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Author

Yuan Tian

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Review Aug 2026

MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems

MUSE is presented, an interactive meta-agent that enhances user understanding and control of agentic data science systems by dynamically restructuring low-level execution traces into multiple semantic levels that support navigation from high-level overviews to low-level implementation details.

Wei-Hao Chen, Weixi Tong, Yuan Tian et al. · 0 citations
Open access Jun 2026

Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization

A framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG, and a novel context engineering method for GraphRAG and Agentic RAG, addressing the context/memory overflow issues.

Long Chen, Ryan Razkenari, Yuxuan Zhou et al. · 0 citations

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