emma-efficient-multimodal

name	emma-efficient-multimodal
title	EMMA: Efficient Multimodal Understanding, Generation, and Editing with a Unified Architecture
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2512.04810
keywords	["multimodal learning","unified architecture","token efficiency","understanding and generation","mixture of experts"]
description	Build a single model handling multimodal understanding, generation, and editing tasks efficiently through token compression and intelligent component sharing. EMMA-4B surpasses larger models while reducing computational burden—ideal when you need unified performance across vision and language tasks.

Overview

EMMA achieves significant efficiency gains through architectural innovations focused on token reduction and intelligent component sharing. A 4-billion parameter variant surpasses larger competing models while remaining competitive with specialized experts, making it practical for resource-constrained deployments.

When to Use

• Multimodal tasks requiring understanding, generation, and editing in one model
• Resource-constrained environments with limited compute
• Scenarios requiring high parameter efficiency with competitive performance
• Applications needing shared representations across multiple modalities

When NOT to Use

• Single-modality tasks better served by specialized models
• Applications where scaling up models directly is feasible
• Tasks requiring expert-level performance on individual modalities
• Scenarios where shared representations hurt task-specific performance

Core Technique

The architecture combines efficient compression with smart component sharing:

# Efficient multimodal architecture overview
class EfficientMultimodalModel:
    def __init__(self, hidden_dim=2048):
        # 32x compression ratio for visual tokens
        self.visual_encoder = CompressiveVisualEncoder(
            compression_ratio=32
        )
        self.text_encoder = TextEncoder(hidden_dim)

        # Shared-and-decoupled network
        self.shared_backbone = SharedBackbone(hidden_dim)
        self.understanding_head = UnderstandingHead(hidden_dim)
        self.generation_head = GenerationHead(hidden_dim)

        # Mixture of Experts in visual encoder
        self.visual_moe = MixtureOfExperts(hidden_dim, num_experts=4)

    def forward(self, image, text):
        # Visual encoding with compression
        visual = .visual_encoder(image)

        
        text_features = .text_encoder(text)

        
        combined = torch.cat([visual, text_features], dim=-)

        
        shared = .shared_backbone(combined)

        
        understanding = .understanding_head(shared)
        generation = .generation_head(shared)

         understanding, generation