斯坦福论文：Astra 探索 + Opus 5.5 审计的混合推理法
视频帖介绍一套双模型编排：GPT-6 Astra 高温采样找多条解法路径，Claude Opus 5.5 低温逐步审计并只重写错的那一步，号称 MATH500 从 29.6% 提到 58.0%。
this is pure f*cking treasure A Stanford research group found a way to orchestrate Claude Opus 5.5 and GPT-6 Astra together, completely breaking through the resampling wall. Most developers think test-time compute means running 32 blind retries on one model. You burn API tokens repeating the same 2-3 error clusters over and over. Stanford's paper splits the compute between two different brains: «BREADTH SEARCH: GPT-6 Astra runs high-entropy exploration (T=0.8) to discover divergent solution paths STEP AUDIT: Claude Opus 5.5 scans each step at T=0.0 and catches the exact logic flaw LOCAL SURGERY: Opus rewrites only the broken step instead of throwing away the entire reasoning chain VERIFIER-FREE CONSENSUS: majority voting locks the proof without relying on dritt-prone PRMS» The benchmark results crush standard Best-of-N sampling: 91.2% recovery rate on detected reasoning flaws <1.8% regressions on correct branches MATH500: 29.6% → 58.0% (+28.4% absolute) AMC-2023: +7.5% with zero reward models Astra explores the maze. Opus repairs the logic. You stop burning tokens on blind retries. You start spending compute on verified reasoning depth.
这条是引用着另一条写的：x.com/motyka_9/status/2091225860808302808