MUSE

Researchers propose Agentic Tool Unlearning (ATU), a two-stage framework addressing tool-mediated recovery where LLM agents can retrieve forgotten knowledge via tools like web search. ATU combines par…

A study using microscopy with ultraviolet surface excitation (MUSE) found that 4x magnification achieves the same diagnostic accuracy as 10x for breast cancer margin detection, with deep learning (DL)…

Researchers have introduced BalDRO, a novel framework for balanced unlearning in large language models that addresses asynchronous forgetting by focusing on hard-to-unlearn data distributions. BalDRO,…