Multimodal large language models (MLLMs) show strong potential as judges. However, existing approaches face a fundamental trade-off: adapting MLLMs to output a single score misaligns with the generative nature of MLLMs and limits fine-grained requirement understanding, whereas autoregressively generating judging analyses is prohibitively slow in high-throughput settings. Observing that judgment reduces to verifying whether inputs satisfy a set of structured requirements, we propose YOFO, a template-conditioned method that judges all requirements in a single forward pass. Built on an autoregressive model, YOFO accepts a structured requirement template and, in one inference step, produces a binary yes/no decision for each requirement by reading the logits of the final token associated with that requirement. This design yields orders-of-magnitude speedups while preserving interpretability. Extensive experiments show that YOFO not only achieves state-of-the-art results on standard recommendation datasets, but also supports dependency-aware analysis -- where subsequent judgments are conditioned on previous ones -- and further benefits from post-hoc CoT.

翻译：多模态大语言模型（MLLMs）展现出作为评判者的强大潜力。然而，现有方法面临一个根本性的权衡：将MLLMs适配为输出单一分数，既与MLLMs的生成本质不符，也限制了对细粒度需求的理解；而通过自回归生成评判分析在高吞吐量场景下则慢得难以接受。我们观察到，评判可归结为验证输入是否满足一组结构化需求。为此，我们提出YOFO，一种基于模板条件化的方法，可在单次前向传播中完成对所有需求的评判。YOFO构建于自回归模型之上，接收一个结构化需求模板，并在一次推理步骤中，通过读取与每个需求相关联的最终标记的对数几率，为每个需求生成一个二元是/否判定。这一设计在保持可解释性的同时，实现了数量级的加速。大量实验表明，YOFO不仅在标准推荐数据集上取得了最先进的结果，还支持依赖感知分析——即后续评判可基于先前结果进行条件化判断——并能进一步受益于事后思维链推理。