Papers

• Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study (2026)
  Kaihua Ding
  4.39
• LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws (2026)
  Xu Ouyang et al.
  1.94
• Physics Question Scene Graph: Fine-grained Evaluation of Physical Plausibility in Text-to-Video Generation (2026)
  Atin Pothiraj et al.
  1.94
• UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems (2026)
  Mingming Ha et al.
  1.83
• Beyond Language Modeling: An Exploration of Multimodal Pretraining (2026)
  Shengbang Tong et al.
  1.78
• X-Coder: Advancing Competitive Programming with Fully Synthetic Tasks, Solutions, and Tests (2026)
  Jie Wu et al.
  1.67
• Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models (2025)
  Samira Abnar et al.
  1.28
• DataDecide: How to Predict Best Pretraining Data with Small Experiments (2025)
  Ian Magnusson et al.
  1.28
• Kinetics: Rethinking Test-Time Scaling Laws (2025)
  Ranajoy Sadhukhan et al.
  1.28
• Scaling Laws for Optimal Data Mixtures (2025)
  Mustafa Shukor et al.
  1.28
• Beyond Outliers: A Study of Optimizers Under Quantization (2025)
  Georgios Vlassis et al.
  1.28
• Scaling Laws for Deepfake Detection (2025)
  Wenhao Wang et al.
  1.28
• ATLAS: Adaptive Transfer Scaling Laws for Multilingual Pretraining, Finetuning, and Decoding the Curse of Multilinguality (2025)
  Shayne Longpre et al.
  1.28
• Scaling Laws for Code: Every Programming Language Matters (2025)
  Jian Yang et al.
  1.28
• Stack More Layers Differently: High-Rank Training Through Low-Rank Updates (2023)
  Vladislav Lialin et al.
  —
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System (2024)
  Weize Chen et al.
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