15 citations · 16 across the 10 of their papers we have counts for
10 papers
From Kepler to Newton: Inductive Biases Guide Learned World Models in Transformers
Ziming Liu, Sophia Sanborn, Surya Ganguli +1
Can general-purpose AI architectures go beyond prediction to discover the physical laws governing the universe? True intelligence relies on "world models" -- causal abstractions th…
Are Your Reasoning Models Reasoning or Guessing? A Mechanistic Analysis of Hierarchical Reasoning Models
Zirui Ren, Ziming Liu
Hierarchical reasoning model (HRM) achieves extraordinary performance on various reasoning tasks, significantly outperforming large language model-based reasoners. To understand th…
Neural Thermodynamic Laws for Large Language Model Training
Ziming Liu, Yizhou Liu, Jeff Gore +1
Beyond neural scaling laws, little is known about the laws underlying large language models (LLMs). We introduce Neural Thermodynamic Laws (NTL) -- a new framework that offers fres…
LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols
Ziming Liu, Bryan Liu, Alvaro Valcarce +1
Integrating Large AI Models (LAMs) into 6G mobile networks is a key enabler of the AI-Native Air Interface (AI-AI), where protocol intelligence must scale beyond handcrafted logic.…
Interpretable Machine Learning in Physics: A Review
Sebastian Johann Wetzel, Seungwoong Ha, Raban Iten +2
Machine learning is increasingly transforming various scientific fields, enabled by advancements in computational power and access to large data sets from experiments and simulatio…
Physics of Skill Learning
Ziming Liu, Yizhou Liu, Eric J. Michaud +2
We aim to understand physics of skill learning, i.e., how skills are learned in neural networks during training. We start by observing the Domino effect, i.e., skills are learned s…
FOCUS: First Order Concentrated Updating Scheme
Yizhou Liu, Ziming Liu, Jeff Gore
Large language models (LLMs) demonstrate remarkable performance, and improving their pre-training process appears to be key to enhancing their capabilities further. Based on the do…
Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training
Wei Bu, Uri Kol, Ziming Liu
The dynamical evolution of a neural network during training has been an incredibly fascinating subject of study. First principal derivation of generic evolution of variables in sta…
Financial Risk Assessment via Long-term Payment Behavior Sequence Folding
Yiran Qiao, Yateng Tang, Xiang Ao +4
Online inclusive financial services encounter significant financial risks due to their expansive user base and low default costs. By real-world practice, we reveal that utilizing l…
Applications of deep learning to relativistic hydrodynamics
Hengfeng Huang, Bowen Xiao, Huixin Xiong +3
In this proceeding, we will briefly review our recent progress on implementing deep learning to relativistic hydrodynamics. We will demonstrate that a successfully designed and tra…