On tau-Bench Retail with Qwen3-32B, trigger policies yield task rewards of 0.347 (Never-think), 0.405 (Warm-up), 0.384 (Always-think), 0.412 (Variance slope beta_t > tau_beta), 0.442 (Temporal curvature kappa_t < -0.15), and 0.405 (Learned-HST), with mean token costs in thousands of 125.7, 107.1, 119.3, 105.1, 104.6, and 108.5 respectively.

On tau-Bench Retail with Qwen3-32B, trigger policies yield task rewards of 0.347 (Never-think), 0.405 (Warm-up), 0.384 (Always-think), 0.412 (Variance slope beta_t > tau_beta), 0.442 (Temporal curvature kappa_t < -0.15), and 0.405 (Learned-HST), with mean token costs in thousands of 125.7, 107.1, 119.3, 105.1, 104.6, and 108.5 respectively. · CiteArk

测量结果

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指标与条件	论文报告	最近可用实测	证据判断
model: Qwen3-32B · domain: Retail · policy: Never-think	0.347 fraction	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Warm-up	0.405 fraction	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Always-think	0.384 fraction	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Variance-slope	0.412 fraction	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Temporal-curvature · threshold: -0.15	0.442 fraction	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Learned-HST	0.405 fraction	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Never-think	125.7 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Warm-up	107.1 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Always-think	119.3 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Variance-slope	105.1 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Temporal-curvature · threshold: -0.15	104.6 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Retail · policy: Learned-HST	108.5 thousands_tokens	—	尚未评估

报告指标与观测值

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测量结果