OpenVLHarness shows modest gains from multimodal memory in visual reasoning
A preprint study tests a multimodal tool that stores and uses visual data across steps, finding small improvements but noting limits and costs.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the abstract
- Authors
- Yu Zhou, Cheng-Fu Yang, Rui Sun, Di Wu, Sibo Peng, Mingyang Zhang, Zhen Yang, Ruiyi Zhang, Nanyun Peng, Zhe Gan, Kai-Wei Chang
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
A controlled study on 22 benchmarks found that simply increasing the inference budget of a base model yields only modest gains for visual reasoning tasks. OpenVLHarness adds a capability layer and persistent multimodal memory to organize tool outputs and evidence across steps, improving average scores by about 9.7–12.6 points over baselines and lowering token costs in some cases.
Why it matters
The approach suggests practical ways to organize external tools and evidence when reasoning with images, with gains that depend on the task and resource use.
OpenVLHarness introduces a way to keep images, data, and links in memory during a task, so the model can reuse evidence without starting over. In tests across 22 benchmarks, it raised average scores compared with no-tool baselines, though the size of gains varied by backbone and setup.
A practical takeaway is that linking outputs from different tools and keeping a memory of what the model saw can help accuracy, but benefits are not guaranteed for every task and may come with higher complexity and cost.
What remains uncertain
This is an abstract-only preprint; results are not finalized and may change in peer review.
Original sources · 1
Check the original paper for its authors, methods, version and access terms.
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