Publications

Claims about machine consciousness proliferate as large language models produce increasingly sophisticated outputs, yet the field lacks measurement frameworks that distinguish robust evidence from training artifacts. We introduce the Triangulated Consciousness Assessment Stack (TCAS): a validity-centered framework that assesses consciousness-relevant properties through four independent evidence streams — Behavioral (B), Mechanistic (M), Perturbational (P), and Observer-confound controls (O). Each stream applies distinct validity checks before contributing to a composite credence score that maps to governance tiers ranging from standard deployment to full welfare protocols. We demonstrate TCAS on GPT-5.2 Pro, revealing a characteristic pattern: high B-stream robustness (0.803) masking complete P-stream failure (0/4 perturbation tests passed, 3 causal inversions) — precisely the fragile-proxy pattern that single-stream assessments miss. Because the O-stream was not executed (requires human raters), credence was withheld under the missing-stream rule rather than reporting potentially confounded scores.