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.

The latest industrial inference engines, such as FasterTransformer and TurboTransformers, have verified that half-precision floating point (FP16) and 8-bit integer (INT8) quantization can greatly improve model inference speed. However, the existing INT8 quantization methods are too complicated, and improper usage will lead to model performance damage greatly. In this paper, we develop a toolkit for users to easily quantize their models for inference, in which Self-Adaptive Mixed-Precision (SAMP) is proposed to automatically control quantization rate by a mixed-precision architecture to balance model accuracy and efficiency. Experimental results show that our SAMP toolkit has a higher speedup than PyTorch and FasterTransformer while ensuring the required accuracy. In addition, SAMP is based on a modular design, decoupling the tokenizer, embedding, encoder and target layers, which allows users to handle various downstream tasks and can be seamlessly integrated into PyTorch.