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cs.CL2026
SPARE: Fast Linear Attention Decoding with Compact Recurrent States
Ke Wan, Zaiyi Zheng, Chen Chen
Linear attention reduces the quadratic sequence-length complexity of softmax attention by maintaining fixed-size recurrent states. However, these states scale quadratically with th…
cs.CL2026
RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models
Ke Wan, Chen Chen
Recurrent-depth language models improve inference-time reasoning by repeatedly applying a shared network to refine internal representations, but the additional recurrent computatio…
cs.CL2026
MORSE: Multi-Context Ordering via Reverse Scoring for Evidence-Preserving Compression
Ke Wan, Yifan Wang, Liheng Lai +1
Retrieval-augmented generation often relies on multiple retrieved contexts that contain substantial redundancy, motivating context compression to preserve useful information under…