Dimensions overall score 8.0

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• DocSage significantly outperforms state-of-the-art long-context LLMs and RAG systems on MDMEQA benchmarks, achieving more than 27% accuracy improvements.result 90%
• Standard RAG's vector similarity-based coarse-grained retrieval often omits critical facts in multi-document multi-entity question answering.limitation 85%
• Graph-based RAG fails to efficiently integrate fragmented complex relationship networks in multi-document scenarios.limitation 85%
• DocSage uses a schema discovery module that dynamically infers query-specific minimal joinable schemas to capture essential entities and relationships.method 90%
• DocSage transforms unstructured text into semantically coherent relational tables with error-aware correction mechanisms to reduce extraction errors.method 90%
• DocSage's reasoning module performs multi-hop relational reasoning over structured tables, leveraging schema awareness to efficiently align cross-document entities.method 90%
• DocSage offers precise fact localization via SQL-powered indexing as a key advantage over existing approaches.technical 85%
• DocSage mitigates LLM attention diffusion via structured representation, which is a key advantage of its agentic design.technical 85%

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