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SeKV is a resolution-adaptive KV cache method that organizes context into entropy-guided semantic spans stored across a GPU-CPU hierarchy, enabling selective token-level reconstruction during decoding while reducing GPU memory by 53.3% versus full caching at 128K context.
Proposes treating semantic compression as a diffusion noise function for handling massive context beyond model windows, using multi-pass reading at decreasing compression levels. Untrained-model experiments show components work in isolation but the full chain needs training to resolve binding bottleneck.
Introduces SimpleMem, an efficient memory framework for LLM agents that uses semantic lossless compression to improve accuracy and reduce token consumption, achieving 26.4% F1 improvement and up to 30x reduction in inference-time token usage.