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Paper Database

Our research layer tracks papers from arXiv, NeurIPS, ICML, ICLR, and other sources, analyzing their impact on deployed models.

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Impact Levels

Paradigm-shifting work that redefines what’s possible
Major advancement with measurable improvements
Small but meaningful improvement
Confirms or reproduces existing work
Disproves or challenges existing claims

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Browse Papers

Chain of Thought at Scale

Achieving human-level mathematical reasoning
Impact: Significant | Models: GPT-4o, Claude 3 Opus

All Papers

Browse complete paper database

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Recent Discoveries

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Latest Papers
  • 3 papers on reasoning improvements
  • 2 papers on multimodal architectures
  • 1 paper challenging existing benchmarks

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Contributing Papers

Found a paper that impacts model performance? We track:
  1. Direct model improvements - Papers that improve specific models
  2. Benchmark changes - New evaluation methods or datasets
  3. Architecture innovations - New techniques applicable across models
  4. Negative results - Papers that challenge existing assumptions
Each paper is cross-referenced with affected models and capabilities.
Papers are reviewed by our graduate research layer before publication