OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning

The paper is well-structured, clearly outlining the problem, proposed solution, methodology, results, and discussion. The sections follow a logical flow, and the arguments are easy to follow.

Strengths: Methodology for vision encoder pre-training and MLLM fine-tuning is described in detail, referencing existing frameworks and datasets., Comprehensive evaluation is conducted across a broad suite of multimodal benchmarks., Extensive ablation studies are performed to isolate the impact of key design choices and training strategies., Comparison against multiple strong baselines (CLIP, SigLIP, other open CLIP variants) is provided under consistent evaluation frameworks (LLaVA-1.5, Open-LLaVA-Next).
Weaknesses: Specific hyperparameter details for all experiments might be extensive and not fully listed in the main text (though some ablation details are in the appendix)., While code is promised, the level of detail needed for *exact* reproduction of complex training procedures might require careful examination of the released code.

The paper provides extensive empirical evidence through multiple tables (Tables 1-9, 10-12 in Appendix) and figures (Figure 1-3) that directly support the key claims about OpenVision's performance relative to baselines, the impact of ablation factors, and the scaling behavior.

The primary contribution is the release of a *fully open, cost-effective family* of vision encoders that are shown to be competitive with or superior to proprietary models in multimodal contexts, combined with empirical analysis identifying key factors for their performance. While building on existing work like CLIPS and Recap-DataComp, the comprehensive open release, multi-scale evaluation, and specific ablation insights contribute significant novelty.

By providing fully open and performant vision encoders, code, and data, the paper has high potential to accelerate open-source research in multimodal AI, reduce dependence on proprietary models, and enable wider accessibility for research and deployment, particularly for resource-constrained applications.

Strengths: Formal and precise academic language is used throughout., Technical concepts are explained clearly, assuming background in vision-language models., The narrative flows logically, guiding the reader through the problem, solution, and findings.
Areas for Improvement: Some sentences are complex., Density of information in tables requires careful reading.