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[ARTICLE · art-28436] src=andlukyane.com ↗ pub= topic=large-language-models verified=true sentiment=· neutral

MiniMax Sparse Attention: Per-Group Block Selection for Cheap Million-Token Inference

MiniMax introduced a sparse attention mechanism that selects per-group blocks to enable cost-effective inference on million-token sequences. The technique reduces computational overhead while maintaining model quality, potentially lowering the barrier for long-context AI applications.

read1 min views1 publishedJun 15, 2026

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source & further reading
andlukyane.com — original article Testing MiniMax M3 on real tasks: repo refactor, screenshot debugging, and Spotify recommendations Book Review: 50 ML Projects to Understand LLMs Gamma-World: Simplex Agent Encoding and Hub Attention for Multi-Agent World Models
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