IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment

Image Quality Assessment (IQA) in open-world environmentsremains challenging due to limited generalization and interpretability.Recent approaches based on multimodal large language models (MLLMs)introduce textual reasoning for quality prediction, yet their judgmentsrely heavily on semantically biased internal representations, making theminsensitive to low-level perceptual degradations. We propose IQA-T1, atool-based visual evidence reasoning framework that augments MLLMreasoning with explicit perceptual observations. During inference, themodel autonomously invokes specialized analysis tools to generate struc-tured visual evidence, such as noise residual maps, gradient statistics,and frequency spectra, which are progressively integrated into the rea-soning process. To support this paradigm, we construct Q-Tool, a datasetcontaining 11k multimodal reasoning chains grounded in tool-generatedevidence. Extensive experiments on seven IQA benchmarks show thatIQA-T1 achieves the best overall performance across datasets while pro-ducing interpretable and evidence-grounded quality assessments. Codeand dataset are available at https://github.com/zibuyu-02/IQA-T1.