CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents
arXiv:2511.02734v3 Announce Type: replace Abstract: Current evaluations of Large Language Model (LLM) agents primarily emphasize task completion, often overlooking resource efficiency and adaptability. This neglects a crucial capability: agents' ability to devise and adjust cost-optimal plans in response to changing environments. To bridge this gap, we introduce CostBench, a scalable, cost-centric benchmark designed to evaluate agents' economic reasoning and replanning abilities. Situated in the travel-planning domain, CostBench comprises tasks solvable via multiple sequences of atomic and com
The rapid advancement of large language models is shifting focus from mere task completion to the efficiency and economic reasoning of AI agents in complex, dynamic environments.
Evaluating cost-optimal planning and adaptation is crucial for the deployment of truly autonomous and economically viable AI agents, moving beyond simple task execution.
The introduction of CostBench changes the evaluation paradigm for LLM agents, setting a new standard for assessing their ability to handle real-world cost constraints and dynamic environments.
- · AI Agent Developers
- · Cloud Computing Providers (for optimized agent usage)
- · Enterprises Adopting LLM Agents
- · Academic AI Research
- · LLM Agents Incapable of Cost Optimization
- · Businesses with Inefficient AI Deployments
- · Legacy AI Task Automation Systems
New benchmarks like CostBench will drive innovation in more resource-efficient and adaptable LLM agent architectures.
Enterprises will prioritize LLM agents that demonstrate superior cost-optimal planning, leading to a competitive advantage for providers focused on efficiency.
The widespread adoption of cost-aware AI agents could significantly reduce operational expenditures across various industries, accelerating automation and potentially impacting labor markets.
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Read at arXiv cs.AI