arXiv Computation and Language
Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval
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arXiv AI
Jun 29
Speculative Refinement: A Hybrid Autoregressive Diffusion Decoding Strategy and Its Behavior Across Benchmarks
arXiv:2606. 27474v1 Announce Type: cross Abstract: How should we evaluate generation systems that combine autoregressive (AR) and diffusion decoding?
arXiv AI
Oct 1
Distilling Diffusion Score Discrepancy for Efficient Training Data Attribution
The paper introduces a new method for training data attribution in diffusion models called TID, which uses a local score discrepancy measure and can be estimated without retraining. It further distills this approach into TIDE, a forward‑only student that reproduces the teacher’s rankings using internal activations, achieving comparable accuracy at dramatically lower query cost. Experiments on CIFAR‑10, ArtBench‑10, and MS‑COCO show that TID outperforms existing methods and TIDE attributes samples in milliseconds, faster than generation itself.
arXiv Machine Learning
Jun 5
Self-Augmenting Retrieval for Diffusion Language Models
arXiv:2606. 06474v1 Announce Type: cross Abstract: Discrete diffusion language models generate text by iteratively denoising an entire response in parallel.
arXiv AI
Jun 10
Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models
arXiv:2606. 10829v1 Announce Type: cross Abstract: Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration, but this parallelism is fragile: positions that are individually confident may be unsafe to commit together when their predictions are coupled.
arXiv Machine Learning
Jun 11
Re-evaluating Confidence Remasking in Masked Diffusion Language Models
arXiv:2606. 12232v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faster inference via parallel token generation.
arXiv Machine Learning
Jun 15
Neither Parallel Nor Sequential: How DiffusionGemma Actually Commits Tokens
arXiv:2606. 14620v1 Announce Type: new Abstract: Open diffusion language models are marketed as parallel, non-autoregressive decoders, yet the order in which a shipped checkpoint actually commits its tokens is almost never measured.