arXiv:2606.13392v1 Announce Type: new Abstract: Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code reasoning, and persistent memory all require the model to jointly attend over hundreds of thousands to millions of tokens, yet the quadratic cost of softmax attention makes this untenable at deployment scale. We introduce MiniMax Sparse Attention (MSA), a blockwise sparse attention built upon Grouped Query Attention (GQA). A lightweight Index Branch scores key-value blocks and independently selects a Top-k subset for each GQA group,
The quadratic cost of softmax attention has become a critical bottleneck for deploying frontier LLMs requiring ultra-long contexts, making efficient sparse attention methods highly relevant.
This development addresses a fundamental limitation in large language models, enabling more sophisticated and autonomous AI applications that require processing vast amounts of information.
The ability to handle hundreds of thousands to millions of tokens efficiently shifts the practical limits of LLM context windows, fostering more capable agentic workflows and complex reasoning tasks.
- · LLM developers
- · AI agent platforms
- · Cloud AI providers
- · Software developers
- · Inefficient AI architectures
- · Data centers with limited compute
Frontier LLMs can now process and reason over significantly larger data sets, such as entire code repositories or persistent memory streams.
This improved context window capability accelerates the development and deployment of advanced AI agents capable of more complex, multi-step tasks.
The reduced computational cost for long contexts could lead to more democratized access to powerful LLMs, lowering operational expenses for new AI applications.
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Read at arXiv cs.AI