arXiv (All)
open access
MOSCOPT: Mixture-of-Skills Collective Optimization for LLM Agents
Natural language prompts and skills serve as the strategic backbone of LLM-based agents. Recent advances in prompt and skill optimization have achieved notable gains, yet all existing methods optimize a \emph{single} text template---missing the synergy among multiple complementary strategies. We pro
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
- RACER: Reinforced Agent Collaboration for Explainable Reasoning on Knowledge Graphs#794072
- RACER: Reinforced Agent Collaboration for Explainable Reasoning on Knowledge Graphs#790053
- AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning#799234
- AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning#797811
- MAxBench: A Multinomial Concept Recovery Benchmark#1070573
Record · ID 1035610
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