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trimodal-hardcoded-moe-toy

Name: trimodal-hardcoded-moe-toy
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Tri-Modal Transformer with Hard-coded Modality Routing (Toy-Scale, 12.1M params)

A toy-scale (12.1M parameters) multimodal Transformer that simultaneously processes Vision, Audio, and Text using Shared Attention + Hard-coded Modality-Specific FFN routing.

Architecture

Shared Multi-Head Attention across all modalities (cross-modal interaction)
3 Separated FFN experts: FFN_vision, FFN_audio, FFN_text (hard-coded routing by modality type, no learnable gating)
Asymmetric attention mask: Vision↔Audio bidirectional, Text→Vision/Audio allowed, Vision/Audio→Text blocked, Text internal causal

Key Results (50 epochs)

Metric	3-way (V+A+T)	2-way (V+T)	2-way (A+T)
Vision MSE	0.0397	0.0388	—
Audio MSE	0.0238	—	0.0227
Text CE	2.1804	2.2026	2.0011

No cross-modality interference: Adding a third modality does not degrade individual modality performance (<5% difference).

Ablation: FFN Routing Strategies (Phase 1, Vision+Text)

Routing Strategy	Vision MSE	Text CE	Params
Separated FFN (hard-coded, ours)	0.0388	2.2026	10.1M
Soft MoE (Top-1 Gating)	0.0408	2.6232	10.1M
Shared FFN (no routing)	0.0370	2.5434	9.0M

Hard-coded routing outperforms learnable routing: Soft MoE (Switch Transformer-style Top-1 gating with load balancing) does not outperform hard-coded modality separation on Text CE. The router must learn what modality-based separation provides structurally for free.

Model Config

python

CONFIG = {
    "d_model": 256,
    "n_heads": 4,
    "ffn_dim": 512,
    "n_layers": 6,
    "vocab_size": 10000,
    "patch_size": 16,
    "max_seq_len": 512,
    "dropout": 0.1,
    "audio_feat_dim": 768,
    "audio_max_tokens": 200,
}

Training Details

GPU: NVIDIA RTX 5090 32GB (~1.5GB VRAM used)
Optimizer: AdamW (lr=3e-4, weight_decay=0.01)
Scheduler: Cosine Annealing (eta_min=1e-6)
Precision: BFloat16 mixed precision
Data: Moving MNIST (10K) + LibriSpeech-100 WavJEPA features (28.5K) + TinyStories (428M tokens)
Training time: ~67 minutes (50 epochs)

Files

best.pt — Best checkpoint (epoch 44, total loss 2.2421)
model.py — Model architecture (TriModalTransformerBlock)
data.py — Dataset loaders
train.py — Training loop
training_log.json — Full training log
config.json — Hyperparameters

Paper

arXiv preprint: Hard-coded Modality Routing in Shared-Attention Transformers: A Toy-Scale Empirical Study (forthcoming)

License

MIT

2264K/trimodal-hardcoded-moe-toy