Budget-Constrained Agentic Large Language Models: Intention-Based Planning for Costly Tool Use

• URL: http://arxiv.org/abs/2602.11541v1
• Date: Thu, 12 Feb 2026 04:01:30 GMT
• Title: Budget-Constrained Agentic Large Language Models: Intention-Based Planning for Costly Tool Use
• Authors: Hanbing Liu, Chunhao Tian, Nan An, Ziyuan Wang, Pinyan Lu, Changyuan Yu, Qi Qi,
• Abstract summary: 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.<n>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.
• Score: 20.31276001607449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory [56.0946692457838]
  BudgetMem is a runtime agent memory framework for explicit, query-aware performance-cost control.<n>A lightweight router performs budget-tier routing across modules to balance task performance and memory construction cost.<n>Across LoCoMo, LongMemEval, and HotpotQA, BudgetMem surpasses strong baselines when performance is prioritized.
  arXiv  Detail & Related papers  (2026-02-05T18:57:09Z)
• Conformal Thinking: Risk Control for Reasoning on a Compute Budget [60.65072883773352]
  Reasoning Large Language Models (LLMs) enable test-time scaling, with dataset-level accuracy improving as the token budget increases.<n>We re-frame the budget setting problem as risk control, limiting the error rate while minimizing compute.<n>Our framework introduces an upper threshold that stops reasoning when the model is confident and a novel lower threshold that preemptively stops unsolvable instances.
  arXiv  Detail & Related papers  (2026-02-03T18:17:22Z)
• Budget-Aware Tool-Use Enables Effective Agent Scaling [82.6942342482552]
  Scaling test-time computation improves performance across different tasks on large language models (LLMs)<n>We study how to scale such agents effectively under explicit tool-call budgets, focusing on web search agents.<n>We introduce the Budget Tracker, a lightweight plug-in that provides the agent with continuous budget awareness.
  arXiv  Detail & Related papers  (2025-11-21T07:18:55Z)
• CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents [20.07359140801559]
  CostBench is a cost-centric benchmark designed to evaluate agents' economic reasoning and replanning abilities.<n>It comprises tasks solvable via multiple sequences of atomic and composite tools with diverse, customizable costs.<n>It also supports four types of dynamic blocking events, such as tool failures and cost changes, to simulate real-world unpredictability.
  arXiv  Detail & Related papers  (2025-11-04T16:58:29Z)
• BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens [33.607723102172194]
  BudgetThinker is a framework designed to empower Large Language Models with budget-aware reasoning.<n>We show that BudgetThinker significantly surpasses strong baselines in maintaining performance across a variety of reasoning budgets.
  arXiv  Detail & Related papers  (2025-08-24T03:17:50Z)
• Cost-Optimal Active AI Model Evaluation [71.2069549142394]
  Development of generative AI systems requires continual evaluation, data acquisition, and annotation.<n>We develop novel, cost-aware methods for actively balancing the use of a cheap, but often inaccurate, weak rater.<n>We derive a family of cost-optimal policies for allocating a given annotation budget between weak and strong raters.
  arXiv  Detail & Related papers  (2025-06-09T17:14:41Z)
• Scalable Chain of Thoughts via Elastic Reasoning [61.75753924952059]
  Elastic Reasoning is a novel framework for scalable chain of thoughts.<n>It separates reasoning into two phases--thinking and solution--with independently allocated budgets.<n>Our approach produces more concise and efficient reasoning even in unconstrained settings.
  arXiv  Detail & Related papers  (2025-05-08T15:01:06Z)
• Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario [62.615210194004106]
  Current research on tool learning primarily focuses on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness. In this paper, we address the selection of homogeneous tools by predicting both their performance and the associated cost required to accomplish a given task.
  arXiv  Detail & Related papers  (2024-06-18T09:24:09Z)
• Budget-Constrained Tool Learning with Planning [40.52601704021853]
  This paper proposes a novel method for budget-constrained tool learning. Our approach involves creating a preferable plan under the budget constraint before utilizing the tools.
  arXiv  Detail & Related papers  (2024-02-25T02:46:33Z)
• Planning with Dynamically Estimated Action Costs [2.8326418377665346]
  Information about action costs is critical for real-world AI planning applications. Recent approaches use black-box external action cost estimators, often learned from data, that are applied during the planning phase. We suggest a generalization of deterministic planning with action costs that allows selecting between multiple estimators for action cost.
  arXiv  Detail & Related papers  (2022-06-08T21:10:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.