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paperarXivTrust 82 · PrimaryPublished yesterdayLive · 1h ago

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

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 pages based on a query, and then iteratively understand information within those pages. However, these methods typically rely on fixed workflows and lack the ability to dynamically scale computation at test time, often leading to insufficient evidence. To address this, we propose D2-ScaleAgent, an agentic framework that introduces a dual-dimensional scaling paradig

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  • FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B

    Fuzzy title match (0.73): “D2-ScaleAgent: Dual-Dimensional Scaling for Long Document Un” ≈ “AgentCore-8B”

  • FuzzySimilar title/name (fuzzy) · 87%SWE-agent/SWE-agent

    Fuzzy title match (0.94): “D2-ScaleAgent: Dual-Dimensional Scaling for Long Document Un” ≈ “SWE-agent/SWE-agent”

  • FuzzySimilar title/name (fuzzy) · 87%zhayujie/CowAgent

    Fuzzy title match (0.94): “D2-ScaleAgent: Dual-Dimensional Scaling for Long Document Un” ≈ “zhayujie/CowAgent”

  • FuzzyOverlapping authors or contributors · 62%ray-project/ray

    Shared author/contributor keys: wang

  • FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow

    Shared author/contributor keys: wang

  • FuzzySimilar title/name (fuzzy) · 59%NousResearch/hermes-agent

    Fuzzy title match (0.73): “D2-ScaleAgent: Dual-Dimensional Scaling for Long Document Un” ≈ “NousResearch/hermes-agent”

  • LinkedLinked via arxiv author · 85%Junshao Zhang

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

  • LinkedLinked via arxiv author · 85%Longrong Yang

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

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