Publications

Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample …

AI agents may soon become capable of autonomously completing valuable, longhorizon tasks in diverse domains. Current benchmarks either …

Large Language Models (LLMs) have emerged as powerful operators for evolutionary search, yet the design of efficient search scaffolds …

Speculative decoding (SD) has proven effective for accelerating LLM inference by quickly generating draft tokens and verifying them in …

Large Language Models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated strong reasoning capabilities. To further enhance …

Modern research computing cyberinfrastructure, such as ACCESS-CI and NAIRR Pilot, offers GPU resources across geographically …

Low-rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource …

Scaling laws are powerful tools to predict the performance of large language models. However, current scaling laws fall short of …

Speculative Decoding is a widely used technique to speed up inference for Large Language Models (LLMs) without sacrificing quality. …

As neural networks (NN) are deployed across diverse sectors, their energy demand correspondingly grows. While several prior works have …

More than 70% of cloud computing is paid for but sits idle. A large fraction of these idle compute are cheap CPUs with few cores that …