LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

LatentLens is a novel interpretability method designed to reveal highly interpretable visual tokens within Large Language Models (LLMs) adapted for vision-language tasks (VLMs). Unlike conventional methods like LogitLens and EmbeddingLens, LatentLens compares visual token representations to contextualized text representations from a large corpus. This approach demonstrates that 72% of visual tokens are interpretable across 10 different VLM configurations and all layers, significantly outperforming LogitLens (23%) and EmbeddingLens (30%). The study also identifies a "Mid-Layer Leap," where early visual tokens align more strongly with semantic representations from middle LLM layers (e.g., layers 8-16) rather than input-level lexical representations. This challenges prior assumptions about visual token interpretability and suggests a deeper alignment between vision and language representations.

Key takeaway

For AI Scientists and Machine Learning Engineers working on VLM interpretability, you should adopt LatentLens to gain a more accurate understanding of visual token representations. This method provides consistently higher interpretability scores across all layers and models, revealing semantic alignment previously underestimated by LogitLens or EmbeddingLens. Consider integrating contextual embedding comparisons into your analysis workflows to uncover deeper insights into how LLMs process multimodal inputs and to potentially mitigate issues like hallucination.

Key insights

LatentLens reveals visual tokens are highly interpretable by comparing them to contextualized text embeddings, not just static vocabulary.

Principles

• Contextual embeddings enhance VLM interpretability.
• Visual tokens align with semantic text representations.
• LLMs process visual inputs with minimal transformation.

Method

LatentLens encodes a large text corpus, storing contextualized token representations. Visual token representations are then compared via cosine similarity to these stored representations, with top-k nearest neighbors serving as descriptions.

In practice

• Apply LatentLens to analyze VLM internal states.
• Use contextual embeddings for cross-modal alignment.
• Explore dynamic corpus generation for richer descriptions.

Topics

• VLM Interpretability
• LatentLens
• Contextual Embeddings
• Vision-Language Models
• LLM Representations
• Mid-Layer Leap

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.