arXiv AI June 9, 2026 By Boai Sun, Wenjin Guo, Zongmin Yu, Liu Yang

Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control

arXiv:2606. 08405v1 Announce Type: new Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 1

The HydroGym Reinforcement Learning Platform for Fluid Dynamics

arXiv:2512. 17534v2 Announce Type: replace-cross Abstract: Modeling and controlling fluids is critical across science and engineering.

By Christian Lagemann, Sajeda Mokbel, Miro Gondrum, Mario R\"uttgers, Yuning Wang, Pol Su\'arez, Ludger Paehler, Deniz A. Bezgin, Aaron B. Buhendwa, Jared L. Callaham, Samuel Ahnert, Nicholas Zolman, Xiao Shao, Jean-Christophe Loiseau, Nikolaus Adams, Matthias Meinke, Wolfgang Schr\"oder, Kai Lagemann, Esther Lagemann, Ricardo Vinuesa, Steven L. Brunton

agents reinforcement-learning robotics benchmarks

arXiv AI

Jul 14

Heuristic Learning for Active Flow Control Using Coding Agents

arXiv:2607. 11565v1 Announce Type: cross Abstract: Active flow control involves nonlinear dynamics, partial observations, and computationally expensive simulations, making controller design particularly challenging.

By Paul Garnier, Jonathan Viquerat, Elie Hachem

agents reinforcement-learning efficiency benchmarks

arXiv AI

Jul 28

LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization

arXiv:2602. 10576v2 Announce Type: replace-cross Abstract: Symbolic regression aims to distill mathematical equations from observational data.

By Boxiao Wang, Kai Li, Tianyi Liu, Chen Li, Junzhe Wang, Yifan Zhang, Jian Cheng

llms reinforcement-learning benchmarks

arXiv AI

Jun 12

From Digital to Physical: Digital Agents as Autonomous Coaches for Physical Intelligence

arXiv:2601. 21570v2 Announce Type: replace Abstract: The field of Embodied AI is witnessing a rapid evolution toward general-purpose robotic systems, fueled by high-fidelity simulation and large-scale data collection.

By Zixing Lei, Genjia Liu, Yuanshuo Zhang, Qipeng Liu, Yuzhu Cai, Sixiang Chen, Jixian Wu, Yunhong Wang, Weixin Li, Chuan Wen, Bo Zhao, Shanghang Zhang, Wenzhao Lian, Siheng Chen

llms agents diffusion reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jul 16

Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning

arXiv:2607. 13553v1 Announce Type: cross Abstract: Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredictability of realistic environments.

By Andrea Maria Braghin, Nicol\`o Botteghi, Matteo Tomasetto, Andrea Manzoni, Gabriele Cazzulani

agents reinforcement-learning robotics

arXiv Machine Learning

Jun 26

Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries

arXiv:2606. 26154v1 Announce Type: cross Abstract: Autonomous microrobots navigating biological vasculature could enable targeted drug delivery and thrombolysis, yet training control policies for realistic environments remains an open challenge.

By Jannik Drotleff, Samuel Tovey, Paul Hohenberger, Christoph Lohrmann, Julian Ho{\ss}bach, Konstantin Nikolaou, Christian Holm

agents reinforcement-learning robotics efficiency

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The HydroGym Reinforcement Learning Platform for Fluid Dynamics

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LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization

From Digital to Physical: Digital Agents as Autonomous Coaches for Physical Intelligence

Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning

Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries