Reddit - r/MachineLearning

Researchers: Is this framework useful, or complete BS?[D]

Level 0: Raw copy-paste / plagiarism

Level 1: Information compilation

Level 2: Understanding & summarization

Level 3: Comparison & evaluation

Level 4: Interpretation & analysis

Level 5: Applying knowledge to solve problems

Level 6: Designing experiments that generate new evidence

Level 7: Combining existing ideas in novel ways

Level 8: Making an original contribution (new method, dataset, benchmark, algorithm, theory, etc.)

Level 9: Breakthrough work that significantly changes a field

Level 10: Paradigm-shifting discoveries that redefine how we understand a domain

Some examples I had in mind:

  • Level 2: A literature review that accurately explains existing work.
  • Level 4: An analysis explaining why Transformers scale better than earlier architectures.
  • Level 5: Building and evaluating a better RAG pipeline for a real-world application.
  • Level 6: Running controlled experiments to test whether a new training strategy improves performance.
  • Level 7: Combining ideas from two different fields to create a new research direction.
  • Level 8: Publishing a genuinely new architecture, benchmark, or algorithm.
  • Level 9: Attention Is All You Need (Transformers) opening an entirely new direction for AI.
  • Level 10: Think of discoveries on the scale of Einstein's relativity or Newton's mechanics-rare, civilization-level shifts.

I know this is subjective, which is exactly why I'm posting it here. I'd really appreciate criticism from people who actually do research. Some questions I'm hoping you can answer:

  • Is this progression fundamentally reasonable, or is it misleading?
  • Which levels don't make sense?
  • Are there levels that should be merged or split?
  • Am I confusing difficulty with originality?
  • What dimensions are missing? (Novelty, rigor, reproducibility, significance, etc.)
  • Is there an existing framework in academia that already captures this idea better?
  • If you were mentoring a new PhD student, would you find something like this useful, or would you throw it away?

Please don't worry about being polite-I genuinely want to know whether this is a useful teaching tool or just academic nonsense. If it's flawed, I'd much rather understand why than keep refining something built on a bad premise.

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