Qwen

LLMs can Compress LLMs: Adaptive Pruning by Agents

TL;DR An LLM acts as a coach to prune another LLM, shrinking it ~45% while preserving key knowledge and accuracy. Traditional pruning uses fixed rules and often wipes out facts. This paper lets a foundation model adaptively choose which layers to trim each round. It reads layer sensitivity snapshots—

Be My Eyes: Small 'eyes', big 'brain'—a modular path to multimodal AI

LLMs are great thinkers—but they’re mostly text-only. BeMyEyes is a new way to give them “sight” without building giant, expensive multimodal models. * Two agents, one goal: a lean Perceiver (vision-language model) looks at images or other formats, while a powerful Reasoner LLM thinks through the answer. They collaborate