arXiv Machine Learning By Jeonglyul Oh, Ikkyu Choi, Inseop Youn, Youngjae Kim

BAFF: Bid-Aware Filter Family for Mitigating Training Data Interference in RTB A/B Tests

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The paper introduces BAFF, a Bid‑Aware Filter Family that mitigates training data interference in real‑time bidding (RTB) A/B tests by applying (k,l)-parameterized hard filters to control bias from ad‑ranking and bid‑pricing disagreements. It proposes a three‑stage online measurement protocol to evaluate data‑sharing strategies against an interference‑free reference model. Experiments show that BAFF variants outperform both log‑sharing and log‑splitting in offline simulations and live DSP deployments, preserving key business metrics more closely.

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arXiv Machine Learning
Jul 30

Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents

arXiv:2607. 27083v1 Announce Type: new Abstract: As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure.

By Yicheng Feng, Yan Zhang, Yan Cheng, Wei Qi
arXiv AI
3d ago

You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference

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.

By Liang He, Jingbo Wen, Yixiong Chen, Yue Yang, Qizhen Lan, Kangning Cui, Xilu Wang