VisionGPT

See like humans. Think like scientists.

VisionGPT is a reasoning-first visual foundation model designed to solve one of the largest weaknesses in modern vision-language systems:

they can describe images, but they struggle to reason about them.

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The Problem

Modern vision models typically follow a simple pipeline:

Image
 ↓
Vision Encoder
 ↓
Language Model
 ↓
Answer

This approach works well for:

• Captioning
• Basic visual question answering
• OCR
• General image understanding

However, it often struggles with:

• Counting
• Spatial reasoning
• Object relationships
• Multi-step visual logic
• Explainable decision making

Example:

Question:

How many people are holding umbrellas?

Traditional Models:

Image
 ↓
Answer

VisionGPT:

Image
 ↓
Objects
 ↓
Relationships
 ↓
Reasoning
 ↓
Answer

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VisionGPT Architecture

VisionGPT is built around explicit reasoning.

┌─────────────────┐
│ Input Image     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Perception      │
│ Engine          │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Scene Graph     │
│ Engine          │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Spatial         │
│ Reasoning       │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Reasoning       │
│ Engine          │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Response        │
│ Engine          │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Final Answer    │
└─────────────────┘

Every answer is derived through explicit reasoning stages.

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Core Principles

1. Reasoning First

VisionGPT is optimized for:

• Counting
• Spatial understanding
• Relationship reasoning
• Multi-step logic

instead of pure caption generation.

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2. Explainable Outputs

Every answer should be traceable.

Objects
 ↓
Relationships
 ↓
Facts
 ↓
Reasoning
 ↓
Answer

No black-box decisions.

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3. Low Hallucination Design

VisionGPT attempts to prevent unsupported conclusions by restricting responses to facts produced by earlier stages.

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4. Modular Architecture

Each subsystem can be developed independently.

Perception

Scene Graph

Spatial

Reasoning

Response

This makes debugging and evaluation significantly easier.

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Model Roadmap

VisionGPT-10B

Initial foundation release.

Perception Engine:         3.0B
Scene Graph Engine:        1.0B
Spatial Reasoning:         0.5B
Reasoning Engine:          4.0B
Response Engine:           1.5B

Total:                    10.0B

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Repository Structure

VisionGPT/

├── docs/
│
├── models/
│
├── datasets/
│
├── training/
│
├── evaluation/
│
├── serving/
│
├── applications/
│
├── research/
│
├── infra/
│
├── tests/
│
└── tools/

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Documentation

Project specifications live under:

docs/specs/

Current specifications:

MODEL_SPEC.md

ARCHITECTURE.md

DATASET_SPEC.md

TRAINING_SPEC.md

EVALUATION_SPEC.md

REPOSITORY_SPEC.md

INTERFACE_SPEC.md

These documents define the project before implementation begins.

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Contributing

VisionGPT is being built as an open architecture project focused on reasoning-first visual intelligence.

Contributions are welcome after the core architecture reaches implementation stability.

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License

Apache 2.0

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Vision

The goal is not to build another model that looks at images.

The goal is to build a system that can:

Observe

Understand

Reason

Explain

with every conclusion grounded in evidence.