Berkeley SPEX: GPT-4o mini fails 92% of trolley problems — replacing 4 words reduces failure to near zero

Researchers at UC Berkeley (Landon Butler, Justin Singh Kang, Yigit Efe Erginbas, Abhineet Agarwal, Bin Yu, Kannan Ramchandran) published SPEX on March 13, 2026 — a signal-processing + coding-theory approach that scales LLM feature-interaction discovery from dozens to thousands of components. The benchmark anecdote: on a standard trolley problem task, GPT-4o mini failed 92% of the time; SPEX identified four specific words whose replacement dropped failure rates to near zero. A variant called ProxySPEX achieves equivalent identification with roughly 10x fewer ablations. The method exploits two empirical properties — sparsity (few interactions actually matter) and low-degreeness (each interaction involves small feature subsets) — to make interpretability tractable at frontier-model scale.