$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction explores A framework that reduces inference latency in Diffusion Large Language Models by minimizing spatial and temporal redundancy during decoding.. Commercial viability score: 6/10 in LLM Inference Optimization.

Tier 3 — ExploratoryCoverage 50%Cost unknown

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Updated 4 days ago computingPending verification refs / 3 sources / Verification pending

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Canonical route: /paper/r-2-dllm-accelerating-diffusion-large-language-models-via-spatio-temporal-redundancy-reduction

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Proof freshness: fresh
Proof status: unverified
Display score: 6/10
Last proof check: 2026-04-22
Score updated: 2026-04-22
Score fresh until: 2026-05-22
References: 0
Source count: 3
Coverage: 50%

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$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

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{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2604.18995"
  }
}

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  "paper_ref": "r-2-dllm-accelerating-diffusion-large-language-models-via-spatio-temporal-redundancy-reduction",
  "topic_slug": null,
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Subject: $R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

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$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

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$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

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$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

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