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arXiv Machine Learning By Xuanchen Wang, Heng Wang, Weidong Cai

LatticeSMC: Where to Spend Inference-Time Compute in Chunked Sequence Generators

Read the original on arXiv Machine Learning →

The paper introduces LatticeSMC, a sampler for chunked sequence generators that balances inference-time compute across chunk indices and denoising steps using a Feynman‑Kac model on a two‑dimensional lattice. By resampling at chunk boundaries and, when scoring is free, within chunks, it achieves higher beat alignment and prompt adherence on music‑to‑dance diffusion and text‑to‑music generation while maintaining quality. The method also outperforms existing steering techniques on long‑range rewards and is preferred by human raters in most pairwise comparisons.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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