Revisiting Classic Thought Experiments to Measure Consciousness for AI Safety

A new study on arXiv takes a fresh look at Leibniz's mill, Turing's imitation game, and Searle's Chinese Room using a framework called Conservation-Congruent Encoding (CCE). This research introduces a basic symbolic setting where task performance (W_causal,T) reflects successful actions, while operational consciousness (κ_T) measures how well the internal setup supports these actions. Interestingly, both a simple lookup method and a compact generative approach can yield similar results, but they vary greatly in κ_T; the first relies on a growing database of unused mappings, whereas the second uses a streamlined internal structure. This work redefines key discussions about understanding by separating outward behavior from internal organization, which is essential for future AI safety assessments. The paper is listed under Computer Science > Artificial Intelligence as arXiv: 2608.00001. It aims to provide a formal way to gauge AI consciousness, potentially improving safety protocols, and includes resources like references and code. Although it doesn't offer concrete conclusions, it proposes an important distinction for evaluating AI internal states.