FitText: New Framework Improves AI Agent Tool Selection Through Dynamic Retrieval
Researchers have introduced FitText, a training-free framework that dynamically improves how AI agents retrieve relevant tools from large API ecosystems during task execution. The system addresses a 'semantic gap' between how users describe tasks and how tools are documented, using an iterative, evolution-inspired process to refine search queries in real time. On a benchmark of 16,464 APIs, FitText achieved an 84.3% pass rate—a 26.7-point absolute improvement over static retrieval methods.
FitText, presented in a preprint on arXiv, targets a core limitation in AI agent systems: static tool retrieval that relies solely on the user's initial query fails to keep pace with how an agent's understanding of a task evolves during execution. The framework embeds retrieval directly into the agent's reasoning loop, treating it as a test-time evolutionary process in which the agent generates and iteratively refines natural-language 'pseudo-tool descriptions'—working hypotheses about what tool it needs. A component called Memetic Retrieval applies evolutionary selection pressure over these candidate descriptions, guided by a tool memory that prevents redundant searches. On the ToolRet benchmark spanning three domains, FitText's reformulation strategies improved NDCG@5 by 2.7 to 10.6 points over static retrieval across all tested base models. On StableToolBench, which covers 16,464 APIs, the full Memetic Retrieval approach with GPT-4o-mini achieved an 84.3% pooled pass rate, a 26.7-point absolute gain. Notably, the framework requires no additional model training, making it broadly applicable to existing agent architectures.
What's missing
The paper has not yet undergone formal peer review, as it is a preprint.
What different sources said
- arXiv cs.AICenter
FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
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