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ChartWalker introduces a novel framework for cross-chart retrieval-augmented generation (RAG) using hierarchical knowledge graph construction and structure-aware sampling. It releases a challenging benchmark (ChartWalker-Bench) and an agentic baseline (ChartWalker-Agent), revealing significant performance gaps in current RAG paradigms.
ContextRAG introduces an extraction-free method for constructing hierarchical graph indices for retrieval-augmented generation, using Residual-Quantization K-Means and Formal Concept Analysis to reduce LLM calls and tokens by orders of magnitude while maintaining competitive F1 scores on multi-hop questions.