OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind
The article discusses a research paper titled "OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind." It highlights various tools and platforms associated with the paper, including bibliographic tools, code repositories, and demo platforms provided through arXivLabs.
A new research paper, "OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind," is now accessible. This paper explores advanced concepts in artificial intelligence. Its availability on arXiv signifies a contribution to the scientific community. The paper is available in various formats, including PDF, HTML (experimental), and TeX Source. It was submitted by Kazi Mahathir Rahman and four other authors. Bibliographic tools such as Google Scholar, Semantic Scholar, and BibTeX are available for citation. To further support the research, tools like Connected Papers, Litmaps, and scite.ai Smart Citations are provided. For researchers interested in the computational aspects, code and data are available through alphaXiv, CatalyzeX Code Finder, DagsHub, Gotit.pub, Huggingface, and ScienceCast. Demonstrations of the work can be explored via Replicate, Hugging Face Spaces, and TXYZ.AI. These platforms offer interactive ways to understand and engage with the research. arXivLabs, a framework supporting experimental projects, facilitates these integrations. It promotes openness, community, excellence, and user data privacy. This collaborative environment ensures that valuable projects benefit the arXiv community while adhering to core values.
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