TMLR

MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications

MEDIC：对LLM在临床应用中的安全性和实用性领先指标的综合评估

Praveenkumar Kanithi, Clément Christophe, Marco AF Pimentel, Tathagata Raha, Prateek Munjal, Nada Saadi, Hamza A Javed, Svetlana Maslenkova, Nasir Hayat, Ronnie Rajan, Shadab Khan

机构 * M42 纠错

AI总结 MEDIC通过综合评估框架揭示LLM在临床应用中的安全性和实用性差异，强调需采用组合方法以应对多维度性能权衡。

Comments Published in Transactions on Machine Learning Research (06/2026)

ARC-Encoder: learning compressed text representations for large language models

ARC-Encoder：为大语言模型学习压缩文本表示

Hippolyte Pilchen, Edouard Grave, Patrick Pérez

机构 * Kyutai 纠错

AI总结 ARC-Encoder是可将文本压缩为原词元数1/4或1/8的编码器，适配多类LLM解码器，在提升推理效率的同时保持最优性能，为大语言模型提供灵活高效的上下文压缩方案。

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Journal ref Transactions on Machine Learning Research 2026 (https://openreview.net/forum?id=lU1P9dsqfn)