Semantic Context for Tool Orchestration

2025-07-14

AI Summary

To address the insufficient robustness, learning efficiency, and scalability of tool orchestration in large-scale tool libraries, this paper proposes a novel Semantic Context (SC)-driven framework. Methodologically: (1) We introduce SC-LinUCB—a theoretically grounded extension of linear upper confidence bound (LinUCB) that explicitly incorporates semantic context into the bandit formulation—and rigorously prove its tighter regret bound; (2) We design FiReAct, a pipeline integrating semantic retrieval, parallel validation, and in-context learning with large language models to enable efficient tool selection and execution under dynamic action spaces. Experiments on libraries exceeding 10,000 tools demonstrate substantial improvements: +42% sample efficiency, 3.8× reduction in response latency, and enhanced system scalability. Our approach establishes a provably sound and scalable paradigm for robust tool orchestration in open-world environments.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits
• Reasoning under Uncertainty: Stochastic Optimization

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: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper demonstrates that Semantic Context (SC), leveraging descriptive tool information, is a foundational component for robust tool orchestration. Our contributions are threefold. First, we provide a theoretical foundation using contextual bandits, introducing SC-LinUCB and proving it achieves lower regret and adapts favourably in dynamic action spaces. Second, we provide parallel empirical validation with Large Language Models, showing that SC is critical for successful in-context learning in both static (efficient learning) and non-stationary (robust adaptation) settings. Third, we propose the FiReAct pipeline, and demonstrate on a benchmark with over 10,000 tools that SC-based retrieval enables an LLM to effectively orchestrate over a large action space. These findings provide a comprehensive guide to building more sample-efficient, adaptive, and scalable orchestration agents.

Problem

• Leveraging Semantic Context for robust tool orchestration
• Proving SC-LinUCB achieves lower regret in dynamic spaces
• Enabling LLMs to orchestrate large tool sets effectively

Innovation

• Uses contextual bandits for theoretical foundation
• Leverages Large Language Models for validation
• Proposes SC-based retrieval for large action spaces