DepthWeave-KV achieves 8.3x KV cache memory reduction with near-full-cache task quality by factorizing key-value states across transformer layers using shared bases and token-adaptive residuals.
The modern mathematics of deep learning
3 Pith papers cite this work. Polarity classification is still indexing.
years
2026 3representative citing papers
DMK extended to rectangular cuboids with arbitrary periodicity via localized octree evaluations on cubical tilings and Fourier-space root-level summation with truncated kernels for reduced periodicity.
A frozen video diffusion backbone augmented with low-rank temporal adapters and a recursive prompt bank outperforms prior long-video generation methods on six benchmarks while tuning only 3.8% of parameters.
citing papers explorer
-
DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compression
DepthWeave-KV achieves 8.3x KV cache memory reduction with near-full-cache task quality by factorizing key-value states across transformer layers using shared bases and token-adaptive residuals.
-
Fast summation on rectangular cuboids with arbitrary periodicity in the DMK framework
DMK extended to rectangular cuboids with arbitrary periodicity via localized octree evaluations on cubical tilings and Fourier-space root-level summation with truncated kernels for reduced periodicity.
-
Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation
A frozen video diffusion backbone augmented with low-rank temporal adapters and a recursive prompt bank outperforms prior long-video generation methods on six benchmarks while tuning only 3.8% of parameters.