✨Real-Time Reasoning Agents in Evolving Environments
📝 Summary:
AI agents struggle with real-time reasoning in dynamic environments, failing to balance logical judgments with timely responses. This paper introduces Real-Time Reasoning Gym and AgileThinker. AgileThinker combines reactive and planning approaches to effectively balance reasoning depth and respon...
🔹 Publication Date: Published on Nov 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.04898
• PDF: https://arxiv.org/pdf/2511.04898
• Project Page: https://realtimegym.saltlab.stanford.edu
• Github: https://github.com/SALT-NLP/RealtimeGym
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✓ https://t.iss.one/DataScienceT
#AI #RealTimeAI #AutonomousAgents #DynamicEnvironments #MachineLearning
📝 Summary:
AI agents struggle with real-time reasoning in dynamic environments, failing to balance logical judgments with timely responses. This paper introduces Real-Time Reasoning Gym and AgileThinker. AgileThinker combines reactive and planning approaches to effectively balance reasoning depth and respon...
🔹 Publication Date: Published on Nov 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.04898
• PDF: https://arxiv.org/pdf/2511.04898
• Project Page: https://realtimegym.saltlab.stanford.edu
• Github: https://github.com/SALT-NLP/RealtimeGym
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AI #RealTimeAI #AutonomousAgents #DynamicEnvironments #MachineLearning