AI RESEARCH
Efficient Learned Data Compression via Dual-Stream Feature Decoupling
arXiv CS.LG
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ArXi:2604.07239v1 Announce Type: cross While Learned Data Compression (LDC) has achieved superior compression ratios, balancing precise probability modeling with system efficiency remains challenging. Crucially, uniform single-stream architectures struggle to simultaneously capture micro-syntactic and macro-semantic features, necessitating deep serial stacking that exacerbates latency. Compounding this, heterogeneous systems are constrained by device speed mismatches, where throughput is capped by Amdahl's Law due to serial processing.