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The paper introduces an audit method for prefix invariance in attention, state-space, and hybrid sequence models, demonstrating that attention masks fail to ensure causality, with 192 faults detected across multiple model checkpoints.
This paper systematically analyzes the role of efficient attention modules in hybrid language model architectures, finding that different designs converge in long-context performance under sufficient training, and that long-range retrieval is primarily carried by full attention while efficient attention shapes the optimization trajectory, revealing a 'Large-Window Laziness' phenomenon.
This paper introduces sparse prefix caching for hybrid and recurrent LLMs, which stores recurrent states at a limited set of checkpoint positions to avoid dense caching while minimizing recomputation. The method outperforms standard heuristics on real-world data, especially when requests share substantial but non-identical prefixes.