GAPE augments RoPE with query- and key-dependent gates to stabilize attention and improve long-context performance in language models.
Exploring the limits of transfer learning with a unified text-to-text trans- former
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LaProx reformulates KV cache eviction as an output-aware matrix approximation, enabling a unified global token selection strategy that preserves LLM performance at 5% cache size across long-context benchmarks.
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Remember to Forget: Gated Adaptive Positional Encoding
GAPE augments RoPE with query- and key-dependent gates to stabilize attention and improve long-context performance in language models.