Arbor: Tree Search as a Cognition Layer for Autonomous Agents
The research paper "Arbor: Tree Search as a Cognition Layer for Autonomous Agents" explores the application of tree search mechanisms to enhance the cognitive abilities of AI agents. This approach aims to improve decision-making and problem-solving in autonomous systems.
A new research paper titled "Arbor: Tree Search as a Cognition Layer for Autonomous Agents" has been published, exploring advancements in artificial intelligence. This paper investigates the use of tree search mechanisms to enhance the cognitive capabilities of autonomous AI agents. The research, by Neha Prakriya and seven co-authors, was submitted on June 10, 2026.
The paper is available in various formats, including PDF, HTML (experimental), and TeX source. It is categorized under cs.AI, focusing on topics related to artificial intelligence.
Additional resources linked to the article include bibliographic tools like Google Scholar and Semantic Scholar for citations, and platforms for code, data, and media such as alphaXiv, CatalyzeX Code Finder, DagsHub, Huggingface, and ScienceCast. Demonstrations are accessible via Replicate, Hugging Face Spaces, and TXYZ.AI.
Related paper recommenders and search tools, including Influence Flower and CORE Recommender, are also provided to help researchers explore connected works. These tools facilitate a deeper engagement with the research and its broader context within the AI community. The paper highlights the ongoing collaboration with arXivLabs, an initiative supporting experimental projects that align with values of openness, community, excellence, and user data privacy.
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