Cover Image

Scaling Law Hypothesis for Multimodal Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sun, Qingyun, Guo, Zhen, Team, PIN AI
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GOD model: Privacy Preserved AI School for Personal Assistant
by: PIN AI Team, et al.
Published: (2025)

The Neural Pruning Law Hypothesis
by: Barbulescu, Eugen, et al.
Published: (2025)

Wukong: Towards a Scaling Law for Large-Scale Recommendation
by: Zhang, Buyun, et al.
Published: (2024)

Scaling Laws for Pre-training Agents and World Models
by: Pearce, Tim, et al.
Published: (2024)

Towards a Comprehensive Scaling Law of Mixture-of-Experts
by: Zhao, Guoliang, et al.
Published: (2025)

Hypothesis Testing the Circuit Hypothesis in LLMs
by: Shi, Claudia, et al.
Published: (2024)

RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generation
by: Yuan, Haonan, et al.
Published: (2026)

Towards Neural Scaling Laws for Time Series Foundation Models
by: Yao, Qingren, et al.
Published: (2024)

Gemstones: A Model Suite for Multi-Faceted Scaling Laws
by: McLeish, Sean, et al.
Published: (2025)

Scaling Law Phenomena Across Regression Paradigms: Multiple and Kernel Approaches
by: Chen, Yifang, et al.
Published: (2025)

The Scaling Law for LoRA Base on Mutual Information Upper Bound
by: Zhang, Jing, et al.
Published: (2025)

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs
by: Bian, Song, et al.
Published: (2025)

Jailbreak Scaling Laws for Large Language Models: Polynomial-Exponential Crossover
by: Halder, Indranil, et al.
Published: (2026)

On Implications of Scaling Laws on Feature Superposition
by: Katta, Pavan
Published: (2024)

Towards Neural Scaling Laws on Graphs
by: Liu, Jingzhe, et al.
Published: (2024)

Scaling Law for Time Series Forecasting
by: Shi, Jingzhe, et al.
Published: (2024)

On the Optimizer Dependence of Neural Scaling Laws
by: Ramani, Vansh, et al.
Published: (2026)

Scaling Laws for Online Advertisement Retrieval
by: Wang, Yunli, et al.
Published: (2024)

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
by: Yuan, Haonan, et al.
Published: (2026)

Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models
by: Vaidhya, Tejas, et al.
Published: (2025)

A Hierarchical Language Model with Predictable Scaling Laws and Provable Benefits of Reasoning
by: Gaitonde, Jason, et al.
Published: (2026)

Information-Theoretic Foundations for Neural Scaling Laws
by: Jeon, Hong Jun, et al.
Published: (2024)

Time Matters: Scaling Laws for Any Budget
by: Inbar, Itay, et al.
Published: (2024)

MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation
by: Chang, Yurui, et al.
Published: (2026)

Explain Less, Understand More: Jargon Detection via Personalized Parameter-Efficient Fine-tuning
by: Wu, Bohao, et al.
Published: (2025)

Generalizing Scaling Laws for Dense and Sparse Large Language Models
by: Hossain, Md Arafat, et al.
Published: (2025)

A Simple Model of Inference Scaling Laws
by: Levi, Noam
Published: (2024)

Can Language Models Discover Scaling Laws?
by: Lin, Haowei, et al.
Published: (2025)

Exploring Scaling Laws for EHR Foundation Models
by: Zhang, Sheng, et al.
Published: (2025)

Theoretical Foundations of Scaling Law in Familial Models
by: Song, Huan, et al.
Published: (2025)

P$^2$ Law: Scaling Law for Post-Training After Model Pruning
by: Chen, Xiaodong, et al.
Published: (2024)

GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
by: Guo, Zihao, et al.
Published: (2024)

GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation
by: Guo, Zihao, et al.
Published: (2025)

Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
by: Abnar, Samira, et al.
Published: (2025)

JTok: On Token Embedding as another Axis of Scaling Law via Joint Token Self-modulation
by: Yang, Yebin, et al.
Published: (2026)

Scaling Laws for Precision in High-Dimensional Linear Regression
by: Zhang, Dechen, et al.
Published: (2026)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice
by: Askari-Hemmat, Reyhane, et al.
Published: (2025)

Does Scaling Law Apply in Time Series Forecasting?
by: Li, Zeyan, et al.
Published: (2025)

Scaling Laws for Data-Efficient Visual Transfer Learning
by: Yang, Wenxuan, et al.
Published: (2025)

Scaling Laws for Imitation Learning in Single-Agent Games
by: Tuyls, Jens, et al.
Published: (2023)