ATMA combines polar attention (direction + bounded-magnitude channels) with gated-delta recurrent compression to achieve length-invariant perplexity and >90% needle retrieval at 64K tokens after 2K training.
Weak-sigreg: Covariance regularization for stable deep learning
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ATMA: Length-Invariant Language Modeling via Polar Attention and Gated-Delta Compression Memory
ATMA combines polar attention (direction + bounded-magnitude channels) with gated-delta recurrent compression to achieve length-invariant perplexity and >90% needle retrieval at 64K tokens after 2K training.