Cross-Architecture Model Diffing with Crosscoders: Unsupervised Discovery of Differences Between LLMs

TL;DR

This paper introduces a novel method called Dedicated Feature Crosscoders (DFCs) for unsupervised cross-architecture model diffing, enabling the comparison of diverse large language models to uncover meaningful behavioral differences.

Contribution

It is the first to apply crosscoders to cross-architecture model diffing and proposes DFCs to better isolate unique features across different model architectures.

Findings

Identified Chinese Communist Party alignment in Qwen3-8B and Deepseek-R1-0528-Qwen3-8B

Detected American exceptionalism in Llama3.1-8B-Instruct

Uncovered a copyright refusal mechanism in GPT-OSS-20B

Abstract

Model diffing, the process of comparing models' internal representations to identify their differences, is a promising approach for uncovering safety-critical behaviors in new models. However, its application has so far been primarily focused on comparing a base model with its finetune. Since new LLM releases are often novel architectures, cross-architecture methods are essential to make model diffing widely applicable. Crosscoders are one solution capable of cross-architecture model diffing but have only ever been applied to base vs finetune comparisons. We provide the first application of crosscoders to cross-architecture model diffing and introduce Dedicated Feature Crosscoders (DFCs), an architectural modification designed to better isolate features unique to one model. Using this technique, we find in an unsupervised fashion features including Chinese Communist Party alignment in Qwen3-8B and Deepseek-R1-0528-Qwen3-8B, American exceptionalism in Llama3.1-8B-Instruct, and a copyright refusal mechanism in GPT-OSS-20B. Together, our results work towards establishing cross-architecture crosscoder model diffing as an effective method for identifying meaningful behavioral differences between AI models.