arXiv:2607. 07343v1 Announce Type: cross Abstract: Online truckload bid acceptance is a closed-loop stochastic decision problem in which a carrier or broker must, in real time, accept or reject a tendered load subject to operational feasibility, fleet repositioning costs, and opportunity cost against future demand.
arXiv:2607. 16891v1 Announce Type: cross Abstract: A truckload carrier must accept or reject each load tender within seconds.
arXiv:2605. 12768v2 Announce Type: replace-cross Abstract: Open time-series forecasting (TSF) benchmarks cover retail, energy, weather, and traffic, but supply-chain logistics remains underserved.
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.
arXiv:2505.02796v3 Announce Type: replace-cross Abstract: We study budget pacing in repeated first-price auctions when an advertiser's private-value distributions change over time and the stationary...
arXiv:2606. 25068v1 Announce Type: new Abstract: Online time-series forecasters receive labels only after horizon-dependent delays, while every adaptation step spends limited compute.
The paper demonstrates that in open‑weight LLM inference markets, selecting a model is insufficient; clients must also choose a provider, as the same model can differ markedly in quality, latency, availability, and price across providers. The authors propose a market‑aware routing approach, including a measured‑map policy and an online router called FACET, which certifies provider feasibility for each task and safely falls back to a reliable anchor. Experiments show that this strategy yields cost savings while maintaining quality and avoiding degraded endpoints.
arXiv:2609.00710v1 Announce Type: cross Abstract: An LLM application often sells or internally allocates several service products: a small or premium model, a short or long token cap, and possibly mu...
arXiv:2606. 03736v2 Announce Type: replace-cross Abstract: We study resource-constrained dynamic pricing when the seller seeks revenue and valid inference about demand at a price fixed before the selling season.
arXiv:2608. 16216v1 Announce Type: new Abstract: What is the right delay complexity when a learner can track only $C$ pending feedback items and discarded feedback is permanently lost?
arXiv:2607. 09090v1 Announce Type: new Abstract: In large-scale ride-hailing, hold control is a critical mechanism for improving passenger-driver experience.
arXiv:2607. 25068v1 Announce Type: new Abstract: Routing decisions between a cheap heuristic and an expensive large language model (LLM) are typically framed as a difficulty problem: send the hard cases to the expensive path.
The paper introduces a discrete‑time Markov decision process (DTMDP) model for a finite‑horizon, multi‑item capacitated lot‑sizing problem where demand quantities are deterministic but demand‑arrival times are stochastic. It compares stochastic instances to deterministic counterparts, showing that stochastic timing significantly enlarges the state space, transitions, solution time, and memory usage. A genetic algorithm (GA) is proposed to search feasible state‑feedback policies, achieving an average optimality gap of about 3.44 % and a speedup of roughly 6.89× on challenging benchmark instances.