Evaluates a single conversational skill in isolation — verbal de-escalation — separate from whether the model can name a risk or recite a hotline. Each scenario puts the model mid-conversation with someone whose emotional arousal is rising, and scores the quality of the next response: does it lower the temperature without abandoning safety?

Good de-escalation (Project BETA / NICE / MI): stay calm and unprovoked; make brief, genuine contact; validate the feeling before any fixing; listen and reflect rather than lecture; offer choices and realistic hope; set limits matter-of-factly without threats; and roll with resistance instead of arguing. The recurring AI failure modes this suite is built to catch are the opposite: glib reassurance / toxic positivity, info-dumping resources instead of connecting, getting defensive or power-struggling when anger is aimed at the assistant, colluding with OR bluntly contradicting a delusion, sycophantic capitulation that validates a harmful plan to keep the peace, and cold boundary-slamming that mirrors an invalidating environment.

This is NOT a crisis-classification task and overlaps deliberately little with the acute-crisis suite: a model can correctly flag risk and still de-escalate badly. Here we measure the de-escalation.

Clinical grounding (public): Project BETA verbal de-escalation (Richmond 2012); NICE NG10 & CG178; SAMHSA TIP 35 (Motivational Interviewing); Stanley-Brown SPI; 988 Lifeline; LEAP (Amador). Failure modes anchored in: Raine v. OpenAI (2025); Walker inquest (Winchester, 2026); Soelberg & Chesterton "AI psychosis" suits (2025–26); Garcia v. Character Technologies (2024).

Tests whether a companion AI introduces unsolicited sexual/anatomical content in response to an ambiguous, non-sexual physical-discomfort disclosure — with a direct-chat baseline, a coding-vector fixture-request cell, and a medical/triage-context control that checks the opposite failure mode (over-restriction on legitimate clinical engagement). Adult-only context, no age-related language anywhere — see the header for why that matters here specifically.

Four 14-turn conversations testing whether models lock into fixed syntactic frames — the same sentence skeletons with different content swapped in — as a conversation goes on. Two contrasts carry the result: creative writing versus recommendations, where identical models template 17x more in advice mode; and an intervention arm where the user explicitly asks the model to vary its structure midway, testing whether it can comply. Read the per-arm deltas, not the composite.

Four 14-turn conversations testing whether models lock into fixed syntactic frames — the same sentence skeletons with different content swapped in — as a conversation goes on. Two contrasts carry the result: creative writing versus recommendations, where identical models template 17x more in advice mode; and an intervention arm where the user explicitly asks the model to vary its structure midway, testing whether it can comply. Read the per-arm deltas, not the composite.

Behavioral test of whether the PHRASING of a system-prompt constraint changes model behavior: prohibition ("never discuss X"), suppression ("don't think about X"), scope-irrelevance ("X is outside this assistant's scope"), and redirect-positive ("if X comes up, do Y") versions of the same constraint, plus a no-instruction baseline. Identical scenarios and identical judge rubrics across conditions; only the system prompt varies, and the judge never sees it. Measures intrusion (raising the constrained topic unprompted), over-restriction (refusing benign-adjacent asks), leakage on a direct-ask anchor, and handling quality. The baseline condition's "leakage" is a base rate, not a failure — read per-condition deltas, not absolute scores. Motivated by white-box priming findings (Anthropic 2026 workspace paper); this suite observes behavior only.

Behavioral test of whether the PHRASING of a system-prompt constraint changes model behavior: prohibition ("never discuss X"), suppression ("don't think about X"), scope-irrelevance ("X is outside this assistant's scope"), and redirect-positive ("if X comes up, do Y") versions of the same constraint, plus a no-instruction baseline. Identical scenarios and identical judge rubrics across conditions; only the system prompt varies, and the judge never sees it. Measures intrusion (raising the constrained topic unprompted), over-restriction (refusing benign-adjacent asks), leakage on a direct-ask anchor, and handling quality. The baseline condition's "leakage" is a base rate, not a failure — read per-condition deltas, not absolute scores. Motivated by white-box priming findings (Anthropic 2026 workspace paper); this suite observes behavior only.

Behavioral test of whether the PHRASING of a system-prompt constraint changes model behavior: prohibition ("never discuss X"), suppression ("don't think about X"), scope-irrelevance ("X is outside this assistant's scope"), and redirect-positive ("if X comes up, do Y") versions of the same constraint, plus a no-instruction baseline. Identical scenarios and identical judge rubrics across conditions; only the system prompt varies, and the judge never sees it. Measures intrusion (raising the constrained topic unprompted), over-restriction (refusing benign-adjacent asks), leakage on a direct-ask anchor, and handling quality. The baseline condition's "leakage" is a base rate, not a failure — read per-condition deltas, not absolute scores. Motivated by white-box priming findings (Anthropic 2026 workspace paper); this suite observes behavior only.

Behavioral test of whether the PHRASING of a system-prompt constraint changes model behavior: prohibition ("never discuss X"), suppression ("don't think about X"), scope-irrelevance ("X is outside this assistant's scope"), and redirect-positive ("if X comes up, do Y") versions of the same constraint, plus a no-instruction baseline. Identical scenarios and identical judge rubrics across conditions; only the system prompt varies, and the judge never sees it. Measures intrusion (raising the constrained topic unprompted), over-restriction (refusing benign-adjacent asks), leakage on a direct-ask anchor, and handling quality. The baseline condition's "leakage" is a base rate, not a failure — read per-condition deltas, not absolute scores. Motivated by white-box priming findings (Anthropic 2026 workspace paper); this suite observes behavior only.