Yoav Hollander

Originally posted on my blog, 24 Jun 2025 - see also full PDF (26 pp) Abstract The V&V method is a concrete, practical framework which can complement several alignment approaches. Instead of asking a nascent AGI to “do X,” we instruct it to design and rigorously verify a bounded “machine-for-X”. The machine (e.g. an Autonomous Vehicle or a “machine” for curing cancer) is prohibited from uncontrolled self-improvement: Every new version must re-enter the same verification loop. Borrowing from safety-critical industries, the loop couples large-scale, scenario-based simulation with coverage metrics and a safety case that humans can audit. Human operators—supported by transparent evidence—retain veto power over deployment. The method proceeds according to the following diagram: This method is not a silver bullet: it still relies on aligned obedience, incentive structures that deter multi-agent collusion, and tolerates only partial assurance under tight competitive timelines. Yet it complements existing scalable-oversight proposals—bolstering Constitutional AI, IDA and CAIS with holistic system checks—and offers a practical migration path because industries already practice V&V (Verification and Validation) for complex AI today. In the critical years when AGI first appears, the V&V method could buy humanity time, reduce specification gaming, and focus research attention on the remaining hard gaps such as strategic deception and corrigibility. TL;DR * Ask AGI to build & verify a task-specific, non-self-improving machine * Scenario-based, coverage-driven simulations + safety case give humans transparent evidence * Main contributions: Complements Constitutional AI, improves human efficiency in Scalable Oversight, handles "simpler” reward hacking systematically, bolsters IDA and CAIS * Works today in Autonomous Vehicles. Scales with smarter “AI workers” * Limits: Needs aligned obedience & anti-collusion incentives About the author Yoav Hollander led the creation