README.md

metadata

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.

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:

@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