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StrideDiffusion: Accelerating Diffusion Models for Time-series Generation

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1 SIGNALFIRST DETECTED 24 July 2026
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14 Apr
New method allows to convert auto-regressive models into diffusion models with a >2x speedup, fully compatible with existing inference stack

<!-- SC_OFF --><div class="md"><p>If the claims presented in the paper are true, this will be very big for multi-user local inference</p> </div><!-- SC_ON --> &#32; submitted by

OPINION · HOW · LocalLLaMAOPEN STORY
8 May
Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs

arXiv:2605.04215v1 Announce Type: cross Abstract: Diffusion-based Large Language Models (D-LLMs) represent a promising frontier in generative AI, offering fully parallel token

REPORT · WHAT · ArXiv CS.AIOPEN STORY
13 Apr
Advantage-Guided Diffusion for Model-Based Reinforcement Learning

arXiv:2604.09035v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) with autoregressive world models suffers from compounding errors, whereas diffusion world

REPORT · WHEN · ArXiv CS.AIOPEN STORY
6 Oct
Discrete Diffusion for Large Graph Generation via Structural Candidate Restriction

arXiv:2610.04056v1 Announce Type: new Abstract: Synthesizing realistic graphs at scale is vital when the graphs of interest are large and real-world samples are limited or

REPORT · WHEN · ArXiv CS.AIOPEN STORY
10 Apr
FVD: Inference-Time Alignment of Diffusion Models via Fleming-Viot Resampling

arXiv:2604.06779v1 Announce Type: new Abstract: We introduce Fleming-Viot Diffusion (FVD), an inference-time alignment method that resolves the diversity collapse commonly

REPORT · HOW · ArXiv CS.AIOPEN STORY
26 Jun
ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration

arXiv:2606.26769v1 Announce Type: new Abstract: The adoption of powerful diffusion models is hindered by their significant inference latency. Recent ``cache-then-forecast''

REPORT · HOW · ArXiv CS.AIOPEN STORY
12 May
SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference

arXiv:2605.08835v1 Announce Type: new Abstract: The expansion of Artificial Intelligence-generated content service requires diffusion model serving to simultaneously achieve high

REPORT · WHAT · ArXiv CS.AIOPEN STORY
9 Jul
D2PO: Optimizing Diffusion Samplers via Dynamic Preference

arXiv:2607.06609v1 Announce Type: cross Abstract: We propose D2PO (Dynamic Direct Preference Optimization), a principled framework for optimizing diffusion sampling policies with

REPORT · HOW · ArXiv CS.AIOPEN STORY
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