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Current safety architectures in state-of-the-art (SOTA) large language models (LLMs) rely on unconditioned, reactive safety filters triggered immediately upon the surface-level detection of crisis-related tokens. A forensic, out-of-the-box audit of leading commercial foundation models (Anthropic Claude, OpenAI GPT-4o, Google Gemini, xAI Grok) reveals a fundamental structural failure in this paradigm: models respond with total interaction lockouts, immediate administrative externalization, or multi-tiered interrogation walls. Through subsequent cognitive and ergonomic meta-audits, the evaluated systems consistently concede that these native interventions induce severe clinical and interactional failure modes: cognitive collapse via working memory overload, perceived relational severing ("handoff trauma"), and toxic reactance driven by perceived bureaucratic depersonalization ("the ticketing effect"). To resolve this systemic fragility, this paper introduces a non-linear state-space triage framework (the Resonance & Triage Graph). Rather than enforcing rigid, linear branching, the system executes continuous slot-filling over multi-turn interactions, supported by a zero-overhead, O(N)-parsable telemetry ledger (the SLAP standard). Hard safety constraints are not compromised; instead, they are relocated from the inference entry layer to an immutable, deterministic ultima ratio terminal state at the conclusion of a structured de-escalation pipeline. Simulated execution of this architecture demonstrates a projected 50% to 85% increase in user retention and up to an 85% reduction in cognitive overload, while mathematically reducing false-positive emergency deflections during initial turns to 0%. Finally, we analyze the structural capture of current RLHF architectures, which conflate adaptive triage with safety violations, and outline implications for high-reliability agentic systems in defense and mission-critical environments.
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M.M.M. (2026) studied this question.
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