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AI Research
arXiv CS.AI·Jun 9·5 min read
Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents
TL;DR
Tool-augmented large language model (LLM) agents struggle with knowing when to use external APIs effectively. Contract2Tool is a framework that automates the generation of lightweight contracts specifying a tool's preconditions, effects, risk level, and cost.
✦ Why It Matters
Engineers can automate tool integration processes, improving efficiency and reliability in LLM applications.
Key Takeaways
- Automate tool contract generation to save time.
- Achieve high accuracy with learned contracts.
- Reduce the number of tools needed for tasks.
- Lower token usage for more efficient processing.
How It Works
Contract2Tool utilizes metadata, schemas, documentation, and execution traces to infer tool contracts. It converts observable evidence into symbolic contracts that define preconditions, effects, and risks associated with each tool.
This allows LLM agents to filter tools based on causal appropriateness, enhancing decision-making during task execution.