paper published exclusively on alphaXiv
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"Mistral Large 4" paper published exclusively on alphaXiv Their latest technical report shares that aggressively reducing KV-cache memory can hurt an LLM's agentic capabilities, even when its language modeling performance looks unchanged. Mistral initially reduced KV-cache memory by 16x, but they found that this hurts long-context agentic tasks, where models need to retrieve information from much earlier context. To fix this, Mistral restored full-context attention to more layers, then adopted DeepSeek Sparse Attention to select the 2,048 most relevant tokens across the context instead of
Posted by alphaXiv (60k followers) 1 h ago · 65 likes · 2.4k views · view the original post on X. Kept by the Dev Radar as AI dev tools.
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