A contrastive learning framework integrating discrepancy estimation and adaptive attention for medical time-series diagnosis.

We propose InDiGO, a knowledge-aware, diversity-optimized framework that aligns clinical signals with decision cues to iteratively refine series-text prompts, enabling effective and generalizable medical time-series decoding with LLMs.