Active Information Gathering

Follow this topic Ask about this topic

The field studies how an agent can decide what information to seek next, and when to stop asking and act or defer. Common approaches include Bayesian experimental design using expected information gain or value of information, active-learning acquisition rules, and reinforcement learning policies trained to ask useful questions. These methods are tested in settings such as sequential clinical diagnosis, conversational tasks like 20 Questions, and agents that probe digital or physical environments before acting. A central challenge is that information gain does not always translate into better or safer decisions, so evaluations increasingly need to measure acquisition costs, interaction quality, and outcomes under incomplete evidence.

Latest papers

26 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Stop or Clarify? Experience-Conditioned Active Fact Verification
Oct 26, 2026Yuhang Dong, Jinghong Li, Xinyue Wang +1Automated Fact-Checking Active Information Gathering
Nanjing University of Information Science and Technology, Nanjing, China
Better Reasoning, Not Just Bigger Models: A Structure-Grounded Framework for Efficient and Reliable Inference
Oct 22, 2026Amirhossein Aghaei, Liangyan Li, Shiyi Yang +2LLM Reasoning with Graphs LLM Grounding
Huawei Research Canada, Markham, Canada · Huawei Research Canada, Waterloo, Canada · Huawei Technologies Co., Ltd., Beijing, China +1
ActiveMedAgent: Cost-Aware Trajectory Learning for Multimodal Medical Diagnosis
Oct 8, 2026Weiwei Ma, Xiaobing Yu, Peijie Qiu +5Medical Diagnosis Active Information Gathering
Washington University in St. Louis · Yale University · Icahn School of Medicine at Mount Sinai +1
Decide, Ask, or Defer: Clinical LLMs under Incomplete Evidence
Oct 3, 2026Mingzhan Yang, Weili WuClinical Decision-Making Clinical Language Model Evaluation
Department of Computer Science University of Texas at Dallas Richardson, TX 75080
LAVOIR: Teaching a Single-Pass Decision Encoder When and What to Ask with Amortized Value of Information
Sep 25, 2026Furkan Yilmaz, Habibe Aleyna Tasdemir, Muhammed Faruk GozayLLM Decision-Making Active Information Gathering
Revelation Control
Aug 24, 2026Qinyou WangActive Information Gathering Cost-Aware Inference
Guixu: Valuation-Driven Data Discovery for Autonomous AI Agents with On-Chain Attestation
Aug 8, 2026Yifan Wu, Yuchen Peng, Jiaqi Chai +4Active Information Gathering Data Valuation
Zhejiang University · The State Key Laboratory of Blockchain and Data Security; Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
Beyond Solution-Centric Search: Adaptive Inquiry and Knowledge Revision for Autonomous ML Engineering
Aug 3, 2026Shaokang Fu, Yulong Tao, Linbo Jin +7Active Information Gathering Automated Machine Learning
Alibaba Group · East China Normal University · Zhejiang University +2
Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support
Jul 29, 2026Shayndhan Sivanathan, Shravan Nageswaran, Mehdi Zadem +10Clinical Triage Clinical Decision Support
Atman Labs · Oxford University Hospitals · Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford +11
Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs
Jul 3, 2026Jakob Hartmann, James Harvey, Jhonathan Navott +5RL for Language Models Amortized Inference
University of Oxford, Oxford, United Kingdom · 2Ellison Institute of Technology, Oxford, United Kingdom
Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models
Jul 3, 2026Shengyi Hua, Kangzhe Hu, Conghui He +2Medical Diagnosis RL for Language Model Reasoning
Qing Yuan Research Institute, Shanghai Jiao Tong University · Shanghai AI Laboratoty · Shanghai Innovation Institute
Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing
Jun 24, 2026Liwei Yu, Shuo Li, Ming Zhou +2LLM Agent Evaluation AI Agent Security Benchmarks
University of Science and Technology of China, Hefei, China · Nanjing University of Science and Technology, Nanjing, China · Research Institute, Runjian Co., Ltd, China +1
Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce
Jun 23, 2026Filippos Ventirozos, Matthew ShardlowActive Information Gathering E-Commerce
Manchester Metropolitan University Manchester, United Kingdom
ACCORD: Action-Conditioned Contextual Grounding for Language Agents
Jun 15, 2026Lai Jiang, Cheng Qian, Zhenhailong Wang +3LLM Grounding Tool-Augmented Language Model Agents
University of Illinois Urbana-Champaign · Stanford University
Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation
Jun 5, 2026Yunhan Wang, Yuda Wang, Zhiying Tu +5Medical Diagnosis Clinical Decision Support
Harbin Institute of Technology, Weihai, Weihai, Shandong, China · Shandong Key Laboratory of Digital Service Computing Technology and Systems, China · Weihai Municipal Hospital, Weihai, Shandong, China +3
Contrastive Learning and Correlation Clustering for Sequences of Network Telescope Data
Jun 3, 2026Jannik Presberger, Alexander Männel, Maynard Koch +3Contrastive Learning Unsupervised Clustering
1TU Dresden · 2HAW Hamburg · Center for Scalable Data Analytics and AI Dresden/Leipzig
ClinEnv: An Interactive Multi-Stage Long Horizon EHR Environment for Agents
Jun 1, 2026Yuxing Lu, Yushuhong Lin, Wenqi Shi +4Long-Horizon Agent Evaluation Clinical Decision-Making
1Georgia Institute of Technology · 2Peking University · University of Texas Southwestern Medical Center +1
CA-BED: Conversation-Aware Bayesian Experimental Design
May 31, 2026Daniel Arnould, Rashad Aziz, Zixuan Kang +5Active Information Gathering Bayesian Experimental Design
BALAR : A Bayesian Agentic Loop for Active Reasoning
May 6, 2026Aymen Echarghaoui, Dongxia Wu, Emily B. FoxClarification Question Generation Bayesian Inference
Department of Statistics, Stanford University · Department of Computer Science, Stanford University
Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation
Apr 13, 2026Chen Huang, Zitan Jiang, Changyi Zou +2Active Information Gathering Task-Oriented Dialogue Systems
Institute of Data Science, National University of Singapore · College of Computer Science, Sichuan University · Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China
Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis
Apr 6, 2026Xuyang Shen, Haoran Liu, Dongjin Song +1Medical Diagnosis Clinical Decision-Making
University of Connecticut · Texas A&M University · NEC Laboratories America
Multi-Robot Coordination for Planning under Context Uncertainty
Mar 14, 2026Pulkit Rustagi, Kyle Hollins Wray, Sandhya SaisubramanianMulti-Robot Motion Planning Multi-Robot Coordination
Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, Corvallis, OR 97331, USA · Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents
Mar 12, 2026Deyu Zou, Yongqiang Chen, Fan Feng +4RL for LLM Agents Active Information Gathering
The Chinese University of Hong Kong · University of California San Diego · Georgia Institute of Technology +1
InfoPO: Information-Driven Policy Optimization for User-Centric Agents
Feb 28, 2026Fanqi Kong, Jiayi Zhang, Mingyi Deng +3LLM Agent Training RL for LLM Agents
Peking University · DeepWisdom · The Hong Kong University of Science and Technology (Guangzhou) +1
Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization
Feb 23, 2026Weichi Yao, Bianca Dumitrascu, Bryan R. Goldsmith +1Active Information Gathering Active Learning
University of Michigan · Columbia University
ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs
Aug 19, 2025Hongxin Ding, Baixiang Huang, Yue Fang +8RL for Language Models Healthcare
National Engineering Research Center of Software Engineering, Peking University, China · School of Computing and Data Science, The University of Hong Kong · School of Computer Science, Peking University, Beijing, China +2

Oct 26, 2026J/K move · Enter open · S save

Stop or Clarify? Experience-Conditioned Active Fact Verification

Yuhang Dong, Jinghong Li, Xinyue Wang, Yuanning Cui

Nanjing University of Information Science and Technology, Nanjing, China

Automated Fact-Checking Active Information Gathering Evidence Retrieval

Open paper AskPDF arXiv

Fact-verification benchmarks score the final label, while an interactive verifier must first decide whether the available evidence is sufficient. We focus on that intermediate Stop-or-Clarify Decision. Uncertainty alone is insufficient, because the benefit of another evidence-acquisition step is unknown before the step is taken. Sequential Experience-Adaptive Clarification (SEAC) wraps a fixed verifier with a lightweight evidence-acquisition policy. For each claim, SEAC retrieves evidence, runs the verifier, forms a label-free decision state, and combines stop risk with an observational clarification-utility estimate from earlier executed trajectories. Clarification is requested only when this score exceeds the action cost and decision threshold. On SciFact, LIAR, and FEVER, SEAC improves mean accuracy and macro-F1 over the compared fact-verification and active-retrieval baselines. Equal-budget and formula-level diagnostics support selective clarification as a contributor to the gain, while the small SciFact split leaves its paired improvement inconclusive.