Triangulating Evidence for Machine Consciousness Claims: A Validity-Centered Stack of Behavioral Batteries, Mechanistic Indicators, Perturbation Tests, and Credence Reporting

Abstract

Frontier AI systems are now producing responses that make users, developers, and policymakers genuinely pause and ask: could these models have conscious experiences? Yet the field still lacks rigorous, hard-to-game tools that can distinguish genuine indicators from optimized artifacts or surface-level cues. We introduce the Triangulated Consciousness Assessment Stack (TCAS), a validity-centered framework that combines four evidence streams: behavioral batteries with robustness controls (B), mechanistic indicators with explicit assumptions (M), perturbation tests that probe causal sensitivity and proxy failures (P), and observer-confound controls that separate anthropomorphic attribution from evidence (O). When all streams are available, TCAS produces theoryindexed credence bands and standardized disclosure cards (TCAS Cards) rather than binary detection verdicts. We report an empirical evaluation of GPT-5.2 Pro via OpenRouter (2026-02-19 UTC) covering B and P streams only, including a pre-specified role-play negative control. M and O were not run in this black-box walkthrough, so theory-indexed credence bands are explicitly withheld under the missing-stream rule. Prompts, rubric, judge prompt, raw outputs, and provenance manifest are released at the repository commit cited in the camera-ready build.