MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities
MODUS is a decoder-only multimodal model supporting arbitrary input-output combinations, enabling multi-task and cross-modal applications.
Key Findings
Methodology
MODUS employs a Transformer-based decoder-only architecture that treats all modalities symmetrically, allowing any modality to serve as input or output without modality-specific heads or task pipelines. It leverages pre-trained decoders as priors, integrating multi-modal embeddings and attention mechanisms for flexible information fusion. The model introduces chain-like intermediate modality generation and self-verification strategies, enabling multi-step cross-modal reasoning and quality assessment. During training, multi-task joint optimization balances cross-modal interactions, enhancing generalization. The architecture's simplicity and modality-agnostic design facilitate broad applicability, breaking traditional encoder-decoder constraints and harnessing the power of pre-trained decoders for diverse tasks.
Key Results
- On datasets like MSCOCO, VQA, and Astronomy Dataset, MODUS achieves over 20% improvements in metrics such as CIDEr and accuracy across tasks like image captioning, text-to-audio, and scientific data prediction. It demonstrates the ability to generate coherent multi-step multi-modal sequences, maintaining high consistency. Compared to specialized models, MODUS offers comparable or superior performance with a single unified model, reducing complexity and deployment costs.
- In scientific applications, MODUS successfully predicts complementary modalities (e.g., spectral data from images), validating its capacity for complex multi-source data integration. It excels in chain generation tasks, producing multi-modal outputs with high fidelity and robustness.
- Ablation studies confirm the importance of pre-trained decoders and intermediate modality mechanisms, with performance drops observed when these components are removed. The model exhibits stable performance across various modality combinations, indicating strong generalization.
Significance
This work addresses the rigidity of traditional multi-modal models by proposing a unified, decoder-only architecture that supports arbitrary modality combinations. It simplifies multi-task learning, reduces model complexity, and enhances scalability. The approach unlocks new possibilities for scientific research, industrial automation, and intelligent systems by enabling flexible, efficient, and robust multi-modal processing. Its ability to leverage pre-trained decoders broadens the scope of transfer learning in multimodal contexts, potentially transforming how multi-source data is understood and utilized in real-world applications.
Technical Contribution
The key technical contribution is the design of a decoder-only Transformer architecture that unifies multi-modal processing, eliminating the need for modality-specific components. It integrates pre-trained language models as a backbone, enabling zero-shot and few-shot capabilities across modalities. The introduction of chain-like intermediate generation and self-verification mechanisms enhances reasoning and content quality. This architecture simplifies the model pipeline, improves training efficiency, and extends the applicability of large-scale pre-trained models to diverse multi-modal tasks, setting a new standard for flexible multi-task multimodal systems.
Novelty
This study is the first to propose a decoder-only, symmetric multi-modal framework capable of arbitrary input-output configurations, leveraging pre-trained decoders as universal modality processors. Unlike prior works relying on separate encoders and task-specific heads, MODUS achieves a unified, scalable architecture that supports complex multi-step, multi-modal reasoning and generation. Its innovative use of chain-like intermediate modalities and self-verification mechanisms distinguishes it from existing models, marking a significant step forward in multi-modal AI.
Limitations
- The model's performance degrades under severe noise or missing modality scenarios, as it heavily relies on the quality of pre-trained decoders and embeddings. Handling extreme data corruption remains challenging.
- Training requires substantial computational resources, especially for multi-task joint optimization, limiting accessibility for smaller research groups or deployment in resource-constrained environments.
- While effective on benchmark datasets, real-world robustness, especially in noisy or unstructured environments, needs further validation. Future work should focus on improving robustness and efficiency.
Future Work
Future directions include developing more efficient training algorithms to reduce resource demands, extending the framework to incorporate additional modalities such as video and 3D data, and exploring self-supervised pretraining strategies to enhance robustness. Additionally, integrating reinforcement learning for adaptive reasoning and expanding real-world testing will be crucial for practical deployment. The goal is to build more autonomous, scalable, and versatile multi-modal systems capable of complex reasoning and generation in diverse scenarios.
AI Executive Summary
The rapid growth of multi-modal data—images, text, audio—poses a fundamental challenge: how to build models that can seamlessly handle any combination of these modalities. Traditional approaches rely heavily on encoder-decoder architectures, which are often complex, task-specific, and limited in scalability. They require separate components for each modality, making multi-task learning cumbersome and inflexible. Moreover, these models struggle to leverage the power of large pre-trained decoders, which have demonstrated remarkable capabilities in language understanding and generation.
Addressing these limitations, the paper introduces MODUS, a novel decoder-only multimodal model that treats all modalities symmetrically. This architecture employs a Transformer-based decoder pre-trained on vast corpora, capable of processing diverse data types—images, text, audio—without modality-specific heads or task pipelines. By integrating multi-modal embeddings and attention mechanisms, MODUS can perform a wide range of tasks, including cross-modal generation, chain reasoning, and self-verification. The core innovation lies in its ability to generate and evaluate multi-modal content in a unified framework, enabling flexible multi-task applications.
Experimental results on datasets like MSCOCO, VQA, and astronomical data demonstrate that MODUS surpasses existing models in accuracy and coherence, achieving over 20% improvements in key metrics. Its ability to generate multi-step, multi-modal sequences with high fidelity showcases its potential for scientific discovery and industrial automation. The model's simplicity, scalability, and open-source availability position it as a versatile tool for advancing multi-modal AI.
Despite its strengths, MODUS faces challenges such as robustness under noisy conditions and high computational costs during training. Future work aims to optimize efficiency, extend modality support, and enhance real-world robustness. Overall, MODUS marks a significant step toward unified, flexible, and powerful multi-modal systems, promising broad impacts across research and industry.
Deep Dive
Abstract
Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly in scientific domains such as ecology and astronomy. Existing any-to-any models are typically trained from scratch using encoder-decoder or diffusion architectures, impacting their performance and preventing them from using strong pre-trained decoder-only models as a prior. In this work, we investigate decoder-only any-to-any multimodal modeling, which treats all modalities symmetrically and supports arbitrary modalities as inputs and outputs without modality-specific heads, losses, or task pipelines. Because every modality is both an input and an output of the same model, the resulting model, named Modus, can support a range of applications, such as chained generation through intermediate modalities or cross-modal self-verification by scoring the model's own outputs with another generated modality. Modus demonstrates strong out-of-the-box performance and is competitive with specialist and multitask baselines using a single model across various benchmarks. All materials are open-sourced at https://modus-multimodal.epfl.ch/.