Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes

Abstract

While Multimodal large language models (MLLMs) demonstrate impressive proficiency in benign scenarios, their cognitive reliability significantly deteriorates in complex scenes characterized by adverse conditions. In such scenarios, models relying on implicit inference devoid of visual evidence succumb to disconnects between perception and logic; simultaneously, existing outcome-oriented benchmarks fail to diagnose the underlying reasoning process. To bridge this gap, we present AD2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {AD}^2$$\end{document}-Bench, which establishes a rigorous Hierarchical Visual Diagnosis to decompose reasoning into a structured Chain of evidence (CoE). This granular perspective reveals that robust cognition is predicated on precise evidence acquisition. From this perspective, we formulate reasoning probabilistically, attributing the collapse to two bottlenecks: (1) Spatial Ambiguity, failing to distinguish targets from background clutter (i.e., localization error); and (2) Semantic Uncertainty, misinterpreting semantics due to feature degradation (i.e., understanding error). To resolve these evidence gaps, we propose Evidence-grounded visual reasoning (EGVOR). Departing from implicit inference, EGVOR reformulates reasoning as the explicit generation of Evidence Atoms–structured triplets that enforce strict spatial-semantic alignment. We orchestrate training via a hierarchical curriculum, progressing from reflective supervision establishment to reinforcement learning that explicitly rewards the reduction of reasoning variance. Extensive experiments demonstrate that EGVOR substantially mitigates instability, establishing a robust framework for trustworthy multimodal cognition.