arXiv:2606.00909v1 Announce Type: new Abstract: This work presents MLLM-Microscope, a novel system designed for analyzing the hidden representations within Multimodal Large Language Models (MLLMs). Our system evaluates the linearity, intrinsic dimension, and anisotropy of multimodal token embeddings across transformer layers. Utilizing the ScienceQA dataset, we evaluate two state-of-the-art MLLMs, LLaVA-NeXT and OmniFusion. We find that both the main and residual streams for tokens of both modalities exhibit highly linear behaviors across transformer layers. However, LLaVA-NeXT's image tokens
This work introduces a novel tool, MLLM-Microscope, enabling deeper understanding of foundational MLLMs at a critical juncture in AI development as these models become more complex and widespread.
Understanding the internal mechanics of MLLMs is crucial for improving their reliability, robustness, and interpretability, which are key bottlenecks for broader enterprise adoption and safety.
This research provides new methodologies and initial findings on how multimodal information is processed within large language models, potentially guiding future architectural designs and training strategies.
- · AI researchers
- · MLLM developers
- · AI safety and interpretability firms
- · Black-box AI approaches
- · AI developers ignoring interpretability
More sophisticated tools for analyzing MLLM internal states will emerge, accelerating model understanding.
Improved MLLM interpretability could lead to more robust and trustworthy AI applications across various sectors.
Deeper insight into MLLM representations might inform the design of truly general artificial intelligence by uncovering latent cognitive structures.
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Read at arXiv cs.CL