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  1. Home
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  3. Boost LLM Reasoning: Stitching Diffusion Thoughts for Faster
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Boost LLM Reasoning: Stitching Diffusion Thoughts for Faster, Accurate

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Evidence Receipt

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

Claims: 0

References: 38

Proof: no_code

Distribution: unknown

Source paper: Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

PDF: https://arxiv.org/pdf/2602.22871v1

First buyer signal: unknown

Distribution channel: unknown

Last proof check: 2026-03-18T22:00:57.959969+00:00

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Dimensions overall score 8.0

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EndoCoT: Scaling Endogenous Chain-of-Thought Reasoning in Diffusion Models
Score 7.0down
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Beyond Mode Elicitation: Diversity-Preserving Reinforcement Learning via Latent Diffusion Reasoner
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Efficient Paths and Dense Rewards: Probabilistic Flow Reasoning for Large Language Models
Score 6.0down
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Batched Contextual Reinforcement: A Task-Scaling Law for Efficient Reasoning
Score 7.0down
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Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning
Score 7.0down
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Improving reasoning at inference time via uncertainty minimisation
Score 7.0down
Builds On This
Reasoning with Autoregressive-Diffusion Collaborative Thoughts
Score 2.0down
Prior Work
Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages
Score 8.0stable

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