Five good results won't tell you if your AI model works: a five-slot validation test
bravo_abad · x · 2026-09-30
Jorge Bravo Abad lays out a practical framework: when a model ranks your candidates but you can only afford five experiments, testing the top five is the obvious but wrong move—five good outcomes only show the model can find something in the region it knows best.
The five-slot test, giving each experiment a job:
- Leading feasible candidate: does your top pick actually work
- Less familiar candidate: highly ranked but from a thinly represented region, testing generalization
- Baseline choice: what your group would have picked without the model
- Predicted poor performer: can the model recognize bad candidates
- Independent repeat: does the recommendation survive a second preparation
Key discipline: commit your predictions and thresholds in writing first, deciding what each outcome means before seeing any data.
More from Research
- Meta, Stanford and Harvard open up ProgramBench leaderboard with community submissions — jyangballin · 2026-09-30
- AI's "OH MY GOD!" exclamations may actually help its reasoning — danintheory · 2026-09-30
- Poison sample selection swings LLM backdoor attack success from 3% to 80% — chhaviyadav_ · 2026-09-30
- Rewriting the ELBO Explainer for Diffusion Language Model Training — zmkzmkz · 2026-09-30
- Google DeepMind scientist releases 58-page paper on game-theory-specialized agents — mdancho84 · 2026-09-30
- Tokens Are Just Integer IDs: The Comma Is Row 28 of the Embedding Matrix — zsakib_ · 2026-09-30