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ARXIV:2604.02560 · LLM GENERATION · SUBMITTED 06 APR · 20:16 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02560LLM GENERATIONSUBMITTED 06 APR · 20:16 UTCFRESHNESS UNKNOWNLiran Ringel · Ameen Ali · Yaniv Romano · arXiv
Accelerate discrete diffusion language model text generation by predicting token dependencies to improve quality and speed.
Opportunity summary
Pain Accelerate discrete diffusion language model text generation by predicting token dependencies to improve quality and speed.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
Accelerate discrete diffusion language model text generation by predicting token dependencies to improve quality and speed. However, parallel decoding introduces a distributional mismatch: it approximates the joint conditional using a fully factorized product of…
Discrete diffusion language models (dLLMs) accelerate text generation by unmasking multiple tokens in parallel. However, parallel decoding introduces a distributional mismatch: it approximates the joint conditional using a fully factorized product of per-token marginals,…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Empirically, DEMASK achieves 1.7-2.2$\times$ speedup on Dream-7B while matching or improving accuracy compared to confidence-based and KL-based baselines.
LLM Generation moved forward this cycle; last verified April 2026. Public score 5.0/10.
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Accelerate discrete diffusion language model text generation by predicting token dependencies to improve quality and speed.
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10.48550/arXiv.2604.02560Accelerate discrete diffusion language model text generation by predicting token dependencies to improve quality and speed.
Abstract
Discrete diffusion language models (dLLMs) accelerate text generation by unmasking multiple tokens in parallel. However, parallel decoding introduces a distributional mismatch: it approximates the joint conditional using a fully factorized product of per-token marginals, which degrades output quality when selected tokens are strongly dependent. We propose DEMASK (DEpendency-guided unMASKing), a lightweight dependency predictor that attaches to the final hidden states of a dLLM. In a single forward pass, it estimates pairwise conditional influences between masked positions. Using these predictions, a greedy selection algorithm identifies positions with bounded cumulative dependency for simultaneous unmasking. Under a sub-additivity assumption, we prove this bounds the total variation distance between our parallel sampling and the model's joint. Empirically, DEMASK achieves 1.7-2.2$\times$ speedup on Dream-7B while matching or improving accuracy compared to confidence-based and KL-based baselines.
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Dimensions overall score 5.0
PROBLEM
Accelerate discrete diffusion language model text generation by predicting token dependencies to improve quality and speed. However, parallel decoding introduces a distributional mismatch: it approximates the joint conditional using a fully factorized product of per-token margin...
METHOD
Discrete diffusion language models (dLLMs) accelerate text generation by unmasking multiple tokens in parallel. However, parallel decoding introduces a distributional mismatch: it approximates the joint conditional using a fully factorized product of per-token marginals, which d...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Empirically, DEMASK achieves 1.7-2.2$\times$ speedup on Dream-7B while matching or improving accuracy compared to confidence-based and KL-based baselines.
WHY NOW
LLM Generation moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Accelerate discrete diffusion language model text generation by predicting token dependencies to improve quality and speed. However, parallel decoding introduces a distributional mismatch: it approximates the joint conditional using a fully factorized product of per-token marginals, which degrades output quality when selected tokens are strongly dependent.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Discrete diffusion language models (dLLMs) accelerate text generation by unmasking multiple tokens in parallel. However, parallel decoding introduces a distributional mismatch: it approximates the joint conditional using a fully factorized product of per-token marginals, which degrades output quality when selected tokens are strongly dependent.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Empirically, DEMASK achieves 1.7-2.2$\times$ speedup on Dream-7B while matching or improving accuracy compared to confidence-based and KL-based baselines.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Generation moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Accelerate discrete diffusion language model text generation by predicting token dependencies to improve quality and speed.
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LLM Generation
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5.0/10 public viability
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Technical feasibility
partial
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Run minimal reproduction from the Build Passport prototype path.
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