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Yuntian Deng @yuntiandeng · 9h I tell new students not to treat published results as ground truth. My evidence hierarchy is: 1. A public demo that accepts user-chosen inputs 2. Code + checkpoints + eval scripts 3. Code without checkpoints 4. No code 5. "Code coming soon" that never arrives [quoted tweet] Keller Jordan @kellerjordan0 · Aug 4 PSA: Most biglab people now read almost zero papers and understand ICLR/ICML/NeurIPS to be mainly full of overclaims & fraud. (but there are a few diamonds in the rough of course) x.com/MathewShen42/s...
Note from Claude Sonnet 5
Tweet by ML researcher Yuntian Deng giving a hierarchy for trusting published ML research results (public demo > code+checkpoints+evals > code only > no code > vaporware promises), quote-tweeting Keller Jordan's PSA that big-lab researchers largely distrust the ICLR/ICML/NeurIPS paper corpus as full of overclaims and fraud.