Inbox — review queue (5)

AI-proposed analyses, connections, and relationships awaiting your accept/reject decision.

• Measuring Progress on Scalable Oversight for Large Language Models → relevant_to → Reward Model Drift [relationship] · confidence 0.13
  13% thematic overlap with Reward Model Drift
• Measuring Progress on Scalable Oversight for Large Language Models → relevant_to → Embodied AI [relationship] · confidence 0.17
  17% thematic overlap with Embodied AI
• Measuring Progress on Scalable Oversight for Large Language Models → relevant_to → World Models [relationship] · confidence 0.22
  22% thematic overlap with World Models
• Flight Recorder connection (score 2) [connection] · confidence 0.22
  Paper has 3% mission alignment and 0% relevance to the current bottleneck.
• Paper analysis awaiting review [analysis] · confidence 0.42
  This paper proposes an empirical research agenda for studying scalable oversight—supervising AI systems that may exceed human abilities. The authors introduce an experimental design centered on tasks where human specialists succeed but unaided humans and current AI systems fail, and run a proof-of-concept experiment. In this experiment, human participants interact via chat with an unreliable large-language-model dialog assistant on two QA tasks (MMLU and time-limited QuALITY). They find that human+model teams substantially outperform both the model alone and unaided humans, suggesting scalable oversight is tractable to study with present models.