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SkillVine is proposed, an automatic skill-evolution framework that formulates skill evolution as a graph search problem and employs a branching exploration strategy, and achieves a balance between exploration and exploitation.
Symbolic regression (SR) seeks to discover parsimonious mathematical laws from observational data, yet conventional approaches often struggle with the vast combinatorial search space of physically meaningful expressions. We present InsightSR, a framework that embeds Large Language Models (LLMs) as a guiding layer aroun...
Results on diverse long-horizon benchmarks demonstrate the efficacy of ScienceFlow's ability to sustain effective research processes, and demonstrates that efficient state management, adaptive exploration, and objective-aligned execution are critical for scaling autonomous research beyond short-horizon interactions.
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