CoLA-Flow Policy encodes action sequences into a continuous latent space and learns an explicit flow there, yielding near-single-step inference with up to 93.7% smoother trajectories and 25-point higher task success than raw-action flow baselines.
Denoising diffusion implicit models
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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Hybrid two-stage diffusion transformer architecture for instruction-guided audio editing via rectified flow that performs joint attention at low resolution then alternates joint and cross-attention at high resolution for improved performance and efficiency.
NC-Diffusion matches quantization noise to the diffusion forward process, adds an adaptive frequency filter and zero-shot enhancement, and reports superior fidelity on benchmarks.
citing papers explorer
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CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation
CoLA-Flow Policy encodes action sequences into a continuous latent space and learns an explicit flow there, yielding near-single-step inference with up to 93.7% smoother trajectories and 25-point higher task success than raw-action flow baselines.
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Hybrid Diffusion Transformer for Instruction-Guided Audio Editing via Rectified Flow
Hybrid two-stage diffusion transformer architecture for instruction-guided audio editing via rectified flow that performs joint attention at low resolution then alternates joint and cross-attention at high resolution for improved performance and efficiency.
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A Noise Constrained Diffusion (NC-Diffusion) Framework for High Fidelity Image Compression
NC-Diffusion matches quantization noise to the diffusion forward process, adds an adaptive frequency filter and zero-shot enhancement, and reports superior fidelity on benchmarks.