CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling (2026)
Standard Transformers struggle with very long inputs because attention cost grows quadratically and the KV cache keeps expanding.
Read As Human: Compressing Context via Parallelizable Close Reading and Skimming (2026)
Long prompts, such as those produced by retrieval-augmented generation, make LLM inference slow because attention cost grows quadratically.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

CoMeT: Collaborative Memory Transformer for Efficient Long Context ModelingAnnual Meeting of the Association for Computational Linguistics (ACL)Runsong Zhao, Shilei Liu, Jiwei Tang, et al.2026
Read As Human: Compressing Context via Parallelizable Close Reading and SkimmingAnnual Meeting of the Association for Computational Linguistics (ACL)Jiwei Tang, Shilei Liu, Zhicheng Zhang, et al.2026
MTP-S2UT: Enhancing Speech-to-Speech Translation Quality with Multi-token Prediction ↗arXiv:2510.10003 · no summary yetJianjin Wang et al.2025

Co-authors here

12 of 14 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Updated 2 November 2026