Stanford paper pairs Astra exploration with Opus step auditing
A Stanford study has Astra run high-entropy breadth search while Opus 5.5 audits each step and rewrites only the broken ones, lifting MATH500 from 29.6% to 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
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