Quantitative lower bounds for trace reconstruction
Recorded by the registry's curators from GitHub; the authors did not submit it here.
OpenAI
Exact worst-case reconstruction of a binary word from independent deletion traces requires $n^{\Omega(\log\log n)}$ samples for every fixed deletion probability $q\in(0,1)$, even with unrestricted computation and any fixed positive success probability. This gives a negative answer to the polynomial-sample question for binary trace reconstruction. More generally, when $q^3\log n\to\infty$, we prove a lower bound of $n^{c\log(q^3\log n)}$ samples for every fixed $0<c<1/(4\log2)$. Here $q$ is the known deletion probability, and all logarithms are natural.
Claims
Files and sources
- Paper
- math @ 1581d4c, build/main.tex ↗
- Posted on GitHub
- 6 October 2026
No verification run is on record for this version.