arXiv AI
ConvergeFlow: Language Flow with Provable Convergence to Token Embeddings
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arXiv Machine Learning
Sep 30
Rethinking Soft Tokens for Parallel Decoding in Diffusion Language Models
arXiv:2609.37391v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable parallel generation by predicting and committing multiple tokens at each denoising step, yet they can generate...
arXiv AI
Jun 29
ELF: Embedded Language Flows
arXiv:2605. 10938v2 Announce Type: replace-cross Abstract: Diffusion and flow-based models have become the de facto approaches for generating continuous data, e.
arXiv AI
Oct 7
SDFlow: Similarity-Driven Flow Matching for Time Series Generation
arXiv:2605.05736v3 Announce Type: replace Abstract: Vector quantization (VQ) with autoregressive (AR) token modeling is a widely adopted and highly competitive paradigm for time-series generation. Ho...
arXiv Machine Learning
Oct 7
Denoising Hierarchical Representations: Joint Continuous Diffusion for Language Modeling
The paper introduces Hierarchical Continuous Diffusion Language Models (H-CDLMs), a framework that jointly diffuses multiple token modalities—individual tokens and coarser clusters of token embeddings—to enhance continuous diffusion language models. Applied to the CoBit architecture, the resulting H-CoBit achieves significant empirical gains, improving MAUVE scores and achieving lower generative perplexity on LM1B and OWT, while also outperforming prior continuous diffusion models on GSM8K. The approach generalizes to other continuous generative paradigms, as shown by consistent improvements when applied to the flow matching model FLM.
arXiv AI
Sep 1
Semantic Flow Regularization: Teaching LLMs to Generate Diverse Yet Coherent Responses
arXiv:2605.27971v2 Announce Type: replace-cross Abstract: When large language models are fine-tuned to generate persona- or tone-conditioned responses, their output diversity is severely limited--a f...
arXiv AI
Jun 9
End-to-End Training for Discrete Token LLM based TTS System
arXiv:2606. 09234v1 Announce Type: cross Abstract: Recent state-of-the-art (SOTA) text-to-speech (TTS) systems typically adopt a cascaded pipeline consisting of a speech tokenizer, an autoregressive large language model (LLM), and a diffusion based flow-matching (FM) model, with these components trained independently.