An arXiv preprint led by Princeton's Sayash Kapoor proposes "shadow evaluation": an AI system was pointed at the research questions of two papers submitted to NeurIPS, told to run its own project and write a paper, and then graded by the original authors. The agent harnessed Claude Opus 4.8 inside a modified OpenClaw scaffold with six days and $3,000 of compute credits per paper; it ran hundreds of experiments without stalling, produced plausible literature reviews and caught its own hallucinations without reward hacking, yet earned only 2/6 and 1/6 from the authors. The authors cite premature lock-in on a hypothesis, insufficient backtracking, overly lenient self-review and messy write-ups; Kapoor says full automation of open-ended research is far off, while AI Scientist co-creator Cong Lu called it an interesting data point but said the guardrails were too weak.