Beyond Static Tools: Test-Time Tool Evolution for Scientific Reasoning

2026-01-12

AI Summary

This work addresses the limitations of existing large language model (LLM) agents, which rely on static tool libraries and struggle with the sparsity, heterogeneity, and incompleteness of tools in scientific reasoning. To overcome this, we propose a novel Test-Time Tool Evolution (TTE) paradigm that dynamically synthesizes, verifies, and optimizes executable tools during inference, transforming tools from predefined resources into problem-driven artifacts. Our approach integrates LLMs with program synthesis and automated verification to establish an end-to-end pipeline for tool generation and evolution. Evaluated on SciEvo—a newly curated benchmark comprising 1,590 tasks and 925 evolved tools—our method achieves state-of-the-art performance, significantly improving accuracy, tool efficiency, and cross-domain transferability.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The central challenge of AI for Science is not reasoning alone, but the ability to create computational methods in an open-ended scientific world. Existing LLM-based agents rely on static, pre-defined tool libraries, a paradigm that fundamentally fails in scientific domains where tools are sparse, heterogeneous, and intrinsically incomplete. In this paper, we propose Test-Time Tool Evolution (TTE), a new paradigm that enables agents to synthesize, verify, and evolve executable tools during inference. By transforming tools from fixed resources into problem-driven artifacts, TTE overcomes the rigidity and long-tail limitations of static tool libraries. To facilitate rigorous evaluation, we introduce SciEvo, a benchmark comprising 1,590 scientific reasoning tasks supported by 925 automatically evolved tools. Extensive experiments show that TTE achieves state-of-the-art performance in both accuracy and tool efficiency, while enabling effective cross-domain adaptation of computational tools. The code and benchmark have been released at https://github.com/lujiaxuan0520/Test-Time-Tool-Evol.

Problem

• scientific reasoning
• static tool libraries
• tool evolution
• open-ended scientific world
• computational methods

Innovation

• Test-Time Tool Evolution
• dynamic tool synthesis
• scientific reasoning
• LLM-based agents
• tool evolution