CRAFT: LLM-Based Iterative Refinement for Temporal Reasoning over Clinical Narratives
Understanding the temporal progression of symptoms in clinical narratives is critical for disease monitoring, safety surveillance, and causality assessment. Clinical narratives, however, rarely provide explicit temporal anchors. Current approaches to temporal information reasoning focus predominantly on pairwise relation classification across multi-visit and timestamp-rich records, leaving the reconstruction of structured symptom trajectories from individual anchor-sparse reports largely unaddressed. We propose CRAFT, an LLM framework that pairs a generator with a constraint-based verifier to iteratively produce and refine stage-wise symptom timelines through targeted feedback. We conduct...
| Authors | Chengyang He, Tahreem Arif, Marko Zivkovic et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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