The principal-reviewer-driver model behind the safe auto-approve analysis, explained
Aaroth · x · 2026-09-15
Aaron Roth describes the model behind his analysis: the principal and reviewer agents each have utility functions to maximize; the driver agent repeatedly proposes actions, and reviewers compare them to a baseline and vote approve/deny based on perceived utility. This formalizes the Codex/Claude Code approval mechanism to study when safety is guaranteed even with misaligned reviewers.
Related event: Aaron Roth's Coalitional Alignment Theory Questions Auto-Approve Safety(10 posts)→
More from Research
- At ACM AI Summit, formal methods and neurosymbolic AI pitched as ready-made paths to safer AI — luislamb · 2026-09-15
- Oxford Paper 'Theory Is All You Need' Argues LLMs Are Mathematically Incapable of True Novelty — gvachtan · 2026-09-15
- What is actually recursive about recursive self-improvement? — TheTuringPost · 2026-09-15
- Single-cell proteomics paired with transcriptomics reveals hidden functional coordination in PBMCs — anshulkundaje · 2026-09-15
- Bio researcher questions protein folding modeling: claims Baker Lab has no in vivo translation — iskander · 2026-09-15
- Foresight Institute's AI for Science & Safety RFP offers grants up to $100K — allisondman · 2026-09-15