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原文摘要

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 $0<γ<q-1$. Numerical comparisons quantify the gains and the remaining gaps.