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cs.LG2026
What Accuracy Hides In Learned Transformer Programs
Jonathan Lee, Narmeen Oozeer, Philip Quirke
Transformer Programs (TP) (Friedman et al., 2023) constrain a Transformer's architecture so that its learned computation can be extracted as readable Python code. TP expose learned…
cs.AI2026
Necessary, Decodable and Reversible, Yet Not Transferable: A Stress Test for Attention-Head Role Claims
Philip Quirke
Mechanistic studies often assign a component a role when removing it damages a behavior, its activation linearly encodes task information, and restoring that activation repairs the…
cs.CY2026
Make Mechanistic Interpretability Auditable: A Call to Develop Guidelines via Continuous Collaborative Reviewing
Michael Lan, Narmeen Fatimah Oozeer, Chaithanya Bandi +4
While mechanistic interpretability (MI) has produced important insights into neural network internals, the field has yet to establish a standardized system to audit experiments. As…