alphaXiv(@askalphaxiv):"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 attending to everything. Their resulting 1T-parameter MoE model (w 52B active) scores 41% on Terminal-Bench 4.0 and 19.7% on FrontierCode 2.0, and its NVFP4-quantized checkpoint fits on four GB300 GPUs.

2026.11.03 07:43

"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 attending to everything. Their resulting 1T-parameter MoE model (w 52B active) scores 41% on Terminal-Bench 4.0 and 19.7% on FrontierCode 2.0, and its NVFP4-quantized checkpoint fits on four GB300 GPUs.