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This position paper analyzes the reproducibility crisis in machine learning—quantifying unavailable code and unreproducible results—and proposes concrete measures to strengthen result checkability and verifiability in ML research.
The paper introduces ReAgent, an automated auditing framework for assessing the consistency between agent-written research documents and their associated repositories, using static and dynamic analysis to identify inconsistencies in methodologies and experimental results.
A group of Fields Medallists, including Terry Tao, issue a declaration highlighting the severe misalignment between AI companies' goals and the mathematical community's values in using AI to solve math problems.
An AI tool named Astra was used to analyze academic replication packages and uncovered numerous coding errors, some of which overturn central results in high-ranking journals, while also revealing that many models were not run properly.
A post-doc discovered that lab supply companies have been selling over 17,000 commercial antibodies using manipulated images, compromising scientific research integrity.
An investigation has identified over 450 images showing signs of manipulation in Thermo Fisher Scientific's antibody verification data, raising concerns about data integrity in their product catalog.
Researchers found 28 AI-generated fake citations in medical papers that influence clinical guidelines, highlighting the risk of AI hallucinations undermining scientific integrity and patient care.