Shadow evaluations: Claude Opus 4.8's attempts at NeurIPS research problems rejected by original authors
CurieuxExplorer · x · 2026-09-05
A new study challenges the intelligence explosion narrative: recursive self-improvement may be harder than expected.
- Context: LLMs excel at ML research chores like coding, data curation, and running experiments; Sakana AI's AI Scientist-v2 even passed ICLR peer review, fueling speculation about imminent recursive self-improvement
- Method: Researchers used "shadow evaluations" — giving AI agents open research questions from high-quality unpublished ML papers, graded by the original authors
- Result: Claude Opus 4.8 attempted two NeurIPS 2026 paper problems; authors rejected both. Princeton's Sayash Kapoor said the papers were "nowhere close" to top-conference quality
- Takeaway: AI agents still struggle with genuinely open-ended research problems central to advancing the field
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