SMCS is a scalable multi-LLM collaboration system using retrieval-based selection and exploration-exploitation-driven enhancement. The dataset supports the research published on arXiv in July 2025. It was created by the author 'aisfuture' and last updated on May 21, 2026.
Use Cases
- Benchmarking retrieval-based LLM selection methods based on the system's described architecture.
- Studying exploration-exploitation trade-offs in multi-agent AI systems based on the system's enhancement mechanism.
- Developing scalable collaboration frameworks for large language models based on the SMCS system concept.
Strengths
- Dataset is directly linked to a peer-reviewed arXiv paper (arXiv:2507.14200) published in July 2025.
- Associated with a public code repository (https://github.com/magent4aci/SMCS) for reproducibility.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last updated 2026-05-21 13:10:24; freshness should be verified.
Provenance
- Source
- aisfuture