On tau-Bench Airline with Qwen3-32B, trigger policies yield task rewards of 0.136 (Never-think), 0.280 (Warm-up), 0.364 (Always-think), 0.328 (Variance slope beta_t > tau_beta), 0.352 (Temporal curvature kappa_t < -0.10), and 0.364 (Learned-HST), with mean token costs in thousands of 98.2, 92.6, 100.6, 86.3, 83.0, and 87.5 respectively.

On tau-Bench Airline with Qwen3-32B, trigger policies yield task rewards of 0.136 (Never-think), 0.280 (Warm-up), 0.364 (Always-think), 0.328 (Variance slope beta_t > tau_beta), 0.352 (Temporal curvature kappa_t < -0.10), and 0.364 (Learned-HST), with mean token costs in thousands of 98.2, 92.6, 100.6, 86.3, 83.0, and 87.5 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: Airline · policy: Never-think	0.136 fraction	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Warm-up	0.28 fraction	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Always-think	0.364 fraction	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Variance-slope	0.328 fraction	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Temporal-curvature · threshold: -0.1	0.352 fraction	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Learned-HST	0.364 fraction	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Never-think	98.2 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Warm-up	92.6 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Always-think	100.6 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Variance-slope	86.3 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Temporal-curvature · threshold: -0.1	83 thousands_tokens	—	尚未评估
model: Qwen3-32B · domain: Airline · policy: Learned-HST	87.5 thousands_tokens	—	尚未评估

报告指标与观测值

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测量结果