When is seeing enough? Cross-granularity evidence search with Sufficiency verification for High-resolution understanding

Although multimodal large language models (MLLMs) have substantially advanced visual understanding, resolving fine-grained details in high-resolution images remains challenging under limited visual input budgets. Existing approaches recover such details through local cropping and iterative region search, but the relevance of individual regions does not establish whether the accumulated evidence is sufficient to support the correct answer. This raises two coupled challenges: acquiring complementary evidence across granularities and determining when the accumulated evidence is sufficient to support an answer. To address these challenges, we propose a novel framework named Cross-Granularity Evidence Search with Sufficiency Verification (CAREER) for high-resolution visual question answering. The core idea is to couple complementary evidence acquisition with explicit sufficiency verification to guide continued observation and answer acceptance. Specifically, we first bootstrap a low-cost global-to-local evidence state and construct a complementary multi-scale candidate atlas that exposes potentially informative observations. Building on this atlas, we adaptively refine or expand views and explore alternative regions according to unresolved evidence requirements, accumulating observations to retain both local details and broader context. To assess evidence sufficiency, we introduce an option-wise verifier that evaluates visual support without access to the generator’s provisional answer, and then incorporate its sufficiency verification to guide further observation and adaptive stopping. Additionally, experiments across three MLLM backbones and three high-resolution QA settings show that CAREER consistently outperforms state-of-the-art visual-search baselines, while also reducing object hallucination on POPE benchmark. The code is available at https://github.com/CAREER-ICLR2027/CAREER.git.