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arXiv 最近完成扫描:2026-11-15 14:58 · 全站最近入库:2026-11-14 15:09各信源进度 →

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新论文 · v1arXiv:2611.11370

A Simple Phase-King Protocol with Predictions

Antonia Giannoulea, George Papadoulis, Nikos Leonardos, Aris Pagourtzis

原文摘要

Inspired by recent work (Ben-David, Dzulfikar, Ellen, and Gilbert, $\textit{Byzantine Agreement with Predictions}$, PODC 2025) we study synchronous binary Byzantine Agreement in the learning-augmented setting, where each process receives predictions about which processes are honest or Byzantine. We present a deterministic Phase King protocol for the anauthenticated setting, combining early stopping with a specifically designed prediction-aware king selection procedure. For at most $t<n/3$ Byzantine processes, the protocol handles arbitrarily many prediction errors, improving upon the $O(n^{3/2})$ bound of Ben-David et al., and terminates within $O(P)$ communication rounds, where $P=1+\min\{\mathbf{B}/n,f\}$, $\mathbf{B}$ is the total number of incorrect prediction bits held by honest processes, and $f$ is the actual number of Byzantine processes. Our protocol uses $O(n^2P)$ messages and $O(n^2P\log n)$ bits, avoiding the initial cubic bit cost of exchanging complete prediction vectors. In comparison to very recent independent work (Dufay, Dzulfikar, and Gilbert, $\textit{Predictions Can Only Help! Communication Efficient Byzantine Agreement with Predictions}$, DISC 2026), which achieves the same error tolerance and round complexity but better communication bounds, our construction provides a much simpler Phase King approach that completely avoids involved preprocessing constructions, rendering it more practical, especially for achieving agreement in time-evolving networks. We believe that the novel prediction-aware king selection technique of our scheme is of independent interest and can find further applications in learning-augmented distributed consensus.

cs.DC

版本更新 · v2arXiv:2610.40290

Leveraging Recourse and Size Bounds for OnlineKnapsack with Removable Items

Dimitris Fotakis, Laurent Gourvès, Aris Pagourtzis, Panagiotis Patsilinakos

原文摘要

In the online unweighted knapsack problem, items arrive sequentially and must be accepted or rejected immediately for a bounded-capacity knapsack, aiming to maximize the total size of accepted items. When decisions are irrevocable, no algorithm achieves a non-trivial competitive ratio. An essential relaxation that circumvents this impossibility is to allow previously accepted items to be removed from the knapsack (a.k.a. removal). In this work, we investigate possible further improvements on the competitive ratio with removal by bringing together two other previously proposed model extensions: knowledge of an upper bound on the item sizes (a.k.a. bound awareness) and ability to reinsert previously rejected items(a.k.a.recourse). Our contributions are twofold. First, we establish close upper and lower bounds on the competitive ratio under bound awareness, removal and recourse. We show that significant improvements on the best known competitive ratio are possible when the upper bound on the item sizes is not too restrictive and only few uses of recourse are available. Second, we introduce the notion of adaptive online algorithms which do not require to know any upper bound on the item sizes but still achieve performance guarantees comparable to the ones obtained under the bound awareness model.

cs.DS · cs.CL