予算制約付きエージェント型大規模言語モデル：高コストツール利用のための意図ベース計画
Budget-Constrained Agentic Large Language Models: Intention-Based Planning for Costly Tool Use
厳しい金銭的予算下で外部ツールを呼び出して多段階タスクを解くLLMエージェントのための、意図認識型階層的世界モデルによる推論時計画フレームワークINTENTを提案。
著者: Hanbing Liu, Chunhao Tian, Nan An, Ziyuan Wang, Pinyan Lu, Changyuan Yu, Qi Qi
分類: cs.AI, cs.LG
原文アブストラクト
We study budget-constrained tool-augmented agents, where a large language model must solve multi-step tasks by invoking external tools under a strict monetary budget. We formalize this setting as sequential decision making in context space with priced and stochastic tool executions, making direct planning intractable due to massive state-action spaces, high variance of outcomes and prohibitive exploration cost. To address these challenges, we propose INTENT, an inference-time planning framework that leverages an intention-aware hierarchical world model to anticipate future tool usage, risk-calibrated cost, and guide decisions online. Across cost-augmented StableToolBench, INTENT strictly enforces hard budget feasibility while substantially improving task success over baselines, and remains robust under dynamic market shifts such as tool price changes and varying budgets.