验证成为AI扩展新方向：论文无需微调提升准确性

NEW AI paper worth bookmarking. This is something I called early, and this paper confirms it: verification has emerged as a new important scaling axis. Here is the simple explainer and what this paper shows. We have seen lots of progress in scaling pre-training, post-training, and test-time compute. For post-training and test-time compute, we are still in its early phases. But one of the most important new directions is using LLMs as verifiers. Verifiers are fundamental to scaling AI. This work from Stanford, NVIDIA, and UC Berkeley builds a training-free verifier that reads a continuous, calibrated score straight off the scoring-token logits instead of trusting a discrete grade. Three knobs move accuracy without any fine-tuning. Score granularity for cleaner separation, repeated evaluation for lower variance, and criteria decomposition for lower complexity. The numbers land across very different domains. 86.5% on Terminal-Bench V2, 78.2% on SWE-Bench Verified, 87.4% on RoboRewardBench, and 73.3% on MedAgentBench. The same continuous score doubles as dense reward for SAC and GRPO and as a task-progress signal shipped in a Claude Code extension. Paper: https://t.co/D9PkrZEvkb Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX