✨WebOperator: Action-Aware Tree Search for Autonomous Agents in Web Environment

📝 Summary:
WebOperator is a tree-search framework that enhances web agents with reliable backtracking and strategic exploration. It addresses challenges like irreversible actions and partial observability by using a safety-aware search and verifying paths. WebOperator achieves state-of-the-art results on We...

🔹 Publication Date: Published on Dec 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.12692
• PDF: https://arxiv.org/pdf/2512.12692
• Project Page: https://kagnlp.github.io/WebOperator
• Github: https://kagnlp.github.io/WebOperator

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#WebAgents #TreeSearch #AI #AutonomousAgents #MachineLearning

✨AT^2PO: Agentic Turn-based Policy Optimization via Tree Search

📝 Summary:
AT^2PO is a framework for multi-turn agentic reinforcement learning. It uses a turn-level tree search with entropy-guided expansion and turn-wise credit assignment. This improves exploration, reward propagation, and policy optimization, achieving state-of-the-art results.

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04767
• PDF: https://arxiv.org/pdf/2601.04767
• Github: https://github.com/zzfoutofspace/ATPO

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#ReinforcementLearning #AgenticAI #TreeSearch #PolicyOptimization #ArtificialIntelligence