TusoAI: Agentic Optimization for Scientific Methods

Alistair Turcan, Kexin Huang, Lei Li, Martin Jinye Zhang

Keywords: AI for Science, Agentic AI, Code Optimization, AutoML

TL;DR: We present TusoAI, an agentic approach to constructing scientific methods, either from scratch, or improving upon a state-of-the-art tool.

Abstract:

Scientific discovery is often slowed by the manual development of computational
tools needed to analyze complex experimental data. Building such tools is costly
and time-consuming because scientists must iteratively review literature, test mod-
eling and scientific assumptions against empirical data, and implement these in-
sights into efficient software. Large language models (LLMs) have demonstrated
strong capabilities in synthesizing literature, reasoning with empirical data, and
generating domain-specific code, offering new opportunities to accelerate com-
putational method development. Existing LLM-based systems either focus on
performing scientific analyses using existing computational methods or on de-
veloping computational methods or models for general machine learning without
effectively integrating the often unstructured knowledge specific to scientific do-
mains. Here, we introduce TusoAI, an agentic AI system that takes a scientific task
description with an evaluation function and autonomously develops and optimizes
computational methods for the application. TusoAI integrates domain knowledge
into a knowledge tree representation and performs iterative, domain-specific op-
timization and model diagnosis, improving performance over a pool of candidate
solutions. We conducted comprehensive benchmark evaluations demonstrating
that TusoAI outperforms state-of-the-art expert methods, MLE agents, and scien-
tific AI agents across diverse tasks. Applying TusoAI to two key open problems
in genetics improved existing computational methods and uncovered new biology
missed by previous methods. Our code is publicly available at https://github.com/Alistair-Turcan/TusoAI.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 20069