A new stochastic load balancing model is introduced that allows for a tradeoff between non-adaptive policies and performance, with results showing a 2-reservation approximation to the omniscient optim…

This paper proves conditions for efficiently approximating expectations of certain functions with respect to standard Gaussian or symmetric exponential probability measures.

The paper introduces Temporary Power Adjusting Withholding (T-PAW), a generalized and more potent block withholding attack than the existing PAW attack, demonstrating that this attack can yield signif…

The paper shows that the envy-free cake-cutting problem with three agents is intractable and establishes the first lower bounds for the Jordan curve problem.

This paper proposes a dependency-aware autoscaling framework for serverless computing, integrating graph-based bottleneck identification, short-term workload forecasting, multi-model consensus, and co…

This paper measures the lower bound for the shortest program generating a sequence, proving a conservation law and providing a deterministic engine to recover generating programs for certain sequences…

The paper introduces AutoformBot, a multi-agent system that successfully autoformalizes a large corpus of open-access graduate-level mathematics textbooks into a verified library in Lean 4, demonstrat…

This paper studies the autarky problem of scheduling energy-consuming jobs with time windows using a battery and an energy forecast, and shows NP-hardness, polynomial-time solvability, and fixed-param…

This paper proposes an approximation algorithm for the submodular joint replenishment problem with decomposable submodular ordering cost functions, achieving an O(k)-approximation.

This paper evaluates AI agent systems for electronic design automation (EDA) using a unified benchmark called FluxBench, assessing their performance across various EDA workflows and tasks.

This paper presents SpecBox, a runtime system for LLM agents that uses speculative sandbox preallocation to improve resource utilization and reduce interactive tail latency.

The paper introduces CaDDTree, a cost-aware method that optimizes token throughput by jointly selecting the tree structure and node budget for speculative decoding, outperforming existing methods like…

This paper proposes a proactive fair scheduling framework to prevent fabric oversubscription and eliminate incast in Mixture of Experts (MoE) architectures, demonstrating consistent link utilization a…

The paper introduces OpenClawBench, a large-scale dataset and framework for measuring process-side anomalies in real-world agent execution trajectories, demonstrating that task success does not guaran…

The paper addresses limitations in the Linear Ordering Problem (LOP) by introducing a novel benchmark suite derived from current economic data and an algorithmic scheme to generate diverse, high-quali…

This paper investigates the use of a Random Forest surrogate model to predict molecular dynamics workload performance and recommend optimal hybrid MPI+OpenMP configurations without exhaustive benchmar…

The paper proposes methodologies to measure lag relevance in machine learning forecasting models using Ghost variables, Shapley values, and additive importance measures. It also introduces auto-releva…