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TOPIC · ENTITY #9497

discrete diffusion models

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

COFFEE: future-aware guidance for discrete diffusion (arXiv:2609.35924v1)

The paper introduces COFFEE, a plug-and-play inference-time framework that guides discrete diffusion models with sequence-level objectives without enumerating completions. It combines a target-free carrier that absorbs marginal token distributions from the denoiser with a compiled finite-state model that records how token combinations affect the objective, supporting hard constraints and learned soft objectives and reporting strong control results across symbolic, language, and biological benchmarks with task-dependent quality/diversity trade-offs.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Spectral Feedback: test-time edit-position selection for discrete protein diffusion models

A new arXiv paper (arXiv:2609.30456v1) introduces Spectral Feedback, a model-agnostic algorithm that iteratively selects token edit-positions to re-mask and re-sample in discrete diffusion models, enabling the model to correct its own generations at test time. Applied to protein inverse folding with a protein stability reward oracle, the method increases the fraction of stable proteins by 32.3% for a pretrained model, 24.8% for Best-of-10 selection, and 5.8% for a state-of-the-art RL fine-tuned diffusion model.

7.0