# Insight O3 Multimodal

> 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.

- Skill: `adu2021/insight-o3-multimodal` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/insight-o3-multimodal`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/insight-o3-multimodal/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/insight-o3-multimodal

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## Overview

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

## Core Technique

**Generalized Visual Search:**

```python
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