Dead Science Walking: Publication Bias and the AI Scientist Pipeline
Kargi Chauhan · 2026 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
The finding, in one line
“We formalise this distortion as the null result gap, estimate it across three domains (drug discovery ~0.”
Abstract (excerpt)
AI scientist systems are beginning to automate the production, evaluation, and iteration of scientific hypotheses. Their promise is speed; their risk is that speed also scales errors embedded in the scientific record. We argue that a near-term risk is corpus failure: AI scientist systems are trained on and grounded in a literature that over-represents positive results and under-represents null findings. We formalise this distortion as the null result gap, estimate it across three domains (drug discovery ~0.60, psychology ~0.56, cancer biology ~0.35), and introduce an amplification index for re
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Metadata source: arXiv