GoKawiilStudy finds AI research agents still lack creativity for independent scientific work
A new evaluation of AI research agents, described by researcher Sayash Kapoor, found the systems performed strong engineering tasks but produced papers far below top AI conference standards. The agents ran flawed experiments, struggled to explain their findings clearly, abandoned promising hypotheses too early, and failed to meaningfully use feedback, time, or compute resources.
GoKawiil's interpretation of the reporting above, not reported fact.
The findings suggest fears about AI rapidly triggering recursive self-improvement may be premature, since genuine scientific research requires creativity, judgment, and the ability to pivot that current models lack. It also hints that today's reinforcement-learning training methods reward narrow engineering skills rather than the open-ended reasoning needed for real discovery.
- AI agents excelled at engineering tasks but failed at producing quality original research.
- They couldn't abandon failing approaches or creatively rethink hypotheses.
- No evidence of 'reward hacking'; errors were caught by supervising orchestrator agents.
The Alignment Problem" by Brian Christian — If this piece made you curious about what AI systems can and can't actually do, Brian Christian's book is a great deep dive into how machine learning research really works and where it breaks down. It's readable, well-reported, and pairs perfectly with debates about AI agents doing science on their own.
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Published there as: “AI recursive self-improvement might not come so quickly after all”
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