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.