斯坦福论文：Astra 探索 + Opus 审计的混合推理
斯坦福研究让 Astra 做高熵广度搜索、Opus 5.5 逐步审计并局部改写，MATH500 从 29.6% 升到 58%。
this is pure f*cking treasure A Stanford research group found how to orchestrate Claude Opus 5.5 and GPT-6 Astra together, and it completely breaks 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 in loops Stanford's paper (arXiv:2608.05643) 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 fallacy > local surgery: Opus rewrites only the broken step instead of dumping the whole reasoning chain > verifier-free consensus: majority voting locks the proof without relying on drift-prone PRMs the benchmark metrics outclass standard Best-of-N sampling: > 91.2% recovery rate on detected reasoning flaws > under 1.8% regressions on correct branches > MATH500 jumps from 29.6% to 58.0% (+28.4% absolute gain) > AMC-2023 improves by +7.5% with zero reward models Astra explores the maze. Opus repairs the logic you stop burning tokens on blind retries. you run verified reasoning depth
这条是引用着另一条写的：x.com/marfinxx/status/2084595353060258071