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Matching papers

Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning

166 upvotes · 3 SEP 2026 · Heng Wang, Jielin Qiu, Wenting Zhao et al.

This paper proposes a new method for managing the memory of large language models, called Random Attention, which improves their performance on tasks that require extended reasoning by reducing memory usage. Practitioners caring about efficient model deployment might find this approach useful.

language modelsKV cacheattention mechanismsmemory efficiencyreasoning tasks

STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization

96 upvotes · 29 SEP 2026 · Bingchen Yao, Haobo Xu, Haokun Lin et al.

This paper proposes a method to quantize recurrent states in deep learning models more efficiently, reducing memory usage without sacrificing accuracy. Practitioners might care about this research if they need to optimize memory usage in their models.

recurrent state quantizationdelta-rule modelslow-precision arithmeticmemory efficiencyGPU optimization