LLMasTool Framework Uses Code-Mined Tree Transformations for Neural Architecture Search
A recent study introduces LLMasTool, a hierarchical tree-based framework designed for Neural Architecture Search (NAS) that views large language models (LLMs) as tools instead of independent agents. This innovative method tackles the shortcomings of existing NAS techniques, which often rely on meticulously crafted search spaces that limit exploration. In contrast, recent agentic approaches using LLMs have had difficulty producing complex, valid architectures consistently. LLMasTool can automatically identify reusable modules from any source code and represents complete architectures as hierarchical trees, facilitating evolution through dependable tree transformations rather than direct code generation. This creates a robust model evolution system that integrates reliable algorithmic search with the capabilities of LLMs. The findings were shared on arXiv under identifier 2604.16555v1, as a cross-type submission.
Key facts
- LLMasTool is a hierarchical tree-based NAS framework
- It treats large language models as tools rather than autonomous agents
- The method automatically extracts reusable modules from arbitrary source code
- Full architectures are represented as hierarchical trees
- Evolution occurs through reliable tree transformations instead of code generation
- Addresses bias in current LLMs toward narrow patterns from training data
- Published on arXiv with identifier 2604.16555v1
- Bridges reliable algorithmic search with LLM assistance
Entities
Institutions
- arXiv