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SFM Beats Transformers on Long Sequences

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πŸ¦™Read original on Reddit r/LocalLLaMA
#non-transformer#long-sequence#state-slots#benchmarkstate-flow-machinestate-flow-machinesfmascend-910huawei

πŸ’‘Non-transformer holds 62% acc where LMs crash at long seqsβ€”new arch!

⚑ 30-Second TL;DR

What Changed

62% acc at 4x length (40 ops) vs 2-3% for transformers

Why It Matters

Challenges transformer dominance for long-sequence stateful tasks like process simulation. Could inspire efficient on-device architectures beyond attention limits.

What To Do Next

Replicate SFM benchmark on Ascend NPU to test long-seq generalization.

Who should care:Researchers & Academics

Key Points

  • β€’62% acc at 4x length (40 ops) vs 2-3% for transformers
  • β€’Explicit addressable state slots updated via learned gates
  • β€’961K params; benchmark extrapolates to 320 ops
  • β€’Inspired by DeltaNet, Mamba but more explicit
πŸ“°

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Original source: Reddit r/LocalLLaMA β†—

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