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ARXIV:2603.11798 · MULTI-DOCUMENT QUESTION ANSWERING · SUBMITTED 17 MAR · 21:43 UTC · FRESHNESS STALE
ARXIV:2603.11798MULTI-DOCUMENT QUESTION ANSWERINGSUBMITTED 17 MAR · 21:43 UTCFRESHNESS STALEarXiv
DocSage is an advanced framework for multi-document multi-entity question answering that enhances relational reasoning and information extraction.
Opportunity summary
Pain DocSage is an advanced framework for multi-document multi-entity question answering that enhances relational reasoning and information extraction.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence failed
DocSage is an advanced framework for multi-document multi-entity question answering that enhances relational reasoning and information extraction. However, existing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks suffer from critical limitations: standard RAG's…
Multi-document Multi-entity Question Answering inherently demands models to track implicit logic between multiple entities across scattered documents. However, existing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks suffer from critical limitations: standard RAG's…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. This agentic design offers three key advantages: precise fact localization via SQL-powered indexing, natural support for cross-document entity joins through relational tables, and mitigated…
Multi-Document Question Answering moved forward this cycle; last verified April 2026. Public score 8.0/10.
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DocSage is an advanced framework for multi-document multi-entity question answering that enhances relational reasoning and information extraction.
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10.48550/arXiv.2603.11798DocSage is an advanced framework for multi-document multi-entity question answering that enhances relational reasoning and information extraction.
Abstract
Multi-document Multi-entity Question Answering inherently demands models to track implicit logic between multiple entities across scattered documents. However, existing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks suffer from critical limitations: standard RAG's vector similarity-based coarse-grained retrieval often omits critical facts, graph-based RAG fails to efficiently integrate fragmented complex relationship networks, and both lack schema awareness, leading to inadequate cross-document evidence chain construction and inaccurate entity relationship deduction. To address these challenges, we propose DocSage, an end-to-end agentic framework that integrates dynamic schema discovery, structured information extraction, and schema-aware relational reasoning with error guarantees. DocSage operates through three core modules: (1) A schema discovery module dynamically infers query-specific minimal joinable schemas to capture essential entities and relationships; (2) An extraction module transforms unstructured text into semantically coherent relational tables, enhanced by error-aware correction mechanisms to reduce extraction errors; (3) A reasoning module performs multi-hop relational reasoning over structured tables, leveraging schema awareness to efficiently align cross-document entities and aggregate evidence. This agentic design offers three key advantages: precise fact localization via SQL-powered indexing, natural support for cross-document entity joins through relational tables, and mitigated LLM attention diffusion via structured representation. Evaluations on two MDMEQA benchmarks demonstrate that DocSage significantly outperforms state-of-the-art long-context LLMs and RAG systems, achieving more than 27% accuracy improvements respectively.
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Dimensions overall score 8.0
PROBLEM
DocSage is an advanced framework for multi-document multi-entity question answering that enhances relational reasoning and information extraction. However, existing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks suffer from critical limitations:...
METHOD
Multi-document Multi-entity Question Answering inherently demands models to track implicit logic between multiple entities across scattered documents. However, existing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks suffer from critical limitati...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. This agentic design offers three key advantages: precise fact localization via SQL-powered indexing, natural support for cross-document entity joins through relational tables, and mitigated LLM attention...
WHY NOW
Multi-Document Question Answering moved forward this cycle; last verified April 2026. Public score 8.0/10.
Evaluations on two MDMEQA benchmarks demonstrate that DocSage significantly outperforms state-of-the-art long-context LLMs and RAG systems, achieving more than 27% accuracy improvements respectively.
Directly stated in abstract with clear numeric improvement claim
partial
standard RAG's vector similarity-based coarse-grained retrieval often omits critical facts
Directly stated as a limitation of existing approaches in the abstract
partial
graph-based RAG fails to efficiently integrate fragmented complex relationship networks
Directly stated as a limitation of existing approaches in the abstract
partial
A schema discovery module dynamically infers query-specific minimal joinable schemas to capture essential entities and relationships
Directly stated as a core component of the method in the abstract
partial
An extraction module transforms unstructured text into semantically coherent relational tables, enhanced by error-aware correction mechanisms to reduce extraction errors
Directly stated as a core component of the method in the abstract
partial
A reasoning module performs multi-hop relational reasoning over structured tables, leveraging schema awareness to efficiently align cross-document entities and aggregate evidence
Directly stated as a core component of the method in the abstract
partial
precise fact localization via SQL-powered indexing
Directly stated as an advantage of the proposed method in the abstract
partial
mitigated LLM attention diffusion via structured representation
Directly stated as an advantage of the proposed method in the abstract
partial
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DocSage is an advanced framework for multi-document multi-entity question answering that enhances relational reasoning and information extraction.
Segment
Multi-Document Question Answering
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