4 papers
IronLLM: Forging Compact Edge-Native Language Models for Real-Time Embodied Intelligence
Changdi Yang, Fengquan Jiao, Haochih Lin +14
We present IronLLM-0.6B, a 654M-parameter language model designed for efficient on-device inference. IronLLM-0.6B combines a hybrid attention architecture with X-MTP, a lightweight…
Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost
Yinggan Xu, Kajetan Schweighofer, Risto Miikkulainen +1
Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune. Sta…
PhySense: Principle-Based Physics Reasoning Benchmarking for Large Language Models
Yinggan Xu, Yue Liu, Zhiqiang Gao +2
Large language models (LLMs) have rapidly advanced and are increasingly capable of tackling complex scientific problems, including those in physics. Despite this progress, current…
Advancing AI-Scientist Understanding: Multi-Agent LLMs with Interpretable Physics Reasoning
Yinggan Xu, Hana Kimlee, Yijia Xiao +1
Large Language Models (LLMs) are playing an increasingly important role in physics research by assisting with symbolic manipulation, numerical computation, and scientific reasoning…