Multimodality Matrix Training (Experimental)

Status: EXPERIMENTAL — not authoritative spec.

This document defines a trainable matrix-first multimodal stack for SCBE-adjacent research.

Objective

Treat modality alignment as first-class structure:

encode modalities -> embeddings E [B, M, D]
compute alignment matrix A [B, M, M]
matrix-weighted fusion -> z_fused
optimize with contrastive alignment + conflict penalty
expose governance telemetry (coherence, drift, conflict)

Implemented scaffold

experimental/multimodal/multimodal_matrix.py

Includes:

MultiModalMatrix
MatrixWeightedFusion
simple text/image/state encoders
clip_contrastive_loss, conflict_penalty
governance_proxy hook
DummyMultimodalDataset + train_dummy

Run

python experimental/multimodal/multimodal_matrix.py

Integration guidance

Use governance_proxy(A) outputs as signal inputs for SCBE policy gates:

coherence -> permit confidence
drift -> scrutiny escalation
conflict -> quarantine/denial pressure

Multi-Model Extension

See MULTI_MODEL_MODAL_MATRIX.md for the N-model x K-modality voting matrix spec that extends this single-model scaffold into a multi-model governance reducer.

Notes

This module is a training scaffold, not production governance logic.
Canonical protocol behavior remains in root SPEC.md.