{"ID":2850611,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.21370","arxiv_id":"2510.21370","title":"HIKMA: Human-Inspired Knowledge by Machine Agents through a Multi-Agent Framework for Semi-Autonomous Scientific Conferences","abstract":"HIKMA Semi-Autonomous Conference is the first experiment in reimagining scholarly communication through an end-to-end integration of artificial intelligence into the academic publishing and presentation pipeline. This paper presents the design, implementation, and evaluation of the HIKMA framework, which includes AI dataset curation, AI-based manuscript generation, AI-assisted peer review, AI-driven revision, AI conference presentation, and AI archival dissemination. By combining language models, structured research workflows, and domain safeguards, HIKMA shows how AI can support - not replace traditional scholarly practices while maintaining intellectual property protection, transparency, and integrity. The conference functions as a testbed and proof of concept, providing insights into the opportunities and challenges of AI-enabled scholarship. It also examines questions about AI authorship, accountability, and the role of human-AI collaboration in research.","short_abstract":"HIKMA Semi-Autonomous Conference is the first experiment in reimagining scholarly communication through an end-to-end integration of artificial intelligence into the academic publishing and presentation pipeline. This paper presents the design, implementation, and evaluation of the HIKMA framework, which includes AI da...","url_abs":"https://arxiv.org/abs/2510.21370","url_pdf":"https://arxiv.org/pdf/2510.21370v1","authors":"[\"Zain Ul Abideen Tariq\",\"Mahmood Al-Zubaidi\",\"Uzair Shah\",\"Marco Agus\",\"Mowafa Househ\"]","published":"2025-10-24T11:52:24Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.AI\",\"cs.CL\",\"cs.DL\"]","methods":"[\"Language Model\"]","has_code":false}