arXiv AI By Vincent Karpf, Joseph Reth, Eike Gerhardt, Audrey Wang, Anna Butz, Jiehao Xing, Jialing Song, Larry Callahan

Metacognitive Steering: Learning the Structure of Scientific Judgment

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arXiv AI
Aug 26

Self-Evolving Scientific Agent Designs Physically-Reasoned Whitebox Fluid Control

The paper introduces a self‑evolving scientific agent that uses large language models and iterative code generation to build interpretable, physically‑reasoned white‑box controllers for complex systems. The agent deploys candidate controllers in simulations, diagnoses dynamic behavior from multimodal evidence, and refines source code until a robust policy is achieved. Applied to a nonlinear fluid‑structure interaction problem—a two‑joint dogfish swimmer navigating an unsteady wake—the agent autonomously designs a controller that consistently reaches targets across a wide range of conditions without retraining.

By Boai Sun, Wenjin Guo, Zongmin Yu, Liu Yang
Hugging Face Trending Papers
Jul 29

SciDataSailor: Deep Scientific Data Exploring

Scientific datasets are commonly organized as hierarchical repositories containing heterogeneous and interdependent files, making their inspection, integration, and analysis labor-intensive and reliant on domain expertise. Although large language model (LLM) agents have advanced substantially in planning, reasoning, and tool use, existing research has largely overlooked their ability to interact with real scientific data assets through executable environments.

arXiv AI
Jun 9

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.

By Boai Sun, Wenjin Guo, Zongmin Yu, Liu Yang