online-algorithms

论文提出 Edit Discovery Optimization (EDO) 问题框架，用于在有限编辑器成本预算下选择性地将人机交互数据交给更强的编辑器（人类专家或更强模型）检查与修正，并提出在线算法 EDIT 在四个推理与事实问答基准上取得更优的成本-错误权衡。

The paper proves the k-server conjecture by demonstrating that the work function algorithm achieves a competitive ratio of k on every metric space.

This paper extends CONES to time-varying loss functions, showing bounds for regret and movement cost using projected proximal algorithms in convex optimization.

Proposes SOLAR, a learning-augmented framework for semantic cache replacement in LLM agents, outperforming classical heuristics by using regret-based modification timing and Bayesian online learning. Achieves constant competitive ratio and significant improvements over FIFO.