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MiniMax M3: How Sparse Attention Makes Long-Horizon Agents Practical
MiniMax M3 uses sparse attention to keep costs predictable, allowing AI agents to handle long-running, multi-step tasks without scaling costs linearly.
What: MiniMax M3 addresses the performance-cost wall of long-context models by employing sparse attention, which selectively processes data rather than calculating attention for every token in an ever-growing sequence.
Why it matters: Sparse attention techniques are becoming critical for moving LLMs from 'chatbots' to 'long-horizon agents' that can maintain state over hours of operation.
Decoder
- Sparse Attention: A mechanism that allows models to focus on relevant subsets of context rather than every single previous token, significantly reducing compute costs for long documents.
Original article
MiniMax M3: How Sparse Attention Makes Long-Horizon Agents Practical
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