Active Information Gathering

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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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
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
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
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 8, 2026, cs.LGJ/K move · Enter open · S save

ActiveMedAgent: Cost-Aware Trajectory Learning for Multimodal Medical Diagnosis

Weiwei Ma, Xiaobing Yu, Peijie Qiu, Jin Yang+4

Washington University in St. Louis · Yale University · Icahn School of Medicine at Mount Sinai+1

Medical Diagnosis Active Information Gathering Multimodal Agents Medical VLMs

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Clinical diagnosis is inherently sequential: clinicians escalate from cheap to costly tests only when additional evidence is expected to resolve diagnostic uncertainty. We present ActiveMedAgent, a framework that brings this cost-aware sequential logic to multimodal medical AI. Given a frozen, API-accessed vision-language model, ActiveMedAgent tracks probability distributions over candidate diagnoses and scores each acquisition by its per-step diagnostic utility minus cost. A lightweight MLP controller is then trained offline on these scored trajectories, learning when to request additional evidence and when to commit. Across three commonly used benchmarks, trajectory-based policy learning consistently outperforms both unguided acquisition and full-modality baselines. Notably, we identify an information overload effect. In 175 cases, the agent produces a correct diagnosis with fewer channels while the full-modality baseline fails, showing that learning what to omit can be as important as learning what to acquire.

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