Beyond Ranking: Fine-Grained Diagnostics and Self-Improvement for MLLMs

摘要

While Multimodal Large Language Models (MLLMs) are advancing rapidly, accurately evaluating their capabilities remains challenging. Current paradigms primarily rely on holistic scoring and static leaderboards, which fail to disentangle fine-grained competencies. Specifically, they suffer from “Outcome Bias” by validating only final answers and ignoring intermediate reasoning. To address these limitations, we introduce ATOM (AnaTomy Of MLLM), a novel MLLM-as-a-judge framework designed to shift the focus from ranking to fine-grained diagnosis. ATOM decomposes complex reasoning into atomic criteria anchored in visual elements, enforcing verification against explicit visual facts. Validated on a newly constructed benchmark with rigorous human rankings, ATOM achieves state-of-the-art accuracy, surpassing the strongest baseline by up to 7.92%. Moving beyond ranking, ATOM bridges the gap between assessment and alignment: by pinpointing atomic-level failures, it establishes a closed-loop mechanism for targeted self-correction. This approach enables models to identify and rectify errors autonomously, successfully resolving up to 39.95% of previously failed queries without human intervention.