🔍MLLMs are nearsighted: zooming helps but kills speed. What if we teach them to "see" fine details without zooming?
Introducing Region-to-Image Distillation(R2I): we train models to internalize "zooming".
🏆 ZwZ-8B achieves SOTA performance on fine-grained perception with zero tool calls.
📊 Plus ZoomBench: human-AI collaboration construction, dual-view "zooming gap" metric
Dive into our joint efforts:
📑Paper: https://t.co/tdEn2QwoKD
🔗Code: https://t.co/b8S4HqcdPT
⚙️ Model&Data: https://t.co/5ZDak0L3C8
#OpenSource #MLLM #LLMs #inclusionAI
distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
Why it matters
If a model internalizes zooming, a perception AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → drops an entire tool-call loop and its latency. ZwZ-8B and ZoomBench provide a checkpoint and a benchmarkA standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.Full definition → to test that trade in your own pipeline.
