3 papers
cs.PF2026
Launch-Bound and Substitutable: Why Three Inference Optimizations Fail to Pay Off in Mixture-of-Experts Models
Gokulakannan Sakthivel, Jerry Wu, Amogh Rajendra +1
Mixture-of-Experts (MoE) models route each token to a few of many expert networks, and that routing is data-dependent in a way standard inference optimizations do not expect. This…
cs.CL2026
Silent Failures in Quantized LLM Reasoning: A Taxonomy-Based Analysis of Hollow Convergence and Failure Mode Shifts
Renuka Oladri, Mohan Vamsi Varadaraju Priya, Jerry Wu
We show that post-training quantization can silently alter how large language models reason even when task accuracy is preserved. Using a six-category failure taxonomy validated by…
cs.AR2026
Enabling Spatially Fine-Grained DVFS in Neural Processing Units for Energy-Efficient LLM Serving
Yuqi Xue, Jerry Wu, Corey Yu +1
As neural processing units (NPUs) evolve rapidly to accommodate the ever-increasing compute demand of large language models (LLMs), their power consumption is becoming a limiting f…