TLDR Tech / AI
MiniMax M3: How Sparse Attention Makes Long-Horizon Agents Practical
Long-horizon tasks are demanding because the context never stops growing. The longer an agent works, the more expensive each step becomes. MiniMax Sparse Attention changes what the model reads at each step, which results in predictable cost at any length and cheap long context with minimal quality cost. It makes long context cheap and fast enough to sit inside iterative systems that need to keep state over time.
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