What was already known
Earlier work provided quasipolynomial sample bounds using nonlocal statistics but did not supply a uniform quasipolynomial-time decoder for an unknown string; pairwise distinguishability alone leaves an exponential search over candidates.
AI involvement
OpenAI credits itself as manuscript author and states that its internal frontier model produced the results in this release. Individual human input is not detailed enough to establish task-only autonomy.
OpenAI is named as author on the manuscript, and its release attributes the results to an internal frontier model. The individual human input is not detailed enough to establish autonomy.
Importance
Turns worst-case quasipolynomial trace sample complexity into an efficient reconstruction algorithm with uniform parameter control.
Limitations
The guarantee assumes known length and rational retention probability. The linked Lean scope proves sample lower bounds, not the decoder or its running time.
Technical verification
Reported is the default when evidence for a higher level is insufficient. The OpenAI-authored manuscript reports and argues the result, but the reviewed sources do not document a completed check of the full assessed claim.
Human understanding
Few is the default when evidence for a higher level is insufficient. No named person is documented as explaining the central argument for this assessed contribution.
Correction and publication history
Primary manuscript, attribution, prior-work, and precise partial or mismatched Lean-scope review for non-notifying local inclusion.
Read this published revision