Verification is emerging as a new scaling axis for AI! Scaling pre-training, post-training, and test-time compute have driven much of the recent progress in large language models. Our new work explores a fourth scaling axis: #verification# —the ability to determine whether a solution is actually correct. In LLM-as-a-Verifier, we introduce a general-purpose framework that provides fine-grained feedback across diverse modalities without additional training. We show that three simple ingredients—higher score granularity, repeated evaluation, and criteria decomposition—consistently improve verification performance. The approach achieves state-of-the-art results across robotics, coding, and medical AI, including RoboRewardBench, Terminal-Bench V2, SWE-Bench Verified, and MedAgentBench. I'm particularly optimistic about the implications for #Robotics# and #PhysicalAI#, where verification can serve as a dense reward signal for reinforcement learning, significantly improving the sample efficiency of SAC and GRPO and, in turn, enabling more capable and reliable autonomous systems. As AI continues to scale, I believe verification will become a foundational capability for building more capable and trustworthy autonomous AI agents. 🌐 Website: 📄 Paper: 💻 Code: Outstanding work led by @jackyk02, in collaboration with @shululi256, @pranav_atreya, @liu_yuejiang, @jyx_su, @chelseabfinn, @istoica05, and @Azaliamirh. #AI# #LLMs# #Verification# #Reasoning# #AgenticAI# #Robotics# #PhysicalAI# #ReinforcementLearning# @StanfordAILab @StanfordEng