“When Are Two Networks the Same? Tensor Similarity for Mechanistic Interpretability” by Logan Riggs, tdooms, Conflux, lwroe, MLNissenGonzalez

We've found a method that tells you: How functionally similar two neural networks are across ALL inputs,Computed solely from the weights (i.e. no data),Using a principled generalization of cosine similarity. There's only one catch: you have to use a tensor network. We've already shown that tensor-transformer variants are performant (this isn't a novel claim, see these papers for MLPs and Attention), so here we're focusing on the interpretability advances. Linear Algebra Applies to Tensors A tensor network is just a specific decomposition of a tensor, d a tensor is just a generalization of a matrix. This means we can apply tools from linear algebra to our entire network in a principled way. In our paper, we focus on a generalization of cosine similarity we call tensor similarity. The most direct result is: The expected inner product of the activations of two multilinear models under a Gaussian input (i.e. functional similarity on gaussian inputs) is equal to their weight-space inner product (ie tensor similarity) Let's look at our baselines: Matrix Similarity: Cosine similarity on each individual set of weights. This measure is sensitive to symmetries (e.g. permutations/rescalings) that leave the model functionally the same.Behavioral [...] ---Outline:(00:47) Linear Algebra Applies to Tensors(02:29) Backdoor Detection(05:17) But wait?(06:22) Catastrophic Forgetting(07:55) Modular Arithmetic(09:00) 2. Layer Attention(09:44) Conclusion The original text contained 4 footnotes which were omitted from this narration. --- First published: May 29th, 2026 Source: https://www.lesswrong.com/posts/Yzw6KDQc336CpHmGi/when-are-two-networks-the-same-tensor-similarity-for --- Narrated by TYPE III AUDIO. ---Images from the article:Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.