TARSE: Test-Time Adaptation via Retrieval of Skills and Experience for Reasoning Agents
Complex clinical decision making often fails not because a model lacks facts, but because it cannot reliably select and apply the right procedural knowledge and the right prior example at the right reasoning step. We frame clinical question answering as an agent problem with two explicit, retrievable resources: skills, reusable clinical procedures such as guidelines, protocols, and pharmacologic mechanisms; and experience, verified reasoning trajectories from previously solved cases (e.g., chain-of-thought solutions and their step-level decompositions). At test time, the agent retrieves both relevant skills and experiences from curated libraries and performs lightweight test-time adaptati...
| Authors | Junda Wang, Zonghai Tao, Hansi Zeng et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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