An arXiv paper posted on 15 Oct 2026 introduces OmniVerifier-C1, a visual verifier that moves beyond binary True/False judgments by automatically decomposing complex prompts or environmental rules into fine-grained components and assigning pointwise scores for a continuous, more informative critique signal. The authors train it with a decoupled RL strategy that jointly optimizes sub-question-level binary judgment and global alignment scoring, and report that the pointwise graded verifier significantly outperforms binary verifiers in traditional text-to-image scenarios. They extend the work to rule-governed generative world modeling, arguing theoretically that pairwise verification yields more accurate critiques than the pointwise formulation when ground-truth images are available, with experiments on structured tasks such as Maze and Sudoku; the paper is listed as NeurIPS 2026.

OmniVerifier-C1·NeurIPS 2026