arXiv (All)
open access
Statistical attribute alignment for black-box generative AI via output post-processing
Generative AI systems are increasingly used, but aligning their outputs with user requirements poses a continuing challenge. Here, we aim to ensure that the distribution of an attribute of an AI-generated output aligns with a user-specified target. This is motivated by examples such as fairness, whe
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- The Computational Search for Unity: Synthesis in Generative AI#718241
- On the Human and Computer Alignment of Attribute-Based Music Matches#820980
- Generative Model Unlearning: A Survey through Target Events, Unlearning Operators, and Evaluation Protocols#609804
- Generative AI in Action: From Core Algorithms to Industry Use Cases#90100
- Quantum MeanFlow: single-shot generative sampling on NISQ hardware#822580
Record · ID 1106306
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive