Exponentially Larger Deletion-Correcting Codes and Constant-List-Decodable Insertion-Deletion Codes
Year: 2026
Type: preprint
Abstract: We establish improved asymptotic rate bounds for adversarial insertion--deletion codes. For binary unique-deletion correction in the linear-deletion regime, we strictly improve Levenshtein's classical lower-bound expression. At deletion fraction $δ=0.1$, our achievable rate increases from $0.162009$ to $0.194890$, a relative gain of approximately $20.30\%$. For list decoding with any prescribed finite list size $L$, we obtain new achievable-rate bounds for binary deletion and insertion codes. Our approach combines the Lovász local lemma with refined counting of common subsequences and supersequences, using generating functions and run-restricted sampling. We also establish a combinatorial reduction that trades $L$ units of insertion budget for one unit of deletion budget without changing the code or increasing the list size. It transfers insertion lower bounds at $γ+Lδ$ and deletion upper bounds at $δ+γ/L$ to mixed errors. We also derive a run-counting Levenshtein-type list-decoding upper bound and a higher-order Elias-type upper bound. The latter recovers Yasunaga's asymptotic unique-decoding bound at $L=1$ and strictly improves the Haeupler--Shahrasbi--Sudan insertion upper bound for every fixed $L$ and $0<γ
Source: arXiv (Cornell University)
Authors: Kiah, Han Mao, Wei, Hengjia, Zeng, Ruixiao
Institution: Nanyang Technological University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G2857663206
OpenAlex ID: W7213934878