[P] Visualizing token-level activity in a transformer

Reddit r/MachineLearning / 3/18/2026

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Key Points

The author is experimenting with a 3D visualization of LLM inference where nodes represent components such as attention layers, FFN, and KV cache.
As tokens are generated, activation paths animate across a network, and node intensity reflects activity to illustrate information flow.
The goal is to make the inference process feel more intuitive, but there are concerns about how accurate or useful this abstraction is.
The post invites feedback on whether this visualization helps build intuition or oversimplifies what’s actually happening.
The topic touches on model interpretability and visualization tooling for transformers, with potential implications for researchers and engineers communicating complex internals.

I’ve been experimenting with a 3D visualization of LLM inference where nodes represent components like attention layers, FFN, KV cache, etc.

As tokens are generated, activation paths animate across a network (kind of like lightning chains), and node intensity reflects activity.

The goal is to make the inference process feel more intuitive, but I’m not sure how accurate/useful this abstraction is.

Curious what people here think — does this kind of visualization help build intuition, or does it oversimplify what’s actually happening?

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