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  3. DOS: Dependency-Oriented Sampler for Masked Diffusion Langua
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DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models

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Viability
0.0/10

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Evidence Receipt

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

Claims: 0

References: 0

Proof: pending

Distribution: unknown

Source paper: DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models

PDF: https://arxiv.org/pdf/2603.15340v1

First buyer signal: unknown

Distribution channel: unknown

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Dimensions overall score 7.0

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Builds On This
Dependency-Aware Parallel Decoding via Attention for Diffusion LLMs
Score 2.0down
Builds On This
Unifying Masked Diffusion Models with Various Generation Orders and Beyond
Score 6.0down
Builds On This
CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think
Score 5.0down
Builds On This
Confidence-Based Decoding is Provably Efficient for Diffusion Language Models
Score 3.0down
Prior Work
DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention
Score 7.0stable
Prior Work
Search or Accelerate: Confidence-Switched Position Beam Search for Diffusion Language Models
Score 7.0stable
Higher Viability
Mask Is What DLLM Needs: A Masked Data Training Paradigm for Diffusion LLMs
Score 8.0up
Higher Viability
TRIMS: Trajectory-Ranked Instruction Masked Supervision for Diffusion Language Models
Score 8.0up

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