Stanford paper pairs Astra exploration with Opus 5.5 step auditing
A video post describes a two-model setup where GPT-6 Astra samples at high temperature to find solution paths and Claude Opus 5.5 audits each step at low temperature, rewriting only the flawed step, claiming MATH500 rises from 29.6% to 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.
This post quotes another post: x.com/motyka_9/status/2091225860808302808