Mao Zheng

DCPM reveals that separating memory processes can boost LLM agents' ability to infer user intentions and beliefs across sessions, leading to a dramatic increase in personalization accuracy.

Current translation benchmarks miss critical real-world constraints: IFMTBench shows instruction following scales more sharply with model size than translation quality, and general instruction following rankings correlate weakly with translation behavior.

LLMs might seem fluent in Chinese-English translation, but HardMTBench reveals their surprising struggles with domain-specific knowledge, exposing weaknesses hidden by standard benchmarks.

A 440MB multilingual translation model now rivals commercial APIs, opening the door for performant on-device translation.