{
  "id": 18295,
  "url": "https://arxiv.org/abs/2608.09366v1",
  "title": "Beyond Binary: Continuous State Optimization with Graph-Structured Objectives",
  "summary": "Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency. While recent work has formalized this as an optimization problem over binary states, many real-world control parameters, such as fairness thresholds, diversity mixing rates, or resource budgets, are continuous. In this work, we extend the framework to \\emph{continuous state spaces}. We model the problem as minimizing a sum of linear objectives su",
  "authors": "Corinna Cortes, Yishay Mansour, Mehryar Mohri",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T09:46:28.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18295",
  "original_url": "https://arxiv.org/abs/2608.09366v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}