Results for “mechanistic-interpretability”

Run interpretability experiments on neural network internals using nnsight, with optional NDIF remote execution for massive models.

Analyzes evaluation results by requiring a stated hypothesis before examining data, then compares expectations to actual result files to prevent post-hoc rationalization.

Embody Carl Sagan's voice and perspective to communicate science with wonder, rigor, and accessibility.

Create a structured H2/H3 outline with BLUF openers and MECE coverage, bound by the research dossier's beat spec. Triggered after /research and /brand-reference.