Polestar's arXiv paper proposes drift-aware cache calibration and token commitment to address inference efficiency challenges in diffusion LLMs.
Read the original at arxiv.org→arXiv:2607.14107v1 Announce Type: new Abstract: The inference efficiency of diffusion large language models (dLLMs) is constrained by two challenges: bidirectional attention precludes efficient KV-cache reuse, while...
Original headline: "Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs"