---
pattern_id: pattern_deepmind_mmv
applicable_symptoms: [deepmind_mmv]
domain: Perception_Vision
---

# Cross-modal representation learning lacks a unified architecture that can handle multiple modalities (e.g., vision, language, audio) with a single network.

**Domain**: `Perception_Vision`

## Fix

Use a multimodal versatile network (MMV) with shared transformer layers and modality-specific encoders to learn joint embeddings across modalities.

## Anti-pattern

Training separate unimodal models and fusing late representations.

## Cross-domain analogies

- **Planning_Decision** → Use structured decoupling to separate modality-specific encoders from a shared reasoning backbone.
  - related fix: Use structured reasoning (e.g., Chain-of-Thought with Critical Layers) that decouples navigation strategy from environment-specific features.
- **Learning_Training** → Use sensor-calibrated noise injection to unify modality-specific distortions into a shared training distribution.
  - related fix: Synthetic depth image generation with self-occlusion-aware ray casting and noise-aware modeling calibrated from real depth sensor characteristics.
- **Control_Locomotion** → Use blocked modality channels to trigger alternative fusion pathways until alignment succeeds.
  - related fix: Trial-and-error heuristic: when an action is blocked, systematically try alternative actions until a traversable path is found or state is exhausted.

## Patch

```diff
--- deepmind_mmv.before.py
+++ deepmind_mmv.after.py
@@ -1,2 +1,4 @@
-# --- BEFORE (vulnerable to the symptom below) ---

+# --- AFTER (ROSCLAW heuristic graft) ---

 # Symptom: Cross-modal representation learning lacks a unified architecture that can handle multiple modalities (e.g., vision, language, audio) with a single network.

+# Fix    : Use a multimodal versatile network (MMV) with shared transformer layers and modality-specific encoders to learn joint embeddings across modalities.

+# Avoid  : Training separate unimodal models and fusing late representations.

```
