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The 3-step system for the paper check

Step 1 - Screening (≈ 5 min)

Skim abstract, LinkedIn post or hugging face-readme

  • Alarm bells: Huge “SOTA jumps” without clear justification
  • Discrepancy with the research consensus
  • Lack of code or data
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    Stage 2 - Validate (≈ 15 min)

    Figures + experiments + related work of a follow-up paper

  • Does the paper really compare its method fairly?
  • Have independent authors confirmed the results?
  • Does the data set match my own?
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    Stage 3 - In-depth (1-2 h)

    Study core chapter, roughly execute code

  • How clean is the implementation?
  • Are hyperparameters properly documented?
  • Can I integrate this into my pipeline (MLOps)?
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    Only those who pass stage 3 end up in Tom's roadmap.

    Tom uses the following tools:

  • Perplexity AI (with ArXiv filter): Search queries in natural language, finds papers far away from Google page 1
  • ChatGPT / Notebook LM: Get explanations, generate quiz questions, answers always with sources
  • auto-sklearn: Quickly generate baseline models and discover weak points in the data set
  • YouTube: Visual deep dives or beginner explanations
  • Reddit r/MachineLearning: Early warning system for brand new models, repos and leaks
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