arXiv (All)
open access
OptiHive: Ensemble Selection for LLM-Based Optimization via Statistical Modeling
LLM-based solvers have emerged as a promising means of automating problem modeling and solving. However, they remain unreliable and often depend on iterative repair loops that result in significant latency. We introduce OptiHive, a framework that enhances any solver-generation pipeline to produce hi
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
- SAGE-Loop: Reliable Closed-Loop LLM-Driven AutoML with Trial-and-Correction and Adaptive Ensembling#1000403
- PMosFM: Preconditioned Manifold Matching for One-Step Physics-Constrained Generation#1154548
- PMosFM: Preconditioned Manifold Matching for One-Step Physics-Constrained Generation#1136816
- DebugRepair: Enhancing LLM-Based Automated Program Repair via Self-Directed Debugging#124132
- If It's Not Buggy, Don't Fix It: On the Dynamics of Iterative Bug-fixing with LLMs#997036
Record · ID 1201904
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