arXiv Machine Learning By Caner Gocmen, Thodoris Lykouris, Deeksha Sinha, Wentao Weng

Scheduling in Queueing Systems with Uncertain and Evolving Holding Costs

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arXiv:2505. 21331v2 Announce Type: replace-cross Abstract: In content moderation for social media platforms, the cost of delaying the review of a content is proportional to its view trajectory, which fluctuates and is apriori unknown.

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arXiv Machine Learning
1d ago

Online Generalized-Mean Welfare Maximization: Achieving Near-Optimal Regret from Samples

The paper investigates online fair allocation of sequential items to agents with heterogeneous preferences, aiming to maximize generalized-mean welfare. In an i.i.d. arrival setting, a pure greedy algorithm achieves near-optimal “~O(1/T)” average regret without needing distributional knowledge. For nonstationary arrivals, the authors show that a single historical sample per distribution suffices to recover the same regret rate, using re-solving algorithms that remain robust to distribution shifts.

By Zongjun Yang, Rachitesh Kumar, Christian Kroer
arXiv AI
Aug 26

The Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem

The paper argues that large language model (LLM) providers, constrained by compute, often degrade service during congestion by routing queries to smaller models, cutting reasoning effort, or truncating context. It shows that this practice misrepresents costs because degraded answers can fail, leading to retries that inflate traffic or churn that erodes lifetime value. By modeling inference allocation with newsvendor, retry, and queueing frameworks, the authors derive a ‘shadow price of intelligence’ that quantifies the marginal value of each query, revealing that throttling under congestion acts as a demand lever rather than a cost lever.

By Elioth Sanabria