Reproduce “Semantic Context for Tool Orchestration” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Robert Müller
This work formally quantifies the benefits of providing natural language descriptions of tools, termed Semantic Context (SC), to intelligent agents for tool orchestration. The approach improves sample efficiency, adaptability, and scalability in both theoretical contextual bandit models and large language models (LLMs), with the FiReAct pipeline enabling high accuracy across over 10,000 tools.
Key insights
- Semantic Context provides a more parsimonious representation of the action space, allowing for more efficient parameter learning and significantly lower regret in tool selection.
- LLMs, when provided with semantic context, engage in a two-step semantic matching process, inferring required capabilities from queries and matching them against tool descriptions for effective in-context learning.
- Semantic features enable robust and continual adaptation in dynamic environments where tool sets evolve, preventing catastrophic forgetting and costly relearning without model re-initialization.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.