LocalAgent: Collaborative Agentic Verification for Fine-Grained Instance-Level Consistency

Multimodal large language models (MLLMs) have made rapid progress in general visual understanding, yet they still struggle with fine-grained instance-level consistency verification, where the goal is to determine whether two images depict the exact same physical object under varying viewpoints, illumination, and backgrounds. This task requires both a global understanding of the target instance and a precise examination of subtle local evidence, since semantically similar objects may differ only in small discriminative regions. To address this challenge, we propose LocalAgent, a collaborative agentic framework that integrates the complementary strengths of large and small models. Specifically, the MLLM first reasons from a global instance-level perspective to identify suspicious local regions that may determine the final verification result. These accurately localized regions are then delegated to a lightweight Local Evidence Verifier (LEV) toolkit for fine-grained appearance and geometric matching. The LEV toolkit consists of Local Appearance Consistency (LAC), which provides illumination- and color-robust similarity estimation, and Local Geometric Correspondence (LGC), which establishes reliable spatial correspondence under viewpoint changes. Rather than directly replacing the MLLM's reasoning, LEV returns structured similarity scores and comparative evidence in a form that the MLLM can effectively interpret for final decision-making. This design enables a coordinated large-small model collaboration: the MLLM contributes global semantic reasoning and region-level suspicion localization, while specialized local verifiers provide precise, invariant, and interpretable evidence for ambiguous cases. We further introduce an answer-dominant reinforcement fine-tuning (RFT) strategy that enables the policy to learn when verification is necessary. Experiments show that LocalAgent achieves a +4.8% average improvement over direct-inference baselines, effectively narrowing the gap with closed-source models, while preserving the general multimodal capabilities of the underlying MLLM.