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
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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.
Frequency-guided inter-layer KV sharing with logit-aware head routing nearly matches full-cache long-context accuracy at about 3.9× lower peak KV memory.
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.
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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.
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FreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inference
Frequency-guided inter-layer KV sharing with logit-aware head routing nearly matches full-cache long-context accuracy at about 3.9× lower peak KV memory.