Cites: Structured Scaling of AI Discovery Across Diverse Scientific Domains

12 citation edges inside this catalog. A title match is weaker evidence than an identifier match. Zero entries does not mean no citations on the web.

• Scaling Laws for Neural Language Models
  arxiv:2001.08361 · evidence: id
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
  arxiv:2203.11171 · evidence: title
• Scaling Laws for Reward Model Overoptimization
  arxiv:2210.10760 · evidence: title
• Self-Refine: Iterative Refinement with Self-Feedback
  arxiv:2303.17651 · evidence: title
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
  arxiv:2402.03300 · evidence: id
• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
  arxiv:2410.07095 · evidence: id
• s1: Simple test-time scaling
  arxiv:2501.19393 · evidence: title
• Can Large Reasoning Models Self-Train?
  arxiv:2505.21444 · evidence: id
• GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
  arxiv:2507.19457 · evidence: id
• Learning to Discover at Test Time
  arxiv:2601.16175 · evidence: id
• Mathematical discoveries from program search with large language models
  doi:10.1038/s41586-023-06924-6 · evidence: id
• ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution
  url:sakana.ai/shinka-evolve · evidence: id