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Evidence record 18295 · automatically gathered

Beyond Binary: Continuous State Optimization with Graph-Structured Objectives

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

Record details

Published: 10 August 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness
Retrieved: 11 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness

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How to cite this record

ethics.ai (10 August 2026), “Beyond Binary: Continuous State Optimization with Graph-Structured Objectives,” evidence record 18295, https://ethics.ai/record/18295 (originally published by arXiv fairness query).

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