The Visual Research Agent Kept Its Evidence Chain

Finding a relevant image and finding a relevant source do not establish how they support the same answer. OneSearch-VL uses a Visually Grounded Evidence Graph to preserve those dependencies during data construction, process supervision and evaluation. Its training sets contain 110,000 supervised examples and 10,000 reinforcement-learning examples, with rewards aimed at traceability and visual grounding. The eight-billion-parameter agent exceeded a tool-enabled Qwen3-VL-8B baseline by 20.2 and 17.6 percentage points on the authors’ new multi-image and video benchmarks, with gains on additional evaluations. Those results are benchmark-specific. The useful architectural move is making the connection between an observed region and a source-supported claim explicit.