Seonglae ChoThe architecture has separate for each layer while sharing only the latent dictionary for scaling, where : source layer-specific encoder, : target layer-specific decoder, : reconstructed layer activation from source latent.
Each input-output layer pair has its own encoder-decoder weight pair. Unlike cross-layer transcoders (Circuit Tracing), encoders are not shared; instead, layers share the same latent space, achieved through loss-based approximation.
This is solved through alignment via co-training, where an alignment loss is added during training to force the latents to match, causing them to converge to a shared latent space.
The original purpose is cross-layer mapping and feature alignment, but it can be used for various purposes such as model diffing and scaling transfer
Decoder sparsity loss
Crosscoders
CrossCoder (2024)
with Cross fine-tuning model & scaling transferability by diffing within same architecture
Sparse Crosscoders for Cross-Layer Features and Model Diffing
This note will cover some theoretical examples motivating crosscoders, and then present preliminary experiments applying them to cross-layer superposition and model diffing. We also briefly discuss the theory of how crosscoders might simplify circuit analysis, but leave results on this for a future update.
https://transformer-circuits.pub/2024/crosscoders/index.html
Open Source Replication of Anthropic’s Crosscoder paper for model-diffing — LessWrong
Intro Anthropic recently released an exciting mini-paper on crosscoders (Lindsey et al.). In this post, we open source a model-diffing crosscoder tra…
https://www.lesswrong.com/posts/srt6JXsRMtmqAJavD/open-source-replication-of-anthropic-s-crosscoder-paper-for
BatchTopK crosscoder to prevent Complete Shrinkage and Latent Decoupling for chat model
Robustly identifying concepts introduced during chat fine-tuning...
Model diffing is the study of how fine-tuning changes a model's representations and internal algorithms. Many behaviours of interest are introduced during fine-tuning, and model diffing offers a...
https://arxiv.org/abs/2504.02922
Using Crosscoder for chat Model Diffing reveals issues with traditional L1 sparsity approaches: many "chat-specific features" are falsely identified because they are actually existing concepts that shrink to zero in one model during training. Most chat-exclusive latents are training artifacts rather than genuine new capabilities.
Complete Shrinkage → A shared concept where one model's decoder shrinks to zero. Latent Decoupling → The same concept is represented by different latent combinations in two models.
Using Top-K (L0-style) sparsity instead of L1 reduces false positives and retains only alignment-related features. Chat tuning effects are primarily not about capabilities themselves, but rather: safety/refusal mechanisms, dialogue format processing, response length and summarization controls, and template token-based control. In other words, it acts more like a shallow layer that steers existing capabilities.
arxiv.org
https://arxiv.org/pdf/2504.02922