arXiv (All)
open access
Harnessing Large Language Models to Compile Task-Relevant Context into Bayesian Optimisation
Incorporating rich task-relevant context, such as domain knowledge and external observations, is a key capability yet remains challenging for Bayesian optimisation (BO). Recently, practitioners have started to use large language models (LLMs) to generate and execute BO programs through coding harnes
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
- Toward Latent Language Model Skills Steering and Optimization: An Empirical Study#794243
- Toward Latent Language Model Skills Steering and Optimization: An Empirical Study#790224
- Multi-module GRPO: Composing Policy Gradients and Prompt Optimization for Language Model Programs#130775
- Exploring Autonomous Agentic Data Engineering for Model Specialization#800049
- Beyond Prompt or Skill? Attribution-Guided Optimization of Modular LLM Programs#1116823
Record · ID 1131989
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