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Pei-Song Wang

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#artificial intelligence Preprint Oct 2026

Let the Gate Decide: Per-Lookup Precision for Quantized Conditional Memory in LLMs

Conditional memory layers, such as DeepSeek's Engram, attach large hashed n-gram tables to a language model, and removing them from DeepSeek-V4.1-Flash costs 36.8 PopQA points. Low-bit quantization loses long-tail facts first, and the standard remedy, giving more bits to frequently accessed rows, backfires: refining lo...

Tian-Qi Chen, Xun-Chao Li, Li Xie et al. · 0 citations
#artificial intelligence Preprint Sep 2026

You Only Need 2/3 of the Chosen Experts: An Empirical Study of Dynamic Expert Pruning in Fine-Grained MoE LLMs

It is found that expert selection is far more redundant than the field's operating points assume, and how much expert computation fine-grained MoEs can dispense with is revealed, and when dynamic allocation earns its complexity is established, informing both practical deployment and future pruning methods.

Yuan-Teng Chen, Qi-Wei Lai, Tian-Qi Chen et al. · 1 citation
#artificial intelligence Preprint Sep 2026

From Splats to Silicon: Rethinking Computational Efficiency of 3DGS

3D Gaussian splatting (3DGS) represents scenes with explicit primitives and supports real-time novel-view synthesis, yet its system efficiency varies substantially across scenes, viewpoints, rendering paths, and platform constraints. Existing studies pursue efficiency through representation and algorithm design, GPU ru...

Min-Nan Pei, Qi-Wei Dong, Yi-Han Zhou et al. · 0 citations

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