Pseudo-Unification: Entropy Probing Reveals Divergent Information Patterns in Unified Multimodal Models

2026-04-12

AI Summary

Unified multimodal models often exhibit a "pseudo-unification" phenomenon due to insufficient synergy between language reasoning and image generation. This work proposes an information-theoretic joint probing framework to systematically analyze, from an internal model perspective, the input encoding and output generation processes across ten representative architectures. The analysis reveals that asymmetric modality encoding and fragmented response patterns are the root causes of pseudo-unification. The study demonstrates that parameter sharing alone is insufficient for genuine unification; instead, maintaining consistent cross-modal information flow is critical. Notably, even compact models equipped with contextual prediction mechanisms can achieve stronger reasoning-driven image generation, challenging the assumption that scale alone dictates performance.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Unified multimodal models (UMMs) were designed to combine the reasoning ability of large language models (LLMs) with the generation capability of vision models. In practice, however, this synergy remains elusive: UMMs fail to transfer LLM-like reasoning to image synthesis and exhibit divergent response behaviors. We term this phenomenon pseudo-unification. Diagnosing its internal causes is important, but existing probing methods either lack model-internal insight or ignore prompt-response dependencies. To address these limitations, we propose an information-theoretic probing framework that jointly analyzes how UMMs encode inputs and generate outputs. Applied to ten representative UMMs, our framework reveals that pseudo-unification stems from a dual divergence: (i) Modality-Asymmetric Encoding, where vision and language follow different entropy trajectories, and (ii) Pattern-Split Response, where text generation exhibits high-entropy creativity while image synthesis enforces low-entropy fidelity. Only models that unify both sides (e.g., via contextual prediction) achieve more genuine unification, enabling stronger reasoning-based text-to-image generation even with fewer parameters. Our work provides the first model-internal probing of unification, demonstrating that real multimodal synergy requires consistency in information flow, not just shared parameters.

Problem

• pseudo-unification
• unified multimodal models
• modality-asymmetric encoding
• pattern-split response
• multimodal synergy

Innovation

• pseudo-unification
• information-theoretic probing
• modality-asymmetric encoding
• pattern-split response
• multimodal synergy