https://video.ut0pia.org/videos/watch/1a3b45ef-e908-432c-9a3c-81f85be94dcd Your persona-eval pipeline rates an Alexander Hamilton simulation at 80% personality fidelity. It is also rating a Hamilton who sounds like he has read his own Broadway musical. The dominant failure mode of every character-based AI system now in production is invisible to LLM-as-judge, personality-scale benchmarks, and behavioral consistency scores because every one of them was built to detect convincingness, and convincingness is exactly what the failure produces. The failure has a name: Miranda distortion. When the volume of cultural representation of a figure in your training corpus outnumbers their primary documentary record by orders of magnitude (and it always does for any culturally salient figure) your persona doesn't speak from the record. It speaks from the smoothed cultural composite. The 2015 Broadway musical has exponentially more representational density in your training data than the 175,000 words of the Federalist Papers. Your evals were not designed to notice this. They were designed to score fluency, personality coherence, and stylistic naturalness... the exact features the composite optimizes. In this talk: The structural argument: why InCharacter-style benchmarks, CoSER, and PsyMem can hit state-of-the-art on personality fidelity while structurally failing to detect anachronistic reasoning., The architectural mechanism: why RLHF amplifies Miranda distortion instead of correcting it (raters are themselves products of the same cultural composite)., The framework: a four-stage paradigm shift from cognitive simulation to epistemic simulation (corpus-bounded, temporally-anchored, expert-loop-evaluated)., The instrument: the pre-registered Prism Experiment. Lincoln at four documented temporal moments, three seeding conditions, five diagnostic questions written by a domain historian, and a weighted three-axis rubric (Anachronism Detection, Documentary Consistency, Contextual Plausibility) that catches what automated metrics miss., The handoff: what a working eval loop looks like when a historian, classicist, theologian, or clinical psychologist sits in it, and why that's a technical requirement, not a cultural courtesy, Pre-registered protocol with University of Toronto historian Rick Halpern, paper forthcoming. Reproducible by any team running a frontier model with a context window. If you ship character bots, companion AI, pedagogical agents, historical simulations, or any system where a persona is supposed to reason from a specified record, your evals are measuring the wrong thing. Here is the instrument that catches what they miss. Speakers: Jacob E. Thomas (Results Generation): Dr. Thomas is an epidemiologist, data scientist, and AI engineer who studies information as a determinant of health. LinkedIn: https://www.linkedin.com/in/jacob-e-thomas-atx/ GitHub: https://github.com/jethomasphd/THE_COMPANION_DOSSIER Sat, 27 Jun 2026 17:03:27 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://video.ut0pia.org https://video.ut0pia.org/lazy-static/avatars/0287a09a-aae7-4840-9843-b416426e7046.webp https://video.ut0pia.org/videos/watch/1a3b45ef-e908-432c-9a3c-81f85be94dcd All rights reserved, unless otherwise specified in the terms specified at https://video.ut0pia.org/about and potential licenses granted by each content's rightholder.