SAGE-VQA: A Training-Free, Self-Evolving Agentic Framework with Grounded Evidence for Knowledge-Based Visual Question Answering

Knowledge-based visual question answering (KB-VQA) aims to combine visual information with external knowledge to answer user questions. Existing methods face two main bottlenecks: visual perception errors can propagate into hallucinations, and generalization across scenarios remains limited. To address these issues, we propose SAGE-VQA (Self-evolving Agent framework with Grounded Evidence), a training-free agentic framework that combines evidence verification with experience evolution. Our key insight is to explore an Evidence-Driven Adaptive Reasoning (EDAR) strategy. It accurately acquires and verifies question-relevant fine-grained visual information. It also extracts and refines reusable experience to support robust reasoning across tasks. Specifically, we design a Co-verified Visual Grounding module that verifies question-relevant visual information and provides grounded evidence for subsequent reasoning. We further introduce a Self-evolving Hierarchical Memory (SHM) mechanism. SHM organizes experiences into declarative and procedural memories based on their nature. These memories can be reused in subsequent reasoning and continually refined through feedback, without updating model parameters. Experimental results show that our method outperforms competing methods based on lightweight open-source vision-language models (VLMs) as well as those based on closed-source VLMs. Moreover, our method can serve as a plug-and-play reasoning framework for existing VLMs without modifying their architectures.