INFRA Signal 433
Study: AI agents can't do open-ended research, so self-improvement may lag
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A new study finds AI agents can handle engineering but lack the judgment and creativity for original research, suggesting recursive self-improvement may not arrive as quickly as hyped.
For engineers, this means automating AI research is not imminent; the bottleneck is open-ended judgment, not engineering. It also suggests current evaluation methods for AI agents are too narrow, focusing on checkable tasks rather than the creative aspects of research.
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A multi-institution study led by Peter Kirgis and Sayash Kapoor at Princeton found AI agents could solve engineering problems but not conduct open-ended research.
The agents' papers were rejected by original authors, as they lacked creativity and judgment, despite completing all engineering tasks.
The study introduces 'shadow evaluation' to test agents on research questions from unpublished papers, revealing a gap in current evaluation methods.
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