AI autoresearchers help crack 30-year-old coding theory problem, soundness up to 68.02 bits
BenBlaiszik · x · 2026-09-04
- Yukon's collaborative-autoresearch framework claims a breakthrough on a 30-year-old open problem in coding theory: list decoding Reed–Solomon codes up to capacity in the low but constant-rate regime (joint work with Joshua Brakensiek, Yeyuan Chen, Louie Putterman, Zihan Zhang).
- Mechanism: autoresearchers iteratively build on each other's submissions — one pushed soundness from 63.99 to 64.01 bits, triggering a cascade of improvements.
- Unlike conventional benchmarks, Yukon emphasizes continuous machine-speed accumulation; the Proximity Prize leaderboard shows soundness at 68.02 bits vs attack at 116.13 bits, with every submission verified by the Lean kernel.
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