ResearchResearch paperLarge Language Models · Reinforcement Learning · Efficiency & Inference1 source · Oct 13, 2026

Coupled Harness--Model Evolution for Scientific Equation Discovery

We develop a coupled evolution framework that improves both parts from the same verifiable fit-error signal.

Large language models (LLMs) are increasingly used to discover scientific equations from experimental data.
Such a discovery system has two parts that must work together: a library of executable computational tools and the model's tool-use policy.
In the tool-evolution phase, an agent grows an executable tool library from a small set of generic numerical operations by diagnosing its own failures: it authors a new tool when a capability is missing and revises its selection rules when the right tool went unused.
The full system solves 79.1/71.3% of LSR-Synth problems at fit tolerances $10^{-2}$/$10^{-3}$, reaches 63.8% pooled accuracy on the distributionally different SRSD-Feynman panel, and obtains a normalized CP3-28 aggregate of $-7.27$.

[1]Coupled Harness--Model Evolution for Scientific Equation Discovery
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 05:17 AM
We develop a coupled evolution framework that improves both parts from the same verifiable fit-error signal.
Large language models (LLMs) are increasingly used to discover scientific equations from experimental data.

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