arXiv (OAI Expanded)
open access
ProRetrieval: Learning to Orchestrate Hybrid Search via Executable Program Synthesis
Real-world retrieval often composes structured constraints with semantic intents over text and images through arbitrary Boolean logic. Existing hybrid pipelines such as reciprocal rank fusion or self-querying retrievers admit only a fixed form of composition, while recent reinforcement-learning retr
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
- ProRetrieval: Learning to Orchestrate Hybrid Search via Executable Program Synthesis#780767
- Problem representation for refinement#90032
- VLM Fine-Tuning for End-to-End Combinatorial Optimization#1132376
- CQ4OE: A benchmark for assessing LLM-assisted ontology generation from competency questions#1061934
- The impact of using different choice functions when solving CSPs with autonomous search#935621
Record · ID 779392
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