For every binary string of known length n and known rational deletion retention probability p, one randomized algorithm reconstructs the string with probability at least 2/3 using an explicit sample budget and bit time polynomial in that budget and the binary input length; for each fixed p, both are quasipolynomial in n.
For every binary string of known length n and known rational deletion retention probability p, one randomized algorithm reconstructs the string with probability at least 2/3 using an explicit sample budget and bit time polynomial in that budget and the binary input length; for each fixed p, both are quasipolynomial in n.
For every binary string of known length n and known rational deletion retention probability p, one randomized algorithm reconstructs the string with probability at least 2/3 using an explicit sample budget and bit time polynomial in that budget and the binary input length; for each fixed p, both are quasipolynomial in n.
If correct, turns worst-case quasipolynomial trace sample complexity into an efficient reconstruction algorithm with uniform parameter control.
Can every binary string be reconstructed from deletion traces with both quasipolynomial samples and quasipolynomial computation?
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OpenAI reports a uniform quasipolynomial-time trace-reconstruction decoder
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