arXiv:2607.01813v1 Announce Type: cross Abstract: Evaluation benchmarks are essential for assessing vision-language models (VLMs), but most multimodal benchmarks are static, making them vulnerable to temporal staleness, data contamination, and costly maintenance. We present MMBench-Live, a continuously evolving multimodal benchmark built by a multi-agent-driven automated pipeline. Our framework treats benchmark evolution as task-guided dataset construction, integrating structured benchmark specification, feedback-controlled real-time data acquisition, and verifiable QA generation with executab
The proliferation of advanced multimodal models necessitates more dynamic and robust evaluation methods to counter rapid model evolution and data contamination.
Reliable and continuously evolving benchmarks are critical for accurately assessing the progress and true capabilities of AI models, preventing misleading performance metrics and guiding research direction.
The standard approach to evaluating multimodal AI, moving from static datasets to dynamically updated and verifiable benchmarks, will now be more rigorous and less susceptible to gaming.
- · AI researchers
- · AI developers focused on robust models
- · Organizations relying on VLM accuracy
- · Developers gaming static benchmarks
- · Models overfitting to outdated datasets
Improved model comparison and identification of genuine advancements in multimodal AI capabilities.
Accelerated development of more generalized and less biased multimodal models due to transparent evaluation.
Enhanced trust and responsible deployment of multimodal AI systems in real-world applications, leading to wider adoption.
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Read at arXiv cs.AI