Fixed-Capacity Causal Models with Parallel Recovery for GPU Lossless Compression

arXiv:2610.02236v4 Announce Type: replace Abstract: GPU lossless compression requires both compact complete frames and fast checked recovery. We evaluate fixed-capacity causal byte-bank representations under a shared 512-row probability budget, select a single representation via a pre-payload complete-cost proxy charging both empirical cross-entropy and compressed model tables, and align two rANS states per 8\,KiB chunk with the causal byte banks. Independent byte banks decode concurrently, whereas intra-word conditional banks decode in two ordered phases.