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D2-ScaleAgent framework enhances long document understanding

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced D2-ScaleAgent, a novel framework designed to enhance the understanding of long and visually rich documents. This agentic system employs a dual-dimensional scaling paradigm, dynamically adjusting retrieval and reasoning processes based on query difficulty. It features a Verifier agent that manages a continuously updated evidence bank, enabling outward scaling for expanded retrieval and inward scaling for fine-grained reasoning with adaptive pruning and sub-agent selection. Experiments show D2-ScaleAgent's effectiveness on benchmarks like MMLongBench-Doc and LongDocURL. AI

IMPACT This framework could improve how AI systems process and reason over lengthy, complex documents, enhancing applications in research and information retrieval.

RANK_REASON The cluster contains a research paper detailing a new framework for document understanding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

D2-ScaleAgent framework enhances long document understanding

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The cluster contains a research paper detailing a new framework for document understanding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hao Zhang, Longrong Yang, Lunhao Duan, Ziyang Wang, Qing-Guo Chen, Shanshan Zhao ·

    D2-ScaleAgent: Dual-Dimensional Scaling for Long Document Understanding

    arXiv:2608.16417v1 Announce Type: new Abstract: Multi-modal retrieval-augmented generation (RAG) is a key technique for visually rich long document understanding. Existing multi-modal RAG methods are progressively advancing toward multi-agent systems: they first retrieve relevant…

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