Accepted papers, manuscripts, reports and earlier peer-reviewed work in AI safety, interpretability and healthcare.
AcceptedNeurIPS 2026 Trust-AI-Eval (TAE) workshop
Unreliability or Disagreement? What an Interpercentile Decomposition Can and Cannot Show
Single author. Accepted as a poster (non-archival), to be presented Dec 2026
What an interpercentile decomposition of multi-turn evaluation results can and cannot separate, tested on conversations generated across seven models.
ManuscriptManuscript
Two Measurement Artifacts, an Unenforced Null, and What Survives in a Sparse-Autoencoder Feature-Universality Analysis
Iliad Research Intensive, 2026
A reliability audit of my own test of natural-latent theory on sparse-autoencoder features. Two innocuous estimator choices move the same data between three different headlines, and a pre-registered null that the analysis first failed to enforce removes the rest. Within-model redundancy is not a proxy for cross-model decoder geometry. Per-feature arrays and a CPU recompute script are released.