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cs.LG2026
Operator Fusion for LLM Inference on the Tensix Architecture
Qingbo Wu, Ke Li, Wenzhu Wang +3
This study addresses on-device inference bottlenecks of Transformer models on Tenstorrent's Tensix architecture and proposes an operator fusion strategy that enhances data locality…
cs.LG2026
Calibrating Overconfidence Without Sacrificing Confidence: Probe-Conditioned Head Intervention for LLMs
Ke Li, Chongzhe Zhang, Zifan Zeng +3
Large language models often express high confidence in answers that are wrong. Standard calibration remedies typically act globally or at the score level, reducing unwarranted conf…
cs.LG2026★ 1 cited
Mamba-3: Improved Sequence Modeling using State Space Principles
Aakash Lahoti, Kevin Y. Li, Berlin Chen +5
Scaling inference-time compute has emerged as an important driver of LLM performance, making inference efficiency a central focus of model design alongside model quality. While the…