1 citations · 1 across the 8 of their papers we have counts for
8 papers
NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation
Junjie Tang, Jun Huan, Hao Zhou +2
Recent agentic approaches to LLM-based kernel generation have achieved impressive results on CUDA. For emerging AI accelerators such as AWS Trainium and Inferentia, automated kerne…
Dissecting model behavior through agent trajectories
Gaurav Gupta, Vatshank Chaturvedi, Jun Huan +2
AI agent performance is not just a modeling problem, it is fundamentally a systems problem. The advanced capabilities of models are realized through agent harnesses. Therefore, a g…
ContextWeaver: Selective and Dependency-Structured Memory Construction for LLM Agents
Yating Wu, Yuhao Zhang, Sayan Ghosh +4
Large language model (LLM) agents often struggle in long-context interactions. As the history grows, sliding windows and prompt compression may drop earlier information that later…
ExecTune: Effective Steering of Black-Box LLMs with Guide Models
Vijay Lingam, Aditya Golatkar, Anwesan Pal +6
For large language models deployed through black-box APIs, recurring inference costs often exceed one-time training costs. This motivates composed agentic systems that amortize exp…
Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
Tianci Liu, Ruirui Li, Yunzhe Qi +8
Large language models (LLMs) have achieved remarkable performance on various natural language tasks. However, they are trained on static corpora and their knowledge can become outd…
Inference Optimization of Foundation Models on AI Accelerators
Youngsuk Park, Kailash Budhathoki, Liangfu Chen +7
Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry an…
Collage: Light-Weight Low-Precision Strategy for LLM Training
Tao Yu, Gaurav Gupta, Karthick Gopalswamy +7
Large models training is plagued by the intense compute cost and limited hardware memory. A practical solution is low-precision representation but is troubled by loss in numerical…
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
Haozheng Fan, Hao Zhou, Guangtai Huang +6
Getting large language models (LLMs) to perform well on the downstream tasks requires pre-training over trillions of tokens. This typically demands a large number of powerful compu…