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LCA is proposed, which directly condenses context within MLA's latent space, where the representation is disentangled into semantic latent vectors and positional keys, and jointly reduces both computational cost and KV cache without adding parameters.
The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a growing body of work studies how models can refine their behavior on the fly by exploiting test-time information and additional computation...
Large language models (LLMs) have achieved remarkable breakthroughs across various applications. However, their architectures remain inefficient in pretraining due to two main limitations: (i) self-attention lacks an explicit inductive bias for locality, leading to redundant modeling of sequence-internal local informat...
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