mlsanityData to 7 Oct 2026

MLLM-Microscope: Unlocking Hidden Structure Within Multimodal Large Language Models

arXiv 2606.00909 ↗First posted 30 May 2026cs.CL cs.AIcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

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 reveal a slight decline in linearity, whereas OmniFusion's remain consistent. Image token dimensions in OmniFusion remain consistently higher across layers compared to LLaVA-NeXT. Also, the OmniFusion's anisotropy is observed to stay consistently low throughout the layers. These findings suggest that the inner workings of MLLMs highly depend on the nature of modality fusion performed before passing the token sequence into LLM. This and other new potential insights obtainable from our system are surely capable of enhancing our understanding of the inner workings of MLLMs, informing future model design and optimization.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 6 of its 13 references that are in the map. First posted on arXiv 30 May 2026.

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6 of 13 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Improved Baselines with Visual Instruction Tuning5 Oct

Other references · 5

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,881
  3. 2019How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddingscited by 770
  4. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 579
  5. 2018Estimating the intrinsic dimension of datasets by a minimal neighborhood informationcited by 246

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  1. 2025DeepInsert: Early Layer Bypass for Efficient and Performant Multimodal Understanding
  2. 2025Multimodal Language Models See Better When They Look Shallower
  3. 2025Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation
  4. 2026Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs
  5. 2024Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey
  6. 2024A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
  7. 2024The Revolution of Multimodal Large Language Models: A Survey
  8. 2026A More Word-like Image Tokenization for MLLMs