arXiv AI

Pricing Access to Dynamic Information Services

arXiv:2510. 09859v5 Announce Type: replace-cross Abstract: A provider sells a \emph{dynamic information service}---a real-time, capacity-constrained process that resolves a customer's uncertainty---to customers who differ privately in urgency.

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
arXiv Machine Learning
Oct 5

When Does Pooling Pay? Credibility and Resolution under Forgetting in Intermittent-Demand Forecasting

The paper investigates when pooling information across intermittent-demand series is beneficial, focusing on the trade‑off between forgetting past data and borrowing strength from other series. By applying a single exponential recency operator to both item‑ and group‑level statistics in a hierarchical empirical‑Bayes hurdle model, the authors derive a diagnostic that quantifies credibility and resolution, and use it to screen for regimes where refinement offers no advantage. Empirical tests on five public panels of over 19,000 series show that the diagnostic accurately predicts when shared priors improve accuracy, and the resulting forecaster (EBB) matches specialist Gaussian‑process models within a few percent while being far cheaper to run.

By Zong-Han Bai, Po-Yen Chu
arXiv AI
Sep 25

ERRAND: Budgeted Maintenance of Agent Memory

ERRAND is a new method for budgeted maintenance of agent memory that treats revalidation of stored knowledge as a priced errand competing for scarce actions. It uses an errand index that is single‑peaked, allowing certainty in either direction to cost nothing, and repairs by writing new versions rather than deleting old ones. In experiments across two drifting tool‑use worlds, ERRAND outperforms non‑oracle policies, achieving up to 10.0 percentage points improvement over eager revalidation while using only 11.0% of steps, and it self‑terminates when no budget is imposed.

By Beining Wu, Zihao Ding, Jun Huang
arXiv AI
Sep 25

Control the Harness, Control the Cost: Routing and Governing AI Coding Agents in the Enterprise

The paper discusses how enterprises increasingly deploy AI coding agent harnesses, often purchased from vendors like Anthropic or OpenAI, and how these harnesses dictate model choice, prompt handling, and cost. It introduces a fast, customizable routing system that classifies prompts and strategically routes them to minimize expensive model usage, achieving 14–21% cost savings in a simulated 10,000-seat enterprise. The study also evaluates risks across twenty harnesses, highlights vendor dependence, and proposes an internal control plane for future harness ownership decisions.

By Arian Abbasi, Alan Aqrawi, Ted Kwartler