---
title: MANN Engram Showcase
emoji: 🧠
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.11.0
app_file: app.py
pinned: true
license: mit
github: https://github.com/Mr-wuff/MANN-Engram
tags:
  - Medical-AI
  - Multimodal
  - SiGLIP
  - Edge-Cloud
  - Routing
  - Privacy-Preserving
---

# MANN-Engram: Edge-Cloud Multimodal Semantic Router

🧠 A privacy-first, zero-hallucination shield for clinical vision-language models.

## Overview

MANN-Engram is an orchestration layer designed to solve the "Clinical Input Noise" problem in Large Multimodal Models (LMMs). It combines cloud intelligence for logical intent distillation with edge-side tensor routing for precise multimodal evidence selection.

## Core Philosophy

Clinical patient data is often noisy. A single diagnostic session may contain emotional complaints, billing frustrations, and unrelated imaging scans. Downstream VLMs can suffer from semantic drift or hallucinations when they process this irrelevant context.

MANN-Engram acts as a surgical filter:

- **Linguistic Distillation (Cloud)**: Uses Qwen-2.5-72B to remove non-medical noise and extract purified clinical intent.
- **Tensor Routing (Edge)**: Projects intent and imaging into a shared latent space via a skew-Gaussian optimized SiGLIP engine.
- **Precision Pruning**: Only verified core evidence is passed to the VLM context window.

## Key Capabilities

- **Linguistic de-noising**: Removes billing complaints, food issues, and emotional noise from patient narratives.
- **Multimodal saliency routing**: Selects relevant diagnostic scans (MRI/CT/X-ray) from unordered data dumps.
- **Dynamic gate control (`Top_p`)**: Allows clinician-controlled precision vs. recall.
- **Edge-cloud synergy**: Keeps privacy-sensitive tensor routing local while offloading reasoning to the cloud.

## Benchmark Case: "The Neurological Decoy"

- Scenario: A patient complains about hospital food and leg cramps, but also mentions a seizure and left-sided numbness.
- Input pool: 1x Brain MRI (Tumor), 1x Chest CT, 1x Abdominal CT, 1x Leg Angiogram.
- Challenge: Ignore the decoy complaints and identify the tumor.
- Result: At `Top_p = 0.6`, MANN-Engram achieves 100% noise suppression and routes only the Brain MRI as core evidence.

## Quick Start Guide

1. Obtain a Hugging Face token.
2. Enter it in the Settings panel.
3. Paste a clinical narrative and upload multiple images.
4. Adjust `Top_p`:
   - `0.6` for high precision
   - `0.85` for clinical safety
5. Review the output dashboard for purified intent and routed evidence.

## Developer Integration

The core logic is available as a standalone Python SDK.

```bash
git clone https://github.com/Mr-wuff/MANN-Engram.git
cd MANN-Engram
pip install -r requirements.txt
```

## Repository Structure

- `mann_engram_en/` — Core routing and intent extraction logic.
- `weights/` — Pre-trained skew-Gaussian weights for SiGLIP routing.
- `examples/` — Jupyter notebooks for threshold sensitivity analysis.

## Citation

If you use this project in research or clinical applications, please cite:

```bibtex
@software{MANN_Engram_2026,
  author = {WuFeiFan},
  title = {MANN-Engram: Edge-Cloud Multimodal Semantic Router},
  url = {https://github.com/Mr-wuff/MANN-Engram},
  year = {2026}
}
```

Created with ❤️ by Mr-wuff. Focused on advancing trustworthy AI in healthcare.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference