This paper presents a compositional cost analysis for probabilistic programs with hierarchical cost structures, allowing computation of mean and higher moments of non-additive costs.

This paper analyzes data-access cost in a memory hierarchy and shows it scales with the fourth root of data size, predicting scalability.

This paper evaluates the feasibility and cost-effectiveness of large-scale AI data centers in low-Earth orbit (LEO) versus terrestrial facilities, considering factors like launch cost, power generatio…

This paper provides an explicit cost analysis of Toom-4 multiplication specifically tailored for the incomplete Number Theoretic Transform (NTT) framework, offering a concrete cost model for hybrid la…

The paper introduces a framework called expected ROI (eROI) to help firms make informed decisions on AI projects by decomposing each project into three components: Value if Successful, Likelihood of S…

This study estimates the true social cost of corporate data breaches by quantifying the direct financial and opportunity costs to victims, finding that these costs can significantly exceed corporate s…

This paper introduces cost-aware Retrieval-Augmented Generation (RAG), demonstrating that fixed evidence selection is brittle and that adaptive, agentic controllers are necessary for effective knowled…

This paper proposes a reliability-aware framework to solve the fuzzy shortest path problem in directed graphs, optimizing routes based not only on cost but also on the reliability of the associated fu…

CADAQUES is an open-source Python framework for autonomous discovery systems that treats cost as a first-class primitive.

This paper investigates ways to transform a theory-based methodology for optimizing visual analytics workflows from theory to practice using case studies.

This paper introduces approximation-preserving coresets, which provide weaker guarantees than strong coresets but stronger guarantees than weak coresets for preserving the costs of good solutions in b…

The paper models the trade-off between deploying increasingly capable AI systems and managing associated cyber risks, finding a 'deployment paradox' where high-loss environments with weak governance l…

This paper models transaction fee dynamics on blockchains by treating the transaction queue as a priority queue, providing analytical insights into how user delay costs influence fees.

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 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 investigates the importance of including reasoning traces in the responses of Large Language Models during both training and inference for Neural Machine Translation, and shows that it posi…

The paper introduces TradeLens, a toolkit for evaluating the agentic viability of large language model agents in trading systems by reconstructing trading trajectories and diagnosing intelligence-to-p…

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…

The paper introduces Hierarchical Adaptive Budgeter (HAB), a framework that improves LLM reasoning efficiency by adaptively allocating computational resources to match the intrinsic complexity of both…