AgenticRAG-Survey (1,625⭐) maps the evolving landscape of agentic retrieval-augmented generation, offering a taxonomy to help engineers pick the right architecture for their RAG pipelines.
A curated and frequently updated bibliography accompanying the IJCAI 2024 survey paper on LLM-based multi-agent systems, organizing research into five key categories and revealing emerging trends.
The Awesome-LLM-Ensemble repo catalogs research on combining multiple LLMs with a clear three-phase taxonomy: before, during, and after inference ensemble methods.
