Causal RL

RL: Reinforcement Learning

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

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From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learning
Sep 30, 2026Jialu Pi, Yanan Ma, Weijie Chen +8Clinical Outcome Prediction Multimodal Reasoning
Dept. of Radiology, Mayo Clinic, Phoenix, AZ, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA · Dept. of Radiology, Mayo Clinic, Phoenix, USA +2
Temporal-Causal Inference for Reinforcement Learning via Automata Learning
Sep 7, 2026Jan Corazza, Daniil Kaminskyi, Simon Lutz +4Reinforcement Learning Causal RL
Research Center Trustworthy Data Science and Security, TU Dortmund University, Dortmund, 44227, Germany. · School for Engineering of Matter, Transport, and Energy at Arizona State University, Tempe, Az 85281, USA.
IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning
Aug 11, 2026Zefeng Liang, Jie Qiao, Ruichu Cai +2Reinforcement Learning Robust RL
School of Computer Science, Guangdong University of Technology, Guangzhou, China · College of Science, Shantou University, Shantou, China
Learning Implicit Causal World Models from Multi-Agent Demonstrations
Jul 28, 2026Jasorsi GhoshCausal Representation Learning Causal World Models
Department of Computer Science Purdue University West Lafayette, Indiana, USA
CRRL: A Causality-Based Reinforcement Learning Framework for Autonomous System Recovery
Jul 3, 2026Safia Fatima, Kai Olav Ellefsen, Leon MoonenReinforcement Learning Robot Failure Recovery
Simula Research Laboratory, Norway · University of Oslo, Norway
An Introduction to Causal Reinforcement Learning
Jun 23, 2026Elias Bareinboim, Junzhe Zhang, Sanghack LeeReinforcement Learning Causal Inference
Department of Computer Science Columbia University New York, USA · Graduate School of Data Science Seoul National University Seoul, Republic of Korea
Mind Dreamer: Untethering Imagination via Active Causal Intervention on Latent Manifolds
May 15, 2026Shaojun Xu, Xiaoling Zhou, Yihan Lin +4Reinforcement Learning Credit Assignment in RL
Center for Brain-Inspired Computing Research, Department of Precision Instrument, Tsinghua University, Beijing, China · College of Computer Science and Technology, Zhejiang University, Hangzhou, China · Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, China +1
Delay-Empowered Causal Hierarchical Reinforcement Learning
May 12, 2026Chenran Zhao, Dianxi Shi, Haotian Wang +4Reinforcement Learning Hierarchical RL
College of Computer Science and Technology, National University of Defense Technology, Changsha, China · Intelligent Game and Decision Lab (IGDL), Beijing, China · School of Artificial Intelligence, Hebei University of Technology, Tianjin, China +1
Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure
May 8, 2026Jiamin Xu, Jacqueline Maasch, Kyra GanMarkov Decision Processes Causal Representation Learning
Cornell Tech, New York, NY
Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark
May 7, 2026Cristiano da Costa Cunha, Ajmal Mian, Tim French +1RL Benchmarks Structural Causal Models
Department of Computer Science and Software Engineering, University of Western Australia, 6009 WA Australia
MAGIC: Multi-Step Advantage-Gated Causal Influence for Multi-agent Reinforcement Learning
May 3, 2026Haohan Yu, Jinmiao Cong, Shengzhi Wang +2Multi-Agent Coordination Intrinsic Reward Methods
1Dalian University of Technology · 2Microsoft, Beijing, China
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
Jun 23, 2023Yash Paliwal, Rajarshi Roy, Jean-Raphaël Gaglione +5Reinforcement Learning Causal RL
Arizona State University, Arizona, USA · Max Planck Institute for Software Systems, Kaiserslautern, Germany · University of Texas at Austin, Texas, USA +3

Sep 30, 2026, cs.CVJ/K move · Enter open · S save

From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learning

Jialu Pi, Yanan Ma, Weijie Chen, Owen Crystal+7

Dept. of Radiology, Mayo Clinic, Phoenix, AZ, USA · School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA · Dept. of Radiology, Mayo Clinic, Phoenix, USA+2

Clinical Outcome Prediction Multimodal Reasoning Clinical Reasoning Causal RL Clinical Risk Prediction

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Major adverse cardiovascular events (MACE) remain the leading cause of mortality worldwide. Opportunistic screening using routinely acquired clinical data offers a scalable approach for identifying high-risk individuals before acute events occur. Although chest X-rays (CXRs) capture latent cardiovascular biomarkers and clinical histories provide complementary patient context, existing medical vision-language models are primarily optimized for radiology interpretation rather than prognostic reasoning. We propose a causal reinforcement learning framework for multimodal clinical reasoning that integrates CXRs and physician-authored clinical histories for opportunistic MACE prediction. The framework introduces (1) a role-decoupled dual-LLM architecture that separates reasoning from risk prediction, (2) a dual-action causal reinforcement learning policy for evidence selection and reasoning optimization, and (3) causal token pruning to learn compact multimodal representations. Evaluated on an internal cohort, an emergency department cohort, and the external MIMIC dataset, the proposed framework consistently outperformed unimodal baselines and state-of-the-art medical vision-language models, achieving AUROCs of 0.720, 0.760, and 0.845, respectively. It also substantially improved reasoning quality, achieving higher GREEN scores and higher expert preference while maintaining robust predictive performance across diverse patient populations.

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