The Visual Agent Keeps the Evidence It Found

More inference did little on 22 visual benchmarks when the missing ingredient was external evidence. OpenVLHarness adds a capability layer that renders heterogeneous tool results into model-readable observations and persistent memory that keeps images, data and references addressable. Across eight evaluated backbones, the authors report average gains of 9.7 to 12.6 points over matched no-tool baselines. In one comparison, a medium-effort smaller model beat maximum-effort systems while using no more than 37 percent of their model-token cost. These are harness-specific results, but they show why tool access and evidence retention must be evaluated together.