arXiv (All)
open access
Search-Aware Reinforcement Learning for Multi-Component Query Understanding in Roblox Game Search
Query understanding (QU) plays a critical role in production search systems, translating raw user queries into search execution plans that drive downstream retrieval and ranking. While large language models (LLMs) have enabled QU to be framed as a structured multi-task generation problem (e.g., inte
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
- Strategyproofness-Exposing Descriptions of Matching Mechanisms#996849
- Salience reasoning in coordination games#766363
- PA3: Policy-Aware Agent Alignment through Chain-of-Thought#798799
- PA3: Policy-Aware Agent Alignment through Chain-of-Thought#797385
- VRL-Bench: Benchmarking agents on computer control tasks under finite trial budgets#1000354
Record · ID 1078248
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