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cs.CV2026
DRILL: Training World Models to Improve Policies, Not Predict Pixels
Zhaolu Kang, Tailong Luo, Lei Wei +9
World models are increasingly used to reduce environment interaction when post-training vision-language-action (VLA) policies with reinforcement learning. Yet they are typically tr…
cs.CV2026
VisualNeedle: Benchmarking Active Visual Search in Information-Dense Scenes
Jingru Chen, Yiming Liu, Mingtao Chen +5
Frontier multimodal large language models (MLLMs) have been reported to achieve over 90\% accuracy on fine-grained perception benchmarks. However, such scores do not necessarily im…
cs.CV2025
FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation
Zheqi He, Yesheng Liu, Jing-shu Zheng +5
We present FlagEvalMM, an open-source evaluation framework designed to comprehensively assess multimodal models across a diverse range of vision-language understanding and generati…