CausalEvo: Causal Skill Self-Evolution for Health LLM Agents
Read the original on arXiv Machine Learning →CausalEvo is a method that enables large language model agents in health to evolve their textual skills—such as calculation rules and measurement units—by evaluating the causal impact of skill changes while keeping model weights, feedback cases, and rule count constant. The approach freezes the LLM to propose candidate rules, pools them with existing skills, and uses paired skill replacements scored on shared feedback to rank and retain the most effective rules. In experiments on six MedCalc-Bench-Verified tasks, CausalEvo achieved 60.00% accuracy, outperforming SkillOpt (55.83%) and a no‑skill baseline (51.67%), while also reducing training token usage by 55.6%. whyItMatters":"CausalEvo demonstrates that controlled, causal evaluation of skill updates can improve health‑focused LLM performance and efficiency, directly benefiting the reliability and resource usage of medical language agents."
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