发现大模型在因果推理中会因权威暗示或社交压力而放弃正确推理，提出新评测与方法解决此问题。

Large language models increasingly fail in a way that scalar accuracy cannot diagnose: they produce a sound reasoning trace and then abandon it under social pressure or an authoritative hint. We argue that this is a control failure, not a knowledge failure, and that it requires an evaluation surface richer than a single accuracy number. We introduce CAUSALT3, a 454 instance expert curated benchmark for causal reasoning across all three rungs of Pearl's ladder, and a three axis evaluation that decomposes performance into Utility (sensitivity to valid causal claims), Safety (specificity against invalid ones), and Wise Refusal (calibrated abstention on genuinely underdetermined items). On this surface we document three reproducible pathologies: a Skepticism Trap at L1 where capable models over refuse sound links, a Sycophancy Trap at L2 where confident user pressure flips correct answers, and a Scaling Paradox at L3 where a frontier model underperforms an older one on counterfactual Safety by 55 points. To mitigate these failures without retraining, we propose Regulated Causal Anchoring (RCA), an inference time process verifier that audits trace output consistency under a PID style feedback loop and abstains rather than ratifying a detected mismatch. Across CAUSALT3 and a supporting CAP-GSM8K stress test, RCA reduces sycophantic acceptance to near zero while preserving valid hint acceptance, recasting trustworthy reasoning as a question of inference time control rather than scale.