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• The proposed Recurrent Reasoning Vision-Language Model (R²VLM) achieves state-of-the-art performance in long-horizon task progress estimation.result 95%
• R²VLM uses a recurrent reasoning framework that processes local video snippets iteratively to avoid computational costs of processing long videos.method 90%
• The model maintains global context through an evolving Chain of Thought that records task decomposition, key steps, and completion status.method 90%
• Existing VLM-based methods for progress estimation primarily leverage video understanding capabilities while neglecting complex reasoning potential.limitation 85%
• Processing long video trajectories with VLMs is computationally prohibitive for real-world deployment.technical 85%
• R²VLM is trained on large-scale, automatically generated datasets from ALFRED and Ego4D.method 90%
• The model demonstrates strong generalization capabilities across multiple downstream applications.result 80%
• R²VLM enables progress-enhanced policy learning, reward modeling for reinforcement learning, and proactive assistance applications.technical 85%

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