Study examines hindsight bias in clinical LLM temporal reasoning
A new arXiv paper argues that clinical language models are frequently assessed on retrospective patient records that already contain the eventual diagnosis, treatment response and outcome. Because those records expose information a real prospective decision-maker would not have, such benchmarks may reward models for exploiting future data instead of genuine reasoning. The work examines how this exposure shapes model judgments in clinical temporal tasks.