arXiv AI

Budget-First Tariff Recommendation (BFTR): A Complete Algorithmic Framework for Telecom Plan Recommendation without Overcharging

The paper introduces Budget-First Tariff Recommendation (BFTR), an algorithmic framework that offers telecom plans without overcharging by aligning final prices with catalog reference prices. BFTR incorporates eight Budget-First strategies, including two novel hybrid approaches—Recursive Hybrid and Knapsack-First Hybrid— and mathematically proves that a suitable offer exists for any positive budget with zero surcharge for non‑interpolated strategies. Experiments on a Nigerian MTN‑inspired dataset show that all strategies achieve zero overcharging, with Recursive Hybrid delivering optimal customer utility and Piecewise maximizing volume, while maintaining sub‑10 ms execution times.

Hugging Face Trending Papers

Aug 19

Budget-First Tariff Recommendation (BFTR): A Complete Algorithmic Framework for Telecom Plan Recommendation without Overcharging

The paper introduces BFTR, a Budget‑First Tariff Recommendation framework that offers eight algorithmic strategies, including two novel hybrid approaches (Recursive Hybrid and Knapsack‑First Hybrid). It mathematically guarantees no overcharging by aligning final prices with catalog reference prices and proves that a suitable offer exists for any positive budget. Experiments on 974 Nigerian MTN customers show all strategies achieve zero surcharge, with Recursive Hybrid and Piecewise delivering optimal budget usage and volume, respectively, while maintaining sub‑10 ms execution times.

arXiv Machine Learning

Sep 22

Plan-Driven Adaptive Bidding for First-Price Auctions with Budget Constraints under Nonstationarity

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...

By Yige Wang, Jiashuo Jiang

benchmarks

arXiv Machine Learning

Sep 2

Prediction-Assisted Pricing and Admission for LLM APIs with Stochastic Token Consumption

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...

By Patrick Wong

llms benchmarks

arXiv Statistics ML

Sep 16

Learning Choice Model Trees for Feature-Based Multi-Product Pricing: Exact Optimization and Field Evidence

arXiv:2609.16952v1 Announce Type: cross Abstract: Feature-based multi-product pricing uses customer characteristics to identify demand heterogeneity and tailor prices across products. Choice model tr...

By Jiajie Zhang, Yanqiu Ruan, Xiao Jin, Chung Piaw Teo

arXiv Machine Learning

Jul 10

Resample or Reroute? Budget-Aware Test-Time Model Selection for Large Language Models

arXiv:2607. 08665v1 Announce Type: new Abstract: Routing among large language models (LLMs) trades response quality against serving cost, motivated by the reported gap between deployed routers and a per-instance oracle.

By Teng-Ruei Chen

llms benchmarks

arXiv Machine Learning

Aug 24

Adaptive Inference for Resource-Constrained Dynamic Pricing

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.

By Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi

arXiv AI

Aug 11

Adaptive Two-Level Allocation of a Conserved Capacity Budget Across Locations and Service Classes

arXiv:2608. 07747v1 Announce Type: new Abstract: We study how to share a single conserved capacity budget across many locations and two service classes when demand is uneven, time-varying, and can exceed supply.

By Simone Mainardi, Kaushal Bansal, Prabhat Singh

efficiency benchmarks

arXiv Machine Learning

Jul 22

JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing

arXiv:2604. 05845v2 Announce Type: replace-cross Abstract: Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget.

By Linghui Meng, Chun Gan, Shengsheng Niu, Chengcheng Zhang, Chenchen Li, Chuan Yang, Yi Mao, Xin Zhu, Jie He, Zhangang Lin, Ching Law

reinforcement-learning benchmarks safety

arXiv Machine Learning

Sep 4

EF1-Constrained Nash Social Welfare with Identical Additive Valuations: Complexity, Guarantees, and Experiments

arXiv:2609. 03846v1 Announce Type: cross Abstract: We study the allocation of indivisible goods among agents with identical additive valuations, focusing on envy-freeness up to one good (EF1) and Nash social welfare (NSW).

By Zih-Sian Yang, Yi-Hao Chen, Yu-Te Kuan, Cheng-Jui Wu, Chuang-Chieh Lin, Po-An Chen

agents reinforcement-learning

arXiv AI

Jul 29

Finding Optimal Cost-Bounded Plan Reductions: Refined Model

arXiv:2607. 25484v1 Announce Type: new Abstract: In some real applications a plan may later become unfeasible due to newly imposed budget constraints, yet, at the same time, using only the original actions of the plan and their order is mandatory.

By Martha Del Toro, Raquel Fuentetaja, Angel Garc\'ia-Olaya

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

llms

arXiv AI

Jul 29

Large Language Model for Operations Research Formulation Selection in Multi-Warehouse Inventory Allocation

arXiv:2607. 25956v1 Announce Type: new Abstract: Multi-warehouse inventory allocation is typically formulated as a mixed-integer programming (MIP) problem, yet no single formulation consistently matches heterogeneous instance-level regimes induced by demand concentration, inventory imbalance, replenishment scale, service constraints, and forecast volatility.

By Jintao Xu, Yingzheng Ma, Jiong Dong, Yongzhi Qi, Jianshen Zhang

llms fine-tuning

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