Muir: Linear Attention with Sensitivity-Allocated Gating and Matched-Filter Readout
Muir is a linear attention architecture that allocates gate capacity by sensitivity, adds a matched-filter readout gate that re-weights state rows, and uses a parallel update designed for stable training at large chunk sizes.
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- Primary sourceHugging Face
- Discovery linkHugging Face
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Muir is a linear attention architecture that allocates gate capacity by sensitivity, adds a matched-filter readout gate that re-weights state rows, and uses a parallel update designed for stable training at large chunk sizes. Per the paper's abstract, across 166M-1.34B parameters Muir lowers perplexity by 1.8-2.0% over GDN-2 and 2.5-3.6% over Transformer, and at 1.34B it outperforms both baselines on multi-hop and multi-needle recall. It reports matching GDN-2 throughput and training 2.7x faster than Transformer at 32K.
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Muir fuses all gates into a sensitivity-allocated low-rank projection and reinvests saved parameters in a wider feed-forward network.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseAcross 166M-1.34B parameters, Muir lowers perplexity by 1.8-2.0% over GDN-2 and 2.5-3.6% over Transformer.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseAt 1.34B, Muir outperforms both baselines on multi-hop and multi-needle recall.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseMuir reports near-100% passkey retrieval to 1M tokens with static readout and matches GDN-2 throughput.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
If the reported results hold, a linear attention design that keeps constant-memory inference while improving recall and training speed could matter for long-context language modeling. The gains are modest in perplexity terms and reported at a limited set of model sizes.
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