Copertina

Neural Thermodynamic Laws for Large Language Model Training

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Liu, Ziming, Liu, Yizhou, Gore, Jeff, Tegmark, Max
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence Data Analysis, Statistics and Probability
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Neural Scaling Law from Lottery Ticket Ensembling
di: Liu, Ziming, et al.
Pubblicazione: (2023)

Physics of Skill Learning
di: Liu, Ziming, et al.
Pubblicazione: (2025)

Superposition unifies power-law training dynamics
di: Chen, Zixin Jessie, et al.
Pubblicazione: (2026)

KAN 2.0: Kolmogorov-Arnold Networks Meet Science
di: Liu, Ziming, et al.
Pubblicazione: (2024)

A Resource Model For Neural Scaling Law
di: Song, Jinyeop, et al.
Pubblicazione: (2024)

A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification
di: Corradini, Flavio, et al.
Pubblicazione: (2024)

Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks
di: Nauck, Christian, et al.
Pubblicazione: (2022)

DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design
di: Yu, Xinling, et al.
Pubblicazione: (2025)

Bayesian Inference with Deep Weakly Nonlinear Networks
di: Hanin, Boris, et al.
Pubblicazione: (2024)

When Attention Collapses: Residual Evidence Modeling for Compositional Inference
di: Houba, Niklas
Pubblicazione: (2026)

Scalable Reinforcement Post-Training Beyond Static Human Prompts: Evolving Alignment via Asymmetric Self-Play
di: Ye, Ziyu, et al.
Pubblicazione: (2024)

Lines of Thought in Large Language Models
di: Sarfati, Raphaël, et al.
Pubblicazione: (2024)

PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research
di: Miao, Tingjia, et al.
Pubblicazione: (2026)

L$^2$M: Mutual Information Scaling Law for Long-Context Language Modeling
di: Chen, Zhuo, et al.
Pubblicazione: (2025)

From Data to Laws: Neural Discovery of Conservation Laws Without False Positives
di: Ray, Rahul D
Pubblicazione: (2026)

Nonnegative matrix factorization and the principle of the common cause
di: Khalafyan, E., et al.
Pubblicazione: (2025)

On the continuity of flows
di: Sha, Congzhou M
Pubblicazione: (2025)

Mesh-free sparse identification of nonlinear dynamics
di: Gao, Mars Liyao, et al.
Pubblicazione: (2025)

The most likely common cause
di: Hovhannisyan, A., et al.
Pubblicazione: (2023)

Optimal Symmetries in Binary Classification
di: Ngairangbam, Vishal S., et al.
Pubblicazione: (2024)

Uncertainty Quantification with Bayesian Higher Order ReLU KANs
di: Giroux, James, et al.
Pubblicazione: (2024)

Practically Effective Adjustment Variable Selection in Causal Inference
di: Noda, Atsushi, et al.
Pubblicazione: (2025)

Anomaly Detection in Soil Heavy Metal Contamination Using Unsupervised Learning for Environmental Risk Assessment
di: Adjokatse, Isaac Tettey, et al.
Pubblicazione: (2026)

Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil
di: Michelucci, Umberto, et al.
Pubblicazione: (2024)

Efficient Training of Deep Neural Operator Networks via Randomized Sampling
di: Karumuri, Sharmila, et al.
Pubblicazione: (2024)

Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model
di: Wang, Xinyu, et al.
Pubblicazione: (2024)

Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
di: Liu, Wei, et al.
Pubblicazione: (2025)

Data-driven Progressive Discovery of Physical Laws
di: Xia, Mingkun, et al.
Pubblicazione: (2026)

Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale
di: Zheng, Candi, et al.
Pubblicazione: (2023)

Thermodynamic bounds on energy use in quasi-static Deep Neural Networks
di: Tkachenko, Alexei V.
Pubblicazione: (2025)

PIED: Physics-Informed Experimental Design for Inverse Problems
di: Hemachandra, Apivich, et al.
Pubblicazione: (2025)

Free Energy Risk Metrics for Systemically Safe AI: Gatekeeping Multi-Agent Study
di: Walters, Michael, et al.
Pubblicazione: (2025)

Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution
di: Ansah-Narh, T., et al.
Pubblicazione: (2026)

PINNACLE: PINN Adaptive ColLocation and Experimental points selection
di: Lau, Gregory Kang Ruey, et al.
Pubblicazione: (2024)

Adaptive Behavioral AI: Reinforcement Learning to Enhance Pharmacy Services
di: del Río, Ana Fernández, et al.
Pubblicazione: (2024)

Understanding complex crowd dynamics with generative neural simulators
di: Minartz, Koen, et al.
Pubblicazione: (2024)

PRISM: Deriving a White-Box Transformer as a Signal-Noise Decomposition Operator via Maximum Coding Rate Reduction
di: Huang, Dongchen
Pubblicazione: (2026)

The role of data embedding in quantum autoencoders for improved anomaly detection
di: Araz, Jack Y., et al.
Pubblicazione: (2024)

PATHFINDER: Multi-objective discovery in structural and spectral spaces
di: Barakati, Kamyar, et al.
Pubblicazione: (2026)

Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking)
di: Nam, Yoonsoo, et al.
Pubblicazione: (2025)