{"ID":2831257,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08740","arxiv_id":"2512.08740","title":"Deconstructing the Dual Black Box:A Plug-and-Play Cognitive Framework for Human-AI Collaborative Enhancement and Its Implications for AI Governance","abstract":"Currently, there exists a fundamental divide between the \"cognitive black box\" (implicit intuition) of human experts and the \"computational black box\" (untrustworthy decision-making) of artificial intelligence (AI). This paper proposes a new paradigm of \"human-AI collaborative cognitive enhancement,\" aiming to transform the dual black boxes into a composable, auditable, and extensible \"functional white-box\" system through structured \"meta-interaction.\" The core breakthrough lies in the \"plug-and-play cognitive framework\"--a computable knowledge package that can be extracted from expert dialogues and loaded into the Recursive Adversarial Meta-Thinking Network (RAMTN). This enables expert thinking, such as medical diagnostic logic and teaching intuition, to be converted into reusable and scalable public assets, realizing a paradigm shift from \"AI as a tool\" to \"AI as a thinking partner.\" This work not only provides the first engineering proof for \"cognitive equity\" but also opens up a new path for AI governance: constructing a verifiable and intervenable governance paradigm through \"transparency of interaction protocols\" rather than prying into the internal mechanisms of models. The framework is open-sourced to promote technology for good and cognitive inclusion. This paper is an independent exploratory research conducted by the author. All content presented, including the theoretical framework (RAMTN), methodology (meta-interaction), system implementation, and case validation, constitutes the author's individual research achievements.","short_abstract":"Currently, there exists a fundamental divide between the \"cognitive black box\" (implicit intuition) of human experts and the \"computational black box\" (untrustworthy decision-making) of artificial intelligence (AI). This paper proposes a new paradigm of \"human-AI collaborative cognitive enhancement,\" aiming to transfor...","url_abs":"https://arxiv.org/abs/2512.08740","url_pdf":"https://arxiv.org/pdf/2512.08740v1","authors":"[\"Yiming Lu\"]","published":"2025-12-09T15:50:15Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"LoRA\"]","has_code":false}