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cs.LG2025
Atlas-Alignment: Making Interpretability Transferable Across Language Models
Bruno Puri, Jim Berend, Sebastian Lapuschkin +1
Interpretability is crucial for building safe, reliable, and controllable language models, yet existing interpretability pipelines remain costly and difficult to scale. Interpretin…
cs.LG2025
Circuit Insights: Towards Interpretability Beyond Activations
Elena Golimblevskaia, Aakriti Jain, Bruno Puri +3
The fields of explainable AI and mechanistic interpretability aim to uncover the internal structure of neural networks, with circuit discovery as a central tool for understanding m…
cs.LG2025
FADE: Why Bad Descriptions Happen to Good Features
Bruno Puri, Aakriti Jain, Elena Golimblevskaia +4
Recent advances in mechanistic interpretability have highlighted the potential of automating interpretability pipelines in analyzing the latent representations within LLMs. While t…