arXiv Artificial Intelligence

Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction

2 hours ago 09.10.2026 07:00 👁 2 views
Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction

Quick summary

arXiv:2610.10641v1 Announce Type: cross Abstract: Large language models (LLMs) offer promising potential for next-visit diagnosis prediction, owing to their ability to integrate longitudinal clinical evidence and reason over it in natural language. However, reinforcement learning for LLM reasoning commonly rewards each trajectory according to the correctness of its final answer. In next-visit diagnosis prediction, multiple diagnoses can be simultaneously valid, but independently rewarding one diagnosis per trajectory does not distinguish repeated hits from coverage of different diagnoses. The

Key takeaways

  • arXiv:2610.10641v1 Announce Type: cross Abstract: Large language models (LLMs) offer promising potential for next-visit diagnosis prediction, owing to their ability to integrate longitudinal clinical evidence and reason over it in natural language.
  • However, reinforcement learning for LLM reasoning commonly rewards each trajectory according to the correctness of its final answer.
  • In next-visit diagnosis prediction, multiple diagnoses can be simultaneously valid, but independently rewarding one diagnosis per trajectory does not distinguish repeated hits from coverage of different diagnoses.

Why it matters

“Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗