arXiv (All)
open access
Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning
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
- Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning#664355
- Heterogeneity in neural network engagement supports individual differences in top-down attention#279913
- Measuring Time-Horizon Engagement Effectiveness: Persistence, Recency, and Re-Emergence#1083906
- Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling#363259
- Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiences#1156216
Record · ID 665855
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