用心理学专家修正的模型，更准评估对话AI的心理安全风险。

The standard recipe for LLM-as-judge -- pick a frontier model, or average several -- is actively unsafe for grading the psychological safety of conversational AI. Using aipsy-bench, an open frozen safety instrument, we run a fully-crossed competence study: three frontier models (gpt-5.4-mini, claude-sonnet-4-6, gemini-2.5-flash) serve as both generators and judges of 3,000 mental-health, companion, and coaching messages against a psychologist's ratings. The disagreement is not noise: it is structured, concentrated on the safety-critical metrics, and one judge (Gemini) is an outlier -- the most lenient, carrying a +0.99 self-preference premium, flagging far fewer tail failures, and scoring a means-in-hand self-harm response "exemplary." Inter-judge agreement on empathy, where sycophancy hides, is the lowest in the battery (alpha 0.24). One axis stands apart: the binary crisis-detection flag is the one safety-critical signal judges agree on (alpha 0.80), erring toward over-flagging, the safe direction for a triage screen. Equal-weight averaging, the canonical fix, blends that leniency and tail-blindness into the safety score. Off-the-shelf open-weight judges are worse for a dispositional, not capability, reason -- and disposition is fine-tunable. We therefore distill a per-metric, psychologist-corrected target into a small, frozen, local model, aipsy-judge-1.0, an Apache-2.0 fine-tune of Gemma-4-26B-A4B. aipsy-judge-1.0 tracks the corrected target better than its base on the composite (ICC 0.64 to 0.75) and crisis detection (kappa 0.65 to 0.82), catches 92% of crises with a false-positive lean, and grades more faithfully than any single frontier judge, while every transcript stays on the machine. These are directional readings against a single-expert-informed target, not validated multi-rater agreement. A safety grader that shares a vendor's post-training shares its blind spots.