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arXiv:2605.28740v2 Announce Type: replace-cross Abstract: As large language models are increasingly deployed for clinical text, ensuring they can reliably signal their own uncertainty becomes critica...
arXiv:2608. 03655v1 Announce Type: cross Abstract: Abstractive summarization models remain vulnerable to factual inconsistency, redundancy, and weak length control.
arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.
arXiv:2608. 19825v1 Announce Type: cross Abstract: Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems.
arXiv:2605.09492v3 Announce Type: replace Abstract: Reliable text generation is critical for deploying large language models (LLMs) in real-world applications, particularly in high-stakes domains suc...
RASPER is a reward‑aligned summarizer that tailors the extraction of information from unstructured discharge notes to improve downstream clinical predictions. It uses a tunable LLM summarizer trained with reinforcement learning, where the reward comes from the loss of a downstream predictor, and incorporates patient‑specific context via a longitudinal encoder that soft‑prompts the summarizer with structured codes. The approach consistently outperforms strong baselines on readmission prediction and medication recommendation tasks in the MIMIC‑III and MIMIC‑IV datasets.