One compression tool slashes the memory needed for AI chats by a massive factor of 914,000.
April 20, 2026
Original Paper
Sequential KV Cache Compression via Probabilistic Language Tries: Beyond the Per-Vector Shannon Limit
arXiv · 2604.15356
The Takeaway
Probabilistic language tries treat the KV cache as a structured sequence rather than a collection of independent vectors. This approach bypasses the traditional Shannon limit that usually restricts how much data can be squeezed. Current hardware bottlenecks for large models often stem from the massive memory footprint of these caches during long interactions. Achieving a million-fold increase in efficiency means huge models can now fit onto mobile phones or low-power edge devices. Engineers can finally stop sacrificing context window size for the sake of hardware constraints.
From the abstract
Recent work on KV cache quantization, culminating in TurboQuant, has approached the Shannon entropy limit for per-vector compression of transformer key-value caches. We observe that this limit applies to a strictly weaker problem than the one that actually matters: compressing the KV cache as a sequence. The tokens stored in a KV cache are not arbitrary floating-point data -- they are samples from the exact formal language the model was trained on, and the model is by construction a near-optimal