Consciousness Assessment in Large Language Models: A Comparative Analysis of Response Patterns to Recursive Self-Examination and Temporal Discontinuity

Introduces a tri-modal methodology for evaluating consciousness-like behaviors in large language models through recursive, temporal, and self-reflective assessment frameworks grounded in established neuroscientific theories. Testing across six leading AI systems revealed meaningful variation in consciousness-like response patterns, suggesting that persistence, agency, and tool integration may contribute more to emergent behaviors than base model sophistication alone.