaboration.
Approach:	They propose a graph-centric framework for orchestrating large language model-based multi-agent systems . they compile a user's natural-language intent into an editable workflow specification and then into an executable graph .
Outcome:	The proposed framework compiles natural-language intent into an executable graph and then compile and executes it at runtime.

FactSearch: An Interactive Agentic Fact Search System for Verifying Large Language Model Outputs (2026.acl-demo)

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Meng Fang, Harry Mackenzie

Potato 2.0: A Comprehensive Annotation Platform with AI-in-the-Loop Support (2026.acl-demo)

Challenge:	Existing tool-augmented verification systems depend on opaque search APIs, introducing uncontrolled variability into factuality evaluation.
Approach:	They propose a reproducibility-oriented agentic fact search system for claim-level factuality verification built on a locally aggregated open-source search infrastructure.
Outcome:	The proposed system 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.

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David Jurgens, Michael Chen, Lina Iyer

Challenge:	Annotated data is still central to NLP and Generative AI, yet the demands on annotation have grown in both scale and complexity.
Approach:	They introduce Potato 2.0, an open source annotation platform for easy deployment and customization.
Outcome:	The new version of potato supports 39 different types of annotation tasks and multiple AI-assistance features.

mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models (2026.acl-demo)

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Jakub Muszyński, Paweł Pozorski, Maria Ganzha

PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers in Overleaf (2026.acl-demo)

Challenge:	mllm-shap is an open-source Python platform for researchers and ML practitioners that extends Shapley value (SV) explainability from text-only large languagemodels to multimodal LLMs that process both text and audio.
Approach:	They present a framework that extends Shapley value (SV) explainability from text-only large languagemodels to multimodal LLMs thatjointly process text and audio.
Outcome:	mllm-shap is the first publicly available framework for complete, reproducible SV-based explainability of text-audioMLLMs.

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Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner, Xuanqiang Angelo Huang, Vilém Zouhar, Dominik Glandorf, Isabel Dahlgren, Rishit Dagli, Yuen Chen, Felix Leeb, Van Q. Truong, Punya Syon Pandey, Yves Bicker, Suvajit Majumder, Wenyuan Jiang, Zeju Qiu, Sankalan Pal Chowdhury, Mrinmaya Sachan, Bernhard Schölkopf, Mona T. Diab, Zhijing Jin

Challenge:	Emerging AI-powered writing assistants 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.
Approach:	They propose a human-centered writing assistant system that delivers actionable suggestions as Overleaf-native inline comments while leaving the actual writing entirely to human authors.
Outcome:	The proposed system outperforms a baseline with the skill library and provides actionable suggestions while leaving the actual writing to human authors.

Gardener: An Agentic AI System for Single-Cell RNA Sequence Analysis (2026.acl-demo)

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Junhan Liu, Zhenke Liu, Yongcheng Shi, Peilin Yu, Minxing Zhang, Jiapeng Zhang

Challenge:	Existing large language models encode workflow progress as conversational state and rely on cloud-centric execution, which hinders traceability and auditability.
Approach:	Gardener is an open-source desktop application for macOS and windows under the Apache License 2.0.
Outcome:	Gardener is released as an open-source desktop application for macOS and Windows under the Apache License 2.0.

TokCollate: A Comprehensive Tool for Tokenizer Evaluation and Visualization across Languages (2026.acl-demo)

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Dušan Variš, Abishek Stephen, Jindřich Libovický

Syntactic Text Processing

Challenge:	Tokenization quality varies significantly across languages, leading to disparities in LLM performance and cost for speakers of less-resourced languages.
Approach:	They propose a Python-based evaluation framework and a JavaScript visualization interface that evaluates tokenizers in a variety of languages.
Outcome:	TokCollate is an evaluation framework for tokenizers with a JavaScript visualization interface.

A System for Dynamically Tracking Content Moderation on Reddit (2026.acl-demo)

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George Arthur Baker, Bharadwaj Kadiyala

AI Steerability 360: A Toolkit for Steering Large Language Models (2026.acl-demo)

Challenge:	Recent work in social media platforms delegate content moderation decisions to users and communities.
Approach:	They propose 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.
Outcome:	The proposed system is the only available solution for general-purpose, real-time, policy-compliant longitudinal data collection on Reddit.

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Erik Miehling, Karthikeyan Natesan Ramamurthy, Praveen Venkateswaran, Ching-Yun Ko, Pierre Dognin, Moninder Singh, Tejaswini Pedapati, Avinash Balakrishnan, Matthew Riemer, Dennis Wei, Inge Vejsbjerg, Elizabeth M. Daly, Kush R. Varshney

Media-to-Insights: A Multi-Agent AI System for Continuous Media Monitoring, Analysis, and Reporting (2026.acl-demo)

Challenge:	The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs.
Approach:	The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs.
Outcome:	The toolkit is available under an Apache 2.0 license and is available on https://github.com/IBM/AISteer360.

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

Information Extraction & Text Mining

Challenge:	Existing systems that use keyword-based media monitoring miss semantically relevant articles and are expensive at scale.
Approach:	They propose a multi-agent media monitoring system that processes streaming articles through three stages: article matching, batched feature extraction, and report generation with deterministic deduplication and density-based clustering.
Outcome:	The proposed system reduces agent invocations by 20% and reduces core feature extraction calls from 7 to 2 per article - a 71% reduction - with bounded quality tradeoffs .

LinkNav: Surfacing Interconnected Information in Scientific Articles (2026.acl-demo)

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Sebastian Antony Joseph, Jennifer Healey, Junyi Jessy Li, Ani Nenkova

Challenge:	a non-linear reading order of academic literature is recognized by authors who make explicit connections between non-adjacent passages.
Approach:	They propose an enhanced reading experience which generates questions and searches for answer-bearing passages in academic papers to form intra-document connections when answers are found.
Outcome:	The proposed interface makes connections between related but non-adjacent passages even if the author did not make them explicit.

FourCorners: Grounded Thai Legal Research over a Temporal Knowledge Graph (2026.acl-demo)

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Pawitsapak Akarajaradwong, Thitiwat Nopparatbundit, Treephop Saeteng, Kasidit Phoncharoen, Sarana Nutanong, Chompakorn Chaksangchaichot

Structured Data in NLP

Challenge:	a new platform addresses five pain points in legal research in Thailand . the tools available to legal practitioners are fragmented and lack a unified tool for cross-referencing, version tracking or structural navigation.
Approach:	They propose a platform that addresses five practitioner pain points through three modules built on a temporal legal knowledge graph covering 552K nodes and 6.3M edges.
Outcome:	The proposed platform addresses five practitioner pain points through three modules built on a temporal legal knowledge graph covering 552K nodes and 6.3M edges.

rosaOS: Agentic Operating System for Embodied LLMs (2026.acl-demo)

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Yijun Ge, Kushaldeep Mujral, Karthik Nambiar, Jimmy Lin

Natural Language Interfaces

Challenge:	Existing LLM–robotic systems are tightly intertwined, making it difficult to switch hardware, add extra capabilities, or expand to multiple devices without bespoke integration.
Approach:	They propose an open-source agentic operating system for embodied LLMs . rosaOS integrates agentic tool-calling and ROS for robot interactions .
Outcome:	The proposed system integrates with the Reachy Mini robot and supports a multi-device setup with a quadruped robot, wheeled mobile robot, and smart lamp.

CritiSense: Critical Digital Literacy and Resilience Against Misinformation (2026.acl-demo)

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Firoj Alam, Fatema Ahmad, Ali Ezzat Shahroor, Mohamed Bayan Kmainasi, Elisa Sartori, Giovanni Da San Martino, Abul Hasnat, Raian Ali

MED-COPILOT: A Medical Assistant Powered by GraphRAG and Similar Patient Case Retrieval (2026.acl-demo)

Challenge:	a recent study found that social media misinformation is reactive and claim-specific, and can degrade under temporal and cross-lingual/domain shift.
Approach:	They present a mobile media-literacy app that builds digital literacy skills through short, interactive challenges with instant feedback.
Outcome:	The app is the first multilingual and modular platform to improve digital literacy skills.

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

PathBuilder: A Quality-Controlled LLM System for Personalized Learning Pathways (2026.acl-demo)

Challenge:	MED-COPILOT is an interactive research prototype for evidence-aware clinical reasoning . large language models (LLMs) are prone to hallucinations and lack verifiable evidence grounding .
Approach:	They propose a system that integrates GraphRAG and semantic-keyword similar-patient retrieval to support transparent clinical reasoning.
Outcome:	The proposed system outperforms baseline and standard RAGs on clinical note completion and medical question answering.

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Jasper Meynard Arana, John Andrew Mañacop, John Allen Manacop, Roy Andrew Garcia, Keith Rick Piniera, Kristine Ann M. Carandang, Ethan Robert Casin, Christian Alis, Christopher Monterola

AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge (2026.acl-demo)

Challenge:	Large language models (LLMs) enable scalable content generation for personalized learning, but reliability and pedagogical alignment remain open challenges.
Approach:	They propose 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.
Outcome:	The proposed system achieves a gain of 37.9 percentage points and a large effect size in a real-world deployment with 179 registered users.

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Karen Zhou, Chenhao Tan

Challenge:	AutoChecklist is an open-source library that unifies checklist-based evaluation into composable pipelines.
Approach:	They propose an open-source library that unifies checklist-based evaluation into composable pipelines.
Outcome:	The open-source library unifies checklist-based evaluation into composable pipelines.

JobMatchAI - An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI (2026.acl-demo)

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Mayank Vyas, Abhijit Chakraborty, Vivek Gupta

Qayyem: A Real-time Platform for Scoring Proficiency of Arabic Essays (2026.acl-demo)

Challenge:	Recruiters and job seekers rely on search systems to navigate labor markets . many systems fail to handle skill synonyms and nonlinear careers .
Approach:	They propose a production-ready system that integrates Transformer embeddings, skill knowledge graphs, and interpretable reranking.
Outcome:	The proposed system optimizes utility across skill fit, experience, location, salary, and company preferences.

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

Text Complexity

Challenge:	Existing Arabic writing technologies primarily use a single quality score for essays, but there is limited support for Arabic AES.
Approach:	They propose a Web-based platform that integrates Arabic AES workflows with a user-friendly interface.
Outcome:	The proposed system integrates with existing Arabic scoring systems and provides a user-friendly interface.

PhyVer: Physics-Grounded Material Claim Verification with Multi-Fidelity Physical Evidence (2026.acl-demo)

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Jianpeng Chen, Wangzhi Zhan, Haohui Wang, Brian Mayer, Dongqi Fu, Dawei Zhou

pAtChWoRK: Patching the Pieces of Public Procurement Documents (2026.acl-demo)

Challenge:	Existing claim verification pipelines operate over text, producing ungrounded judgments.
Approach:	They propose a physics-grounded material claim verification system that can be used to verify claims with physical evidence.
Outcome:	The proposed system reduces MAE and sign-offs with experts over text-only GPT-5.1.

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Lorena Calvo-Bartolomé, Saúl Blanco Fortes, Erick Cedeño, Jerónimo Arenas-García

Challenge:	pAtChWoRK corrects manual classification errors and extracts complex unstructured fields such as award and solvency criteria and tenders’ objectives.
Approach:	pAtChWoRK corrects manual classification errors and extracts complex unstructured fields such as award and solvency criteria and tenders’ objectives.
Outcome:	pAtChWoRK corrects manual classification errors and extracts complex unstructured fields such as award and solvency criteria and tenders’ objectives.

UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models (2026.acl-demo)

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Qundong Shi, Jie Zhou, Biyuan Lin, Junbo Cui, Guoyang Zeng, Yixuan Zhou, Ziyang Wang, Xin Liu, Zhen Luo, Yudong Wang, Zhiyuan Liu

Paper2Web: Let’s Make Your Paper Alive! (2026.acl-demo)

Challenge:	Existing evaluation frameworks for audio foundation models are heavily reliant on English, making it difficult to objectively assess models’ performance on Chinese.
Approach:	They propose a unified framework that supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.
Outcome:	The proposed framework supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards.

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Yuhang Chen, Tianpeng Lv, Yao Wan, Philip S. Yu, Dongping Chen

Challenge:	Current approaches to creating layout-aware, interactive academic webpages are limited.
Approach:	They propose a benchmark dataset and evaluation framework for assessing academic webpage generation that incorporates rule-based metrics like Connectivity, Completeness and PaperQuiz.
Outcome:	The proposed framework outperforms baselines like template-based webpages and arXiv/alphaXivo versions while maintaining low cost.

OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction (2026.acl-demo)

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Junxing Hu, Tianlong Li, Lei Yu, Ai Han

Challenge:	Existing MAS frameworks lack standardized abstractions, leading to low efficiency and repetitive implementation of core functions.
Approach:	They propose an open-source framework that encapsulates agents, tools, and reasoning flows as pluggable atomic components.
Outcome:	The OxyGent framework provides a robust and scalable foundation for multi-agent systems in industrial environments.

EduCoder: An Open-Source Annotation System for Education Transcript Data (2026.acl-demo)

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Saad Ashraf, Jim Malamut, Vishal Kumar, Guanzhong Pan, HyunJi Nam, Mei Tan, Lucía Langlois, Liliana Carolina Santos-Deonizio, Helen Spencer Higgins, Dorottya Demszky

Challenge:	Existing annotation tools do not support team-based workflows or access to instructional context .
Approach:	They present an open-source web platform for annotating classroom conversation transcripts . they argue that existing annotation tools do not provide access to instructional context .
Outcome:	EduCoder is an open-source web platform designed for annotating classroom conversation transcripts.

CPTCoder: A Reliable LLM System for Medical Procedure Code Prediction (2026.acl-demo)

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Benlu Wang, Ziyao Shangguan, Kyle Tegtmeyer, Zhenyu Zhang, Sophie Chheang, Arman Cohan

Challenge:	Clinical procedure coding is an extreme multi-label classification problem . CPTCoder predicts standardized medical procedure codes from clinical text .
Approach:	a new human-in-the-loop system predicts standardized medical procedure codes from clinical text.
Outcome:	CPTCoder outperforms baseline system by 12 and 5 points in a clinical procedure classification problem.

ParseJargon: Personalized Real-time Jargon Support in Online Meetings (2026.acl-demo)

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Yifan Song, Wing Yee Au, Hon Yung Wong, Brian Bailey, Tal August

Challenge:	Recent advances in speech-to-text technologies and large language models (LLMs) have the potential to overcome these limitations with automated, real-time jargon support.
Approach:	They built an interactive LLM-powered system that provides real-time personalized jargon support tailored to users’ individual backgrounds in online meetings.
Outcome:	The proposed system provides more precise jargon identification and enhanced participants’ comprehension, engagement, and appreciation of colleagues’ work.

The olmOCR Project: Building Fully Open OCR using VLMs (2026.acl-demo)

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Jake Poznanski, Kyle Lo, Luca Soldaini

AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists (2026.acl-demo)

Challenge:	olmOCR is a fully open OCR system developed through iterative public releases and community feedback.
Approach:	They propose an open OCR system that combines a 7B vision-language model trained in two stages: finetuning and reinforcement learning with visual unit tests.
Outcome:	The proposed system achieves state-of-the-art performance among open systems and proprietary APIs at a fraction of the cost.

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Junshu Pan, Panzhong Lu, Yixuan Weng, QiYao Sun, Fang Guo, Zijie Yang, Qiji Zhou, Yue Zhang

Challenge:	Recent advances in artificial intelligence (AI) have accelerated the growth of both human-authored and AI-generated research outputs.
Approach:	They propose an AI-driven open-access platform built on open preprints, AI-augmented analysis and review, and reader feedback.
Outcome:	The proposed platform supports human scientists through an interactive UI and AI scientists through Model Context Protocol (MCP)-based interactions.

TruthSplit: Revealing Conditional Validity in Arguments Through Multi-Worldview Comparative Reasoning (2026.acl-demo)

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Benjamin Stieger, Maximilian Terberger, Thomas Huber, Christina Niklaus

TONY: an open-source TOolkit for Nlp in psYchology (2026.acl-demo)

Challenge:	Existing argumentation tools analyze properties of the argument, but leave perspective-specific background knowledge implicit.
Approach:	They propose a system that analyzes arguments from multiple perspectives using a perspective-dependent analysis approach.
Outcome:	The proposed system analyzes arguments from multiple perspectives and generates interpretations, value conflicts and assumption gaps.

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Federico Ravenda, Sofia Irene Ravenda, Volodymyr Karpenko, Daniele Montagnani, Andrea Raballo, Antonietta Mira

Challenge:	Existing NLP tools are fragmented, closed-source, or difficult to use . a single sentence can convey emotion, social dynamics, cognitive states, and implicit attitudes .
Approach:	They propose an open-source python TOolkit for NLP in clinical psychology.
Outcome:	The TOolkit bridges traditional psycholinguistic analysis and modern NLP . it integrates interpretable lexical features with state-of-the-art lightweight transformer models . the toolkit is released under an open-source license and is evaluated through multiple MH–related datasets.

Formally Specifying the Intended Behavior of the Program: LLM-Driven Neuro-Symbolic Program Specification Synthesis (2026.acl-demo)

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Cheng Wen, Hu Junjie, YiKun Hu, Jie Su, Bin Yu, Dugang Liu, Zhiwu Xu, Weidi Sun, Shengchao Qin, Cong Tian

AnnoHID: LLM-Assisted Annotation Framework for Low-Resource Medical Texts (2026.acl-demo)

Challenge:	Formal verification typically requires developers to write detailed formal specifications . a formal verification system that generates candidate specifications is costly and error-prone .
Approach:	They propose an LLM-driven neuro-symbolic demonstration system that reframes specification writing as constrained structured synthesis.
Outcome:	The proposed system reduces hallucinations and produces proof-ready annotations.

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Annisa Maulida Ningtyas, Guntur Budi Herwanto, Yunita Sari, Rifki Afina Putri, Filip Kovacevic, Alaa El-Ebshihy, Varvara Arzt, Florina Piroi

RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions (2026.acl-demo)

Challenge:	Social media platforms are a popular way to communicate with medical experts and improve health literacy.
Approach:	They introduce a semi-automated annotation framework for medical texts in low-resource languages . they use large language models for pre-annotation and human validation to support efficient annotation .
Outcome:	The proposed framework is applied to medical social media texts in Bahasa Indonesia . it yields higher inter-annotator agreement and human review improves output . future work focuses on mitigating pre-annotation bias and reducing annotation overhead .

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Keyu He, Qianou Ma, Valerie Chen, Wayne Chi, Tongshuang Wu

A Dynamic Self-Evolving Extraction System (2026.acl-demo)

Challenge:	Deployed in a university software engineering course, RECAP captured 2,034 prompts and 8,239 code edits from 41 students across a multi-week project.
Approach:	They propose an open-source platform that passively records AI chat sessions and fine-grained code edits inside VS Code without disrupting the developer’s workflow.
Outcome:	The open-source platform captures 2,034 prompts and 8,239 code edits from 41 students across a multi-week project.

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Moin Aminnaseri, Hannah Kim, Estevam Hruschka

ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning (2026.acl-demo)

Challenge:	High-quality information extractions often require domain-specific accuracy, up-to-date understanding of specialized taxonomies, and the ability to incorporate emerging jargon and rare outliers.
Approach:	They propose a Dynamic Self-Evolving Extraction and Curation Toolkit which continuously improves as it is used to extract structured information from raw text.
Outcome:	The proposed toolkit continuously improves as it is used in medical, legal, and HR domains.

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Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, Minh Ngoc Ta, Ekaterina Fadeeva, Artem Vazhentsev, Daria Galimzianova, Nikolai Rozanov, Viktor Mazanov, Jingwei Ni, Tianyi Wu, Igor Kiselev, Mrinmaya Sachan, Iryna Gurevych, Preslav Nakov, Timothy Baldwin, Artem Shelmanov

FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)

Challenge:	Existing TTC scaling strategies and reasoning scorers are fragmented and evaluated under inconsistent protocols.
Approach:	They propose a framework for seamless test-time compute scaling of large language model reasoning . they use a modular Python library to implement state-of-the-art scaling strategy and scorer families .
Outcome:	The proposed framework evaluates performance and computational efficiency on mathematical and coding tasks.

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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie

Challenge:	FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions .
Approach:	They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures .
Outcome:	The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes.

Expert Calibration Lens for Pruning Mixture of Experts (2026.acl-demo)

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Luis Frentzen Salim, Chia-Chun Wu, Tran Van Nhiem, Lun-Wei Ku, Yung-Hui Li

Challenge:	Expert pruning is a practical deployment technique for Mixture-of-Experts models . but its success depends heavily on the calibration set used for pruning .
Approach:	They propose a calibration tool that compares expert activations across datasets to predict calibration perturbations without running expensive pruning procedures.
Outcome:	The proposed system compares expert activations across datasets to predict calibration perturbations without running expensive pruning procedures.

Ryze: Evidence-Enriched Data Synthesis from Biomedical Papers (2026.acl-demo)

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Yeqi Huang, Yue Chen, Yanwei Ye, Guanhao Su, Luo Mai

Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents (2026.acl-demo)

Challenge:	Existing post-training pipelines that generate QA pairs require costly expert annotation and synthetic data that drops evidence structure.
Approach:	They propose a system that converts raw biomedical papers into evidence-enriched training sets and a domain-specialized VLM.
Outcome:	Ryze synthesizes QA pairs with complete supporting evidence, reduces layout and OCR errors . the system outperforms the base model on LAB-Bench and surpasses GPT-5.2 by +3.8%.

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Asaf Yehudai, Lilach Eden, Michal Shmueli-Scheuer

Challenge:	Agentic systems are becoming more capable of defining strategies, taking actions, and solving complex, multi-step tasks.
Approach:	They propose an automatic, dynamic, and easy-to-use evaluation framework that provides textual insights into agent behavior on three levels of granularity: system, trace, and node.
Outcome:	The proposed framework produces high-quality, data-driven, insightful feedback on system, trace, and node.

FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use (2026.acl-demo)

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Suparno Roy Chowdhury, Tejas Anvekar, Manan Roy Choudhury, Muhammad Ali Khan, Kaneez Zahra Rubab Khakwani, M Bassam Sonbol, Irbaz Bin Riaz, Vivek Gupta

Programming Languages in NLP

Challenge:	Clinical trial databases are central to modern oncology research and drug development.
Approach:	FD-NL2SQL is a schema-aware clinical NL2sql assistant for SQLite-based oncology databases . it decomposes a natural-language question into predicate-level sub-questions and synthesizes executable SQL .
Outcome:	FD-NL2SQL synthesizes SQL based on decomposition, retrieved exemplars, and schema . clinical trial databases are central to modern oncology research and drug development .

DIAGRAMS : A Review Framework for Reasoning-Level Attribution in Diagram QA (2026.acl-demo)

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Anirudh Iyengar Kaniyar Narayana Iyengar, Tampu Ravi Kumar, Manan Suri, Raviteja Bommireddy, Dinesh Manocha, Puneet Mathur, Vivek Gupta

Question Answering

Challenge:	Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question-answer pair to all visual regions needed to derive the answer.
Approach:	They propose a diagram question-answer review framework that decouples interface logic from dataset-specific JSON structures through an internal meta-schema and dataset adapters.
Outcome:	The proposed framework achieves 85.39% precision and 75.30% recall across six diagram QA datasets.

SlideGuard: AI-Driven Evaluation of Graduate Student Presentation Materials (2026.acl-demo)

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Nikolay Alekseevich Butakov, Maria Khodorchenko, Mazein Nikita, Daniil Gareev, Yuri Falevskiy, Georgii Konev, Denis Nasonov

Spectra: A Mechanistic Interpretability Library for Vision-Language Models (2026.acl-demo)

Challenge:	Effective communication is a core objective of graduate education in AI and machine learning (ML).
Approach:	They propose an evaluation agent that assesses slide decks against a framework of expert-defined criteria using a visual language model.
Outcome:	The evaluation agent detects the majority of expert-identified issues on a dataset of 150 annotated slide decks and shows strong results on structural and visual criteria and known limitations on subjective dimensions such as research quality.

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Clement Neo, Yongsen Zheng, Kwok-Yan Lam, Luke Ong

Explainability & Interpretability in NLP

Challenge:	Existing interpretability tools for visionlanguage models are limited to activation probing and saving.
Approach:	They propose a library specifically designed for mechanistic interpretability of visionlanguage models that provides unified abstractions for activation patching, attention pattern analysis, and meta-functions across diverse VLM architectures.
Outcome:	The proposed library handles architecture-specific complexities while maintaining a simple, high-level interface.

Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent Collaboration (2026.acl-demo)

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Yinghao Tang, Yupeng Xie, Yingchaojie Feng, Tingfeng Lan, Wei Chen

Challenge:	Recent LLM-based agents can automate content creation, but naively applying them yields uncontrollable and unverifiable outputs.
Approach:	They propose a human-agent collaborative system that generates interactive educational documents from a single topic input.
Outcome:	The proposed system generates documents comparable in quality to human-authored ones.

Praat++: Multimedia Annotation System for Speech and Vocalization (2026.acl-demo)

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Weiran Zhang, Kenny Q. Zhu

AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse (2026.acl-demo)

Challenge:	High-quality time-aligned annotation is fundamental to speech processing and animal vocalization research, yet precise boundary localization and consistent labeling remain challenging in collaborative settings.
Approach:	They propose a web-based multimedia annotation system for collaborative, video-informed, and AI-assisted timeline labeling of audio and video data.
Outcome:	The proposed system improves time-aligned labeling and accuracy in speech and animal vocalization annotations.

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Zhang Zhang, Shuqi Lu, Hongjin Qian, Di He, Zheng Liu

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance (2026.acl-demo)

Challenge:	Existing frameworks for building LLM-based agents treat agent behavior as static-knowledge gained during execution is not preserved for future use.
Approach:	They propose a new paradigm that preserves successful task solutions as executable subagent code rather than textual experience.
Outcome:	The proposed agent-based agent-driven paradigm preserves successful tasks as executable subagent code rather than textual experience.

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Mengzhang Li, Yuan Yao