Xingyu Zhu

The work names and systematically characterizes phase sensitivity in models with chunked KV-cache compression: the same information is easier or harder to retrieve depending on its phase relative to compression-window boundaries, with long-context retrieval accuracy in the DeepSeek-V4 family differing by up to 40.2 percentage points across phases at 128K context; the authors reproduce the effect by pretraining 23 compressed models from scratch (Qwen3-0.6B backbone, 100B tokens), use KV-head and layer mean-replacement knockouts to reveal phase specialization, and prove in an idealized induction model that gradient flow drives compression gates toward static preferences, showing that average benchmark scores can conceal periodic positional failures.

The work names and systematically characterizes phase sensitivity in models with chunked KV-cache compression: the same information is easier or harder to retrieve depending on its phase relative to compression-window boundaries, with long-context retrieval accuracy in the DeepSeek-V4 family differing by up to 40.2 percentage points across phases at 128K context; the authors reproduce the effect by pretraining 23 compressed models from scratch (Qwen3-0.6B backbone, 100B tokens), use KV-head and layer mean-replacement knockouts to reveal phase specialization, and prove in an idealized induction model that gradient flow drives compression gates toward static preferences, showing that average benchmark scores can conceal periodic positional failures.