OpenVLHarness

Headlines · 1

1. Oct 18
   OpenVLHarness pairs tool use with multimodal memory, lifts scores 9.7–12.6 points

   An arXiv paper (arXiv:2610.26860, submitted 19 October 2026) by Yu Zhou and 10 co-authors introduces OpenVLHarness, a multimodal reasoning harness built around a capability layer and persistent multimodal memory. The capability layer combines task-level tool interfaces with evidence rendering, converting outputs from heterogeneous backends into model-readable observations, while persistent memory keeps the corresponding images, structured data and web references as addressable artifacts; a coding interface executes generated programs over that stored data. The authors report that across 22 benchmarks with eight open and proprietary backbones, OpenVLHarness improves average scores by 9.7–12.6 points over matched no-tool baselines, and that with GPT-6 Luna at medium reasoning effort it beats the base model, Codex and Visual Sketchpad at maximum effort by 7.7–8.5 points at up to 37% of their model-token cost.

   Hugging Face · Papers · 🔥 0

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