Enterprise AI Analysis
DocSeeker: Revolutionizing Long Document Understanding with Structured Visual Reasoning
DocSeeker addresses the core challenges of low Signal-to-Noise Ratio (SNR) and supervision scarcity in long document understanding by introducing a novel Analysis-Localization-Reasoning (ALR) paradigm and a two-stage training framework. This enables MLLMs to robustly handle complex, lengthy documents.
Executive Impact
DocSeeker achieves a remarkable 30-60% performance gain across all five document VQA benchmarks compared to the Baseline, demonstrating superior capabilities. Its robust generalization from short-page training to ultra-long documents effectively mitigates performance decay associated with long-sequence inputs, proving its enterprise readiness for complex document workflows.
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Deep Analysis & Enterprise Applications
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Reasoning Paradigm (ALR)
Two-Stage Training
Resolution Allocation (EGRA)
Synergy with RAG
DocSeeker's Structured ALR Workflow
Question Analysis (User Intent)
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Evidence Localization (Relevant Pages)
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Reasoning Process (Synthesize Info)
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Final Answer (with Page IDs)
High Interpretability
The Analysis-Localization-Reasoning (ALR) paradigm mandates explicit evidence grounding, yielding high interpretability and allowing users to easily verify answers by referring to cited pages. This counteracts noise in long visual inputs.
| Stage | Goal | Method | Key Outcome |
|---|---|---|---|
| Stage I: SFT | Inject ALR Paradigm | Supervised Fine-Tuning on Distilled ALR CoT Data | Acquires structured reasoning, initial capabilities |
| Stage II: EviGRPO | Optimize Localization & Reasoning | Evidence-aware Group Relative Policy Optimization (RL) | Achieves precise evidence grounding, robust generalization |
Efficient Data Distillation
DocSeeker employs an efficient knowledge distillation strategy using Gemini-2.5-Flash as a teacher model, generating high-quality ALR Chain-of-Thought (CoT) annotations without costly full-document prompting, boosting the distillation success rate to 67.3%.
EGRA Strategy
The Evidence-Guided Resolution Allocation (EGRA) strategy optimizes resource allocation by maintaining high resolution for ground-truth evidence pages and downsampling non-evidence pages (70% to lower resolution), significantly reducing input tokens and increasing the Signal-to-Noise Ratio (SNR) during training.
Robustness to Ultra-Long Documents
DocSeeker demonstrates remarkable robustness to ultra-long document reasoning. While baseline models suffer dramatic performance degradation as document length increases (e.g., from 34.5% to 13.9% accuracy), DocSeeker's performance remains largely stable, effectively mitigating performance decay and showcasing strong generalization capabilities. This capability is crucial for enterprise applications handling extensive documents.
Citation: Figure 3, Page 7
Natural Synergy
DocSeeker's strong localization capability makes it naturally synergistic with visual RAG systems. It resists noise interference from a large number of retrieved pages, enabling the retriever to perform coarse-grained filtering while DocSeeker conducts fine-grained reading and localization within the still-noisy results, significantly improving overall performance.
Overcoming the Top-K Dilemma
Visual RAG systems often face the 'top-k dilemma,' where a large 'k' (number of retrieved pages) ensures high recall but introduces noise, causing performance collapse in baseline models (Figure 4a). DocSeeker's ability to resist noise interference and precisely localize evidence within noisy contexts allows it to leverage RAG effectively, even with suboptimal 'k' values, transforming a weakness into a strength.
Citation: Figure 4, Page 7
Estimate Your Enterprise AI ROI
Understand the potential time and cost savings by automating document understanding with DocSeeker's advanced AI capabilities.
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Your AI Implementation Roadmap
A phased approach to integrate DocSeeker into your enterprise workflows for maximum impact and seamless adoption.
Phase 1: Discovery & Strategy Session
Identify core business challenges in document processing, define project scope, and outline AI integration strategy tailored to your enterprise needs.
Phase 2: Pilot Deployment & Refinement
Implement DocSeeker in a controlled pilot environment, validate performance on real-world documents, and fine-tune for optimal accuracy and efficiency.
Phase 3: Full-Scale Integration & Optimization
Seamlessly integrate DocSeeker across your enterprise workflows, scale its capabilities, and establish continuous monitoring for ongoing performance optimization and ROI maximization.
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