arXiv (All)
open access
The Missing Coefficients: Bayesian Pairwise Merging for Model Personalization
How can we personalize a shared expert library from a user's pairwise choices? Prior work can realize different reward trade-offs by merging reward-specialized experts, given a vector of trade-off weights. In practice, users can more naturally choose between outputs than specify numerical weights. T
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
- REPREC: Representation Driven Parameter-Efficient Recommendation System#684554
- REPREC: Representation Driven Parameter-Efficient Recommendation System#682779
- Keeping the Index Open: The Recommendation-Side Cost of Shared Search and Recommendation#674576
- SetMIR: Multi-Interest Retrieval as Set Prediction#799467
- SetMIR: Multi-Interest Retrieval as Set Prediction#798044
Record · ID 1144022
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