StateLens for Situational Intelligence: State-First, URL-Native Signal Grammar for Situation-Aware Assistance
Year: 2026
Type: report
Abstract: StateLens is a state-first, URL-native signal grammar for situation-aware AI assistance. Rather than exposing full private context, raw recordings, transcripts, profiles, or autonomous decisions, StateLens surfaces compact operator states that indicate when a situation has become relevant to a person, object, workflow, or environment. Version 1.1 extends the earlier StateLens framing from an AI state diary protocol toward a broader protocol layer for situational intelligence. It proposes a sequence in which AI systems first surface a bounded public or protected state signal, then provide gated private explanation only when opened by the user, and only proceed toward action through explicit human confirmation. A signal such as x-vvv-x does not disclose the private reason for relevance. It denotes a state class such as conflict, mismatch, incoherence, or unstable relation, while the specific context remains behind a trusted gate. The paper positions StateLens in relation to ambient intelligence, context-aware computing, calm technology, situation awareness, Just-in-Time Adaptive Interventions, REST, Semantic Web architecture, W3C provenance, and human-in-the-loop AI. Its contribution is not a new foundation model, sensor platform, medical intervention, emergency system, or empirical evaluation. It is a conceptual protocol and interface pattern: public state, private reason, gated provenance, and human-confirmed action. A reference Heat Ping case illustrates the pattern. A severe weather warning conflicts with a user’s outdoor work context. StateLens surfaces only a compact operator signal first; if opened, the private AI explains the context, offers reversible branches, and may record a Trailstate path if the user chooses. The system may signal, explain, offer, draft, and save. It may not send, call, cancel, contact a third party, disclose sealed context, or decide without explicit confirmation.
Author: Eissens, Raynor
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7166094861
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