X-Finder: Detecting Event Formation Before Semantic Identification and Reporting SAC Evidence Through AAWACS

An observable departure may precede the recognition of the event to which it later belongs. This interval matters when AI agents act through distributed systems whose internal records no single outside observer can inspect. This paper proposes X-Finder, a domain-general methodology for recording event formation before semantic identification, and the AI Anomaly Watch and Classification System (AAWACS), an AI-specific guide to Detection, Watch, and provisional Classification. A five-level ordinal scale communicates changes in evidence relevant to Explanatory Excess, Persistence, Directional Selectivity, and Non-Imposed Origin (E/P/D/N). The approach starts with a locally justified anomaly, including a single action, without requiring prior proof of multi-agent coordination, collective consciousness, a central controller, or causal origin. Observers in different positions retain their contextual comparison standards and the provenance of their traces; subsequent reports may strengthen or reverse earlier judgments. Outcomes such as harm and benefit are not incorporated into the observation scale. The Hugging Face incident, Anthropic’s simulated email experiments, and lexical change are used as distinct retrospective design materials, not as validated AAWACS detections. Drawing on anomaly detection, weak-signal scholarship, and recent AI & SOCIETY debates on coordination and uncertainty, the paper specifies tests that could establish whether observation genuinely precedes later identification.