让AI自己找关键图像细节，逐步修正错误判断。
LOCI: A Locator-Critic with Refinement Loop
- 分两步：先定位候选区域，再由独立模型评估其是否足够
- 在多个复杂视觉任务上提升12.1%至11.2%准确率
- 无需训练，可提升开源与闭源大模型表现
视觉语言模型在需要复杂视觉理解的任务上仍表现不佳。我们认为核心问题并非高层推理能力不足，而是无法准确定位图像中的关键细节。由于这一缺陷，模型常基于错误的感知基础生成看似合理但实际错误的推理。为此，我们提出无需训练的定位-评判框架LOCI，将视觉搜索与证据验证解耦。该框架包含定位代理（Locator）和评判代理（Critic），前者提出候选视觉证据，后者评估其相关性与充分性。两者通过迭代优化循环，持续改进证据直至足以回答问题。这种解耦且自我修正的过程显著提升了性能，在多个复杂视觉基准测试中达到领先水平。对于开放权重模型如Qwen3-VL，准确率提升达+12.1（V*）、+5.8（HR-Bench）、+11.2（VisualProbe-Hard）；对闭源模型如Gemini 2.5 Pro，分别提升+8.9（V*）、+4.3（HR-Bench）、+4.8（VisualProbe-Hard）。
原文摘要 · Abstract (English)
Vision-Language Models (VLMs) still struggle on tasks requiring complex visual understanding. We argue that the core issue is not high-level reasoning, but instead failing to locate critical details in the image. Due to this shortcoming, VLMs generate often plausible but incorrect reasoning based on flawed perceptual grounding. To address this, we propose Locator-Critic (LOCI), a training-free framework that decouples visual search from evidence verification. LOCI employs a Locator agent to propose candidate visual evidence and a separate Critic agent to evaluate its relevance and sufficiency. These agents engage in an iterative refinement loop, progressively improving the evidence until it is adequate to answer the given question. This decoupled, self-correcting process yields substantial performance gains, achieving state-of-the-art results on multiple complex visual benchmarks. LOCI improves accuracy for both open-weight models like Qwen3-VL (+12.1 on V*, +5.8 on HR-Bench and +11.2 on VisualProbe-Hard) and proprietary models like Gemini 2.5 Pro (+8.9 on V*, +4.3 on HR-Bench, +4.8 on VisualProbe-Hard).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。