10 papers
Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed
Haokun Lin, Kaijie Zhu, Haobo Xu +4
Small Language Models (SLMs) have emerged as a more efficient alternative to traditional Large Language Models (LLMs), offering promising potential in resource-constrained scenario…
Don't Regenerate, Debug: A Domain-Specific Agent for Repairing Near-Miss Hardware Operators
Yansong Sun, Shenxiu Wu, Siyuan Chen +6
Kernel generation for hardware accelerators such as GPUs and NPUs has become a proving ground for large language models (LLMs), and state-of-the-art systems raise correctness throu…
AgenticCANN: Automated Ascend C Operator Generation via Knowledge-Augmented Agentic Evolution
Junhao Qiu, Zidong Wang, Yansong Sun +3
The paper introduces AgenticCANN, a framework that uses large language models combined with knowledge‑augmented, stage‑adaptive agents to automatically generate and optimize Ascend…
DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization
Haokun Lin, Xinle Jia, Haobo Xu +7
The MXFP4 microscaling format, which partitions tensors into blocks of 32 elements sharing an E8M0 scaling factor, has emerged as a promising substrate for efficient LLM inference,…
ResearchEVO: An End-to-End Framework for Automated Scientific Discovery and Documentation
Zhe Zhao, Haibin Wen, Jiaming Ma +4
An important recurring pattern in scientific breakthroughs is a two-stage process: an initial phase of undirected experimentation that yields an unexpected finding, followed by a r…
Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
Haokun Lin, Haobo Xu, Yichen Wu +6
Recent advances in diffusion large language models (dLLMs) have introduced a promising alternative to autoregressive (AR) LLMs for natural language generation tasks, leveraging ful…