The authors developed SRHarness around three components: composable scientific actions for analyzing data and candidate equations, persistent state for retaining evaluated hypotheses, and lifecycle management for continuing, branching, restarting, or terminating search trajectories.
Structured agent harnesses improve LLM-driven discovery of symbolic equations
SRHarness organizes scientific operations, hypothesis history, and search trajectories, improving equation recovery without changing the underlying language model.
Top University
Zihan Yu · Shixuan Zhou · Hao Huang · Jingtao Ding · Yong Li
BNRist · Tsinghua University
Research Digest··2 min read
Yu and colleagues built a domain-specific runtime for agentic symbolic regression, where language models search for interpretable equations that explain observed data.
Why this paper
From Tsinghua University and BNRist
In one line
SRHarness, a structured runtime harness, improves agentic symbolic regression accuracy via composable actions, persistent state, and trajectory lifecycle management.
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