My most common research advice: do quick sanity checks
Viral research guide reveals how basic checks prevent wasted months on broken AI experiments.
A viral research guide from an AI expert, published as part of the Inkhaven Residency, is gaining traction for its practical advice to junior researchers. The core recommendation emphasizes implementing "quick sanity checks" before diving deep into experiments, arguing that researchers often waste "countless hours or days if not weeks or months" on fruitless investigations due to overlooked fundamental flaws. The advice is structured as the first of a three-part series, with subsequent pieces covering "saying precisely what you want to say" and "asking why one more time."
The guide provides concrete, technical examples from modern AI research where sanity checks are critical. It advises checking for data bias, verifying that LLM agents (AI that can take actions) are successfully using their tool-calling scaffolds, and ensuring reasoning chains are functioning correctly. A specific case study involves analyzing why language models fail at tasks like the n=10 Tower of Hanoi problem—often not due to a capability gap, but because models like Claude Opus 4 refuse to attempt the task, calling it "extremely tedious and error prone." Other checks include quantifying basic correlations, such as how often weaker models reveal a "hidden task" in their output, and examining the mean and standard deviation of key dataset statistics to spot obvious errors before they derail a project.
- Advises checking for broken LLM agent scaffolds and tool-call success rates, common failures 1-2 years ago.
- Recommends validating reasoning chain length and model refusal patterns, like Claude Opus 4 rejecting tedious tasks.
- Suggests quantifying data basics (mean, std dev) and checking for bias to prevent months of wasted research.
Why It Matters
Saves AI researchers months of work by catching flawed experimental setups and data issues before deep investment.