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  3. Efficient Document Parsing via Parallel Token Prediction
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Efficient Document Parsing via Parallel Token Prediction

Stale18d ago
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0.0/10

Compared to this week’s papers

Stale evidence

Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: stale

Source paper: Efficient Document Parsing via Parallel Token Prediction

PDF: https://arxiv.org/pdf/2603.15206v1

Repository: https://github.com/flow3rdown/

Source count: 0

Coverage: 50%

Last proof check: 2026-03-18T22:54:38.628Z

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

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Efficient Document Parsing via Parallel Token Prediction

Overall score: 7/10
Lineage: 8a62c55b91a4…
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Canonical Paper Receipt

Last verification: 2026-03-18T22:54:38.628Z

Freshness: stale

Proof: unverified

Repo: active

References: 0

Sources: 0

Coverage: 50%

Missingness
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Unknowns
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  • Workspace mode blends saved sources, prior evidence queries, and linked papers.

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Dimensions overall score 7.0

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Keep exploring

Builds On This
Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?
Score 5.0down
Builds On This
P-EAGLE: Parallel-Drafting EAGLE with Scalable Training
Score 5.0down
Prior Work
Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training
Score 7.0stable
Prior Work
GLM-OCR Technical Report
Score 7.0stable
Prior Work
Efficient Training-Free Multi-Token Prediction via Embedding-Space Probing
Score 7.0stable
Prior Work
PP-OCRv5: A Specialized 5M-Parameter Model Rivaling Billion-Parameter Vision-Language Models on OCR Tasks
Score 7.0stable
Higher Viability
Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing
Score 8.0up
Competing Approach
MDPBench: A Benchmark for Multilingual Document Parsing in Real-World Scenarios
Score 7.0stable

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