ResearchNovember 05, 2026
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MLLMs finally learn to look closer
Researchers present PSGAgent, a method that improves scene graphs from multimodal language models through a think-act-observe loop.
#MLLM#Scene Graph#Agentic Reasoning#Computer Vision#RL
š„ What happened
Researchers from Tsinghua and partners unveiled PSGAgent, an agentic framework that turns Multimodal LLMs from passive image viewers into active detectives. Two tools ā Region Zoom-In and Visual Highlight ā let the model zoom into specific image regions and filter out irrelevant noise. Training uses a two-stage pipeline: an error-aware chain-of-thought dataset for cold start, then hierarchical rewards via GSPO.
š” Why it matters
Current MLLMs systematically miss small or peripheral objects because they only see a fixed-resolution view ā a real perception bottleneck. PSGAgent hits SOTA on the PSG benchmark, proving agentic reasoning isn't hype but a fix for a measurable flaw. The think-act-observe loop is a blueprint for all vision agents.
ā” Our take
Finally, someone tackles the obvious: static image viewers are so 2025. If you're not building agentic tools for perception now, you're literally not looking hard enough.
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