The Menu Is an Execution Prior: State-Path Tool Menus for Online Agents

2026-09-08

AI Summary

本文提出状态路径工具菜单，通过学习从请求状态到结果的预执行路径来优化在线代理的工具选择，提高多步骤任务的成功率。

Abstract

Language models act through tools, yet practical agents face libraries containing thousands of interfaces. We introduce the tool menu as the short, ordered subset of available tools shown to an agent before execution. The agent can call only tools in this menu. Multi-step tasks require the final action and the prerequisite tools that create its inputs in a usable order. Current constructors rank tools by request relevance, which can surface the final action while omitting or delaying less obvious producers. We introduce the state path, a pre-execution route from the observable request state to the desired outcome, and propose State-Path Tool Menu to learn it. Our framework treats the menu as an execution prior over these routes. Its encoder represents which tools can run from the current state, how their outputs satisfy later inputs, and which orders recur in training paths. A retriever covers an executable entry, the missing-input producers, and the final action. A reranker then places producers before consumers. On ToolBench, our menu raises online success from 0.737 to 0.898 and outperforms retrieval, reranking, generation, and routing baselines without changing the agent. The State-Path menu also covers more complete chains with 32 tools than the official list covers with 128, and its success gain persists across executor families with different model capacities. Our code is at https://github.com/Met2348/State-Path.

Problem

• language models
• tool menu
• multi-step tasks
• state path
• execution prior

Innovation

• State-Path Tool Menu
• Execution Prior
• Tool Ranking
• Multi-step Tasks