In the online Minimum cost Bipartite Perfect Matching with Delays (MBPMD) problem, $m$ requests and $m$ servers arrive over time in a metric space. An online algorithm has to match each request to a server and can postpone a matching decision by paying the delay cost. The objective is to minimize the total distance and delay cost. Azar, Chiplunkar, and Kaplan gave a randomized $O(\log n)$-competitive algorithm for metric spaces with $n$ points that are known in advance. For deterministic algorithms, however, the best known competitive ratio in general metric spaces was $O(m^{0.59})$, due to Azar and Jacob-Fanani. On the line, Kuo proposed the Virtual Robust Matching (VRM) algorithm and showed that it is $O(\sqrt{m}\log^2 m)$-competitive. We prove that VRM is $O(\log m)$-competitive for the MBPMD problem in any metric space. VRM is deterministic and does not need to know the metric space or $m$ in advance. It is the first deterministic polylogarithmic-competitive algorithm for the MBPMD problem in general metric spaces. Unlike the previous analysis of VRM, our analysis does not rely on a geometric decomposition of the metric space. Instead, it charges the waiting time of requests over time to the edges of a fixed optimal matching. We also extend VRM to the non-bipartite version and prove that the resulting deterministic algorithm is $O(\log m)$-competitive. This improves the $O(\log^5 m)$ bound of Dufay and Wattenhofer and matches a concurrent result of Ahmadian, Chawla, Kumar, Purohit, and Zhang. The proof that VRM is $O(\log m)$-competitive and its non-bipartite extension were found by GPT-6 Astra (OpenAI) with almost no human input. We used Claude Opus 5.5 (Anthropic) to simplify and present the results. See the AI Usage and Methodology section for details. We take full responsibility for this paper.

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