insight-o3-multimodal

name	insight-o3-multimodal
title	InSight-o3: Empowering Multimodal Foundation Models with Visual Search
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.18745
keywords	["multimodal","vision-language","search","reasoning","benchmarking"]
description	Enable VLMs to perform generalized visual search—locating relational, fuzzy, and conceptual regions from free-form language descriptions. Introduces O3-Bench benchmark with high-density composite charts/maps, uses RL-trained vSearcher for spatial localization, improving frontier models (GPT-5-mini 39%→61.5%) without architecture changes.

Overview

InSight-o3 addresses VLM weakness with dense, complex visuals requiring both advanced reasoning and precise visual perception.

Core Technique

Generalized Visual Search:

class VisualSearcher:
    def search_conceptual_regions(self, image, query):
        """Find relational/fuzzy/conceptual regions from free-form language."""
        # e.g., "regions where trend changes" not just object names
        regions = model.predict_regions(image, query)
        return regions

RL-Trained vSearcher: Hybrid RL with in-loop feedback (vReasoner) and IoU supervision.

Performance

• GPT-5-mini: 39.0% → 61.5% on O3-Bench
• Plug-and-play enhancement

References

• Generalized visual search capability
• O3-Bench benchmark for dense visuals