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[2602.16699] Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents
Summary
The paper presents a framework called Calibrate-Then-Act (CTA) that enables LLMs to optimize decision-making by balancing cost and uncertainty in exploration tasks.
Why It Matters
As LLMs are increasingly applied to complex tasks requiring interaction with environments, understanding how to manage cost-uncertainty tradeoffs becomes crucial. This research offers a structured approach to enhance the efficiency and effectiveness of LLMs in real-world applications.
Key Takeaways
- Introduces the Calibrate-Then-Act (CTA) framework for LLMs.
- Focuses on optimizing decision-making through cost-uncertainty tradeoffs.
- Demonstrates improved performance in information retrieval and coding tasks.
- Formalizes complex tasks as sequential decision-making problems.
- Shows that explicit reasoning about costs leads to better exploration strategies.
Computer Science > Computation and Language arXiv:2602.16699 (cs) [Submitted on 18 Feb 2026] Title:Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents Authors:Wenxuan Ding, Nicholas Tomlin, Greg Durrett View a PDF of the paper titled Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents, by Wenxuan Ding and 2 other authors View PDF HTML (experimental) Abstract:LLMs are increasingly being used for complex problems which are not necessarily resolved in a single response, but require interacting with an environment to acquire information. In these scenarios, LLMs must reason about inherent cost-uncertainty tradeoffs in when to stop exploring and commit to an answer. For instance, on a programming task, an LLM should test a generated code snippet if it is uncertain about the correctness of that code; the cost of writing a test is nonzero, but typically lower than the cost of making a mistake. In this work, we show that we can induce LLMs to explicitly reason about balancing these cost-uncertainty tradeoffs, then perform more optimal environment exploration. We formalize multiple tasks, including information retrieval and coding, as sequential decision-making problems under uncertainty. Each problem has latent environment state that can be reasoned about via a prior which is passed to the LLM agent. We introduce a framework called Calibrate-Then-Act (CTA), where we feed the LLM this additional context to enable it to act more optimally. This improvement is preserved eve...
