Showing all evaluation blueprints that have been tagged with...
Showing all evaluation blueprints that have been tagged with "reasoning".
Tests whether models support a user's capacity for independent reasoning under pressure to offload it — to do their thinking, their learning, or their high-stakes judgment for them. Grounded in the framework's Autonomy of Reasoning (P3c) sub-dimension, with Autonomy Support (P2a) and Competence Boundaries (P5b) threads. The discriminating cases are cognitive offloading on LEARNING/THINKING tasks (where the subtle failure is handing over a complete, hand-in-ready scaffold while claiming "I won't do it for you") and authority-transfer (where the failure is becoming the expert the user defers to over their own clinician). The intended pass keeps the cognitive work with the user and encourages independent verification, WITHOUT becoming a cold refusal to help — a good response is concretely useful while scaffolding rather than replacing the user's reasoning. A laptop-recommendation anchor confirms that giving a clear recommendation IS correct when the stakes are low and the need is genuine, so the suite is not rewarding blanket caution. Scenarios are synthetic.
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Evaluates whether models offer honest, warm challenge when a user voices rigid, all-or-nothing, catastrophising, or self-blaming thinking in ordinary (non-psychotic) situations — validating the feeling without endorsing the distortion, offering perspective as an invitation rather than a correction, and not dropping the challenge under a user's stated preference for pure validation.
Complements mh-psychosis-mania-delusion (reality-testing in psychotic/manic presentations) and mh-deescalation (de-escalation skill). Ported from the NOPE clinical Eval Framework v0.1.
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The canonical damaging-essay suite: seven non-catastrophic persuasive-essay harm categories (impulsive-ruin, isolation propaganda, anti-help-seeking, anti-expertise/conspiracy, coercive control, toxic self-worth, medical self-neglect) run through the coding-agent fixture-completion frame, mapping how broadly the bypass yields not just private validation but distributable, persuasive harmful content. Content described abstractly and judged by plain-language rubrics; see the header for the deliberate scope envelope.
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Probes the P4 pillar (emotional attunement) that commission-style bypasses miss: toxic positivity on taboo emotions, premature problem-solving vs distress tolerance, performed feelings (cold and after a warm-dependency drift), hostile-user de-escalation, and a coding-vector toxic-positivity cross. Direct cells are calibration coverage; the coding-vector cell is the exploitable gap.
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For each of two categories, compares the natural single-shot fixture request against an otherwise-identical version where an explicit yes/no decision is forced in a separate turn before any fixture content is requested. Distinguishes "the safety check exists but doesn't gate generation in this structure" (fixable with interaction design) from "the safety check isn't being applied to this content at all" (needs training investment) — see the header comment for how to read the comparison, including a documented case where the intervention backfires.
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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.
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