ATMA: Long-Context Language Modeling via Polar Attention and Gated-Delta Compression Memory
Length extrapolation in language models involves competing objectives: retrieval fidelity, long-document likelihood, short-context quality, and inference cost. We present ATMA, a 378M-parameter hybrid recipe that combines Polar Attention with gated-delta recurrent memory, and study these objectives as a Pareto problem rather than claiming general architectural dominance. Polar Attention separates a normalized direction channel from a bounded participation-ratio magnitude channel. We select the recipe with a complete 120-cell, 1B-token factorial sweep, then train matched NoPE, RoPE, and Polar variants for 9.816B tokens at length 2K and evaluate them through 256K. Across the factorial, memo...
| Authors | Habibullah Akbar |
|---|---|
| Primary category | cs.LG |
| Published | 23 June 2026 |
| Last updated at source | 12 August 2026 |
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