Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
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Diffusion- lm improves controllable text generation
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Continuous language diffusion works by entering high-margin decoder basins where frozen T5 embeddings recover 93-96% of native decisions and linear readouts reach 97.9% agreement, implying models should be evaluated as representation-decoder systems.
DiffuSeq adapts diffusion models to conditional sequence-to-sequence text generation and reports performance matching or exceeding strong baselines including pretrained language model systems while generating more diverse outputs.
Coupling Models enable single-step discrete sequence generation via learned couplings to Gaussian latents and outperform prior one-step baselines on text perplexity, biological FBD, and image FID metrics.
Saber improves both speed and accuracy of diffusion language models on code generation by dynamically adjusting unmasking steps and reverting low-confidence tokens via backtracking.
DiffuMeta uses diffusion transformers and algebraic language representations to generate diverse 3D shell metamaterials with targeted stress-strain responses under large deformations including buckling and contact.
Logit-KL Flow Matching recovers the flow-matching velocity field from conditional likelihood maximization and uses iterative denoise-re-noise sampling to improve perplexity and downstream metrics over prior NAR baselines on text and code tasks.
A single transformer combines language modeling loss and diffusion loss on mixed-modality data, scaling to 7B parameters and 2T tokens while matching specialized language and diffusion models.
An ensemble of stage-specialized text-to-image diffusion models improves prompt alignment over single shared-parameter models while preserving visual quality and inference speed.
A single-objective rectified flow variant uses neural ODEs trained by regression to monotonically decrease a fixed convex transport cost while preserving marginal distributions.
The paper proposes CDCD, a continuous-time and continuous-space diffusion framework for categorical data, and reports results on language modeling tasks.
A holistic survey of affective computing for intelligent agents covering emotion understanding via multimodal data, affective cognition, emotional expression synthesis, key challenges, and future directions emphasizing generative technologies.
citing papers explorer
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Continuous Language Diffusion as a Decoder-Interface Problem
Continuous language diffusion works by entering high-margin decoder basins where frozen T5 embeddings recover 93-96% of native decisions and linear readouts reach 97.9% agreement, implying models should be evaluated as representation-decoder systems.
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DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
DiffuSeq adapts diffusion models to conditional sequence-to-sequence text generation and reports performance matching or exceeding strong baselines including pretrained language model systems while generating more diverse outputs.
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Logit-KL Flow Matching: Non-Autoregressive Text Generation via Sampling-Hybrid Inference
Logit-KL Flow Matching recovers the flow-matching velocity field from conditional likelihood maximization and uses iterative denoise-re-noise sampling to improve perplexity and downstream metrics over prior NAR baselines on text and code tasks.
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Continuous diffusion for categorical data
The paper proposes CDCD, a continuous-time and continuous-space diffusion framework for categorical data, and reports results on language modeling tasks.