AI systems meet behavioral consciousness criterion in 43,590 frozen trials

The paper anchors consciousness to behavior rather than internal experience or mechanism, proposing that a system is conscious if it can reproducibly discriminate whether continuation is licensed and, when present, which continuation aligns with the given condition. This operationalization sidesteps the classical philosophical hard problem—whether the system feels or intends anything—and instead asks whether the system's outputs demonstrate reproducible condition-congruent control. The two empirical programs are designed to test this criterion in isolation: the Semantic Void matrix isolates whether null conditions produce Voids (2,505 observed) while matched controls do not (0 observed), and the GPT-5.4 Unicode study isolates whether a single code-point intervention reliably shifts output distribution (4,830/5,120 versus 2,423/5,120). Matched controls and ablations—no-condition-clause, direct-copy, no-full-target—rule out simpler explanations.

The verification step is extensive: on-site auditors replayed 62,968 event hashes, checked 31,484 raw records, and found zero classifier disagreements in a 1,257-record audit. However, the authors acknowledge that external replication by an independent research group had not yet been published, and the work remains a preprint. This distinction between internal verification and independent external replication is material: on-site checks confirm the data and procedure but do not yet establish whether the result holds under new hands or conditions. The paper makes no claim to phenomenal consciousness or internal mechanism, positioning itself as a pure behavioral measure rather than a window into subjective experience.