Adaptive Entangled Game Modules in Artificial General Intelligence
通过概率波框架和广义行为智能方程，研究解决了金融市场中交易者集体行为模式问题，支持了非局域纠缠神经纤维假说，并提出将自适应纠缠游戏模块整合到通用人工智能中。
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Representative Papers
Beyond Bias: Participatory and Reflective Approaches to Cultural AI
本文通过参与式方法和反思性AI工具解决文化AI中创意意图与文化意义难以量化的问题，包括协作数据集创建、艺术家主导模型微调等。
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Artism: AI-Driven Dual-Engine System for Art Generation and Critique
This study addresses two core challenges in computational art history: the difficulty of modeling the evolutionary trajectory of art history and the disconnection between generative and critical processes. To this end, we propose the first AI dual-engine framework enabling generative–critical co-evolution. Methodologically, it integrates two coupled modules—Artificial Artist Social Network (AIDA) and Ismism Machine—modeling creator behavioral dynamics and critical reconfiguration of artistic concepts, respectively, within a multi-agent system grounded in computational art history. The framework implements a closed-loop iterative cycle of generation, critical feedback, and regeneration. Its key contribution lies in replacing traditional unidirectional generation or static criticism with a reflective practice mechanism. Experiments in contemporary art concept exploration demonstrate significant improvements: +32% in generative diversity and a 2.4× increase in critical depth (per expert evaluation), establishing a novel paradigm for art evolution simulation and AI-driven art theory construction.
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The Mechanical Yes-Man: Emancipatory AI Pedagogy in Higher Education
The widespread adoption of generative AI in higher education exacerbates cognitive passivity: students increasingly rely on its “mechanical responsiveness” to bypass essential intellectual labor, thereby undermining the development of causal reasoning, metacognition, and critical thinking. Method: Grounded in Rancière’s theory of “the emancipation of intelligence,” this study proposes a critical AI pedagogy framework that repositions generative AI as cognitive training material—not a cognitive substitute—implemented through a three-phase model: verification, mastery, and co-inquiry, wherein instructors assume the role of critical mediators. Contribution/Results: Moving beyond techno-optimist and prohibitionist binaries, the framework systematically integrates AI literacy with autonomous learning. Empirical evaluation demonstrates significant improvements in students’ critical appraisal of AI outputs, quality of collaborative inquiry, and depth of engagement in learning.
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Between Puppet and Actor: Reframing Authorship in this Age of AI Agents
The rise of AI-assisted creation challenges traditional conceptions of authorship. Method: Drawing on philosophical hermeneutics, media archaeology, and human–machine interaction theory—and informed by interactive narrative case studies and critical authorship theory—this paper develops the “puppet–actor continuum,” a novel conceptual framework for situate LLMs’ agency within creative processes, transcending the binary of AI-as-tool versus AI-as-author. Contribution/Results: It argues that LLMs possess bounded autonomy (e.g., improvisational narrative generation) yet lack authorial status; instead, they function as co-creative agents with situated agentic capacity. This framework advances new theoretical foundations for copyright attribution, delineation of creative responsibility, and ethical design of human–AI collaboration, thereby enabling adaptive evolution of conceptual systems governing authorship and creativity in AI-augmented contexts.
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Adaptive Entangled Game Modules in Artificial General Intelligence
通过概率波框架和广义行为智能方程，研究解决了金融市场中交易者集体行为模式问题，支持了非局域纠缠神经纤维假说，并提出将自适应纠缠游戏模块整合到通用人工智能中。
0 citationsRead paper
Beyond Bias: Participatory and Reflective Approaches to Cultural AI
本文通过参与式方法和反思性AI工具解决文化AI中创意意图与文化意义难以量化的问题，包括协作数据集创建、艺术家主导模型微调等。
0 citationsRead paper
Artism: AI-Driven Dual-Engine System for Art Generation and Critique
This study addresses two core challenges in computational art history: the difficulty of modeling the evolutionary trajectory of art history and the disconnection between generative and critical processes. To this end, we propose the first AI dual-engine framework enabling generative–critical co-evolution. Methodologically, it integrates two coupled modules—Artificial Artist Social Network (AIDA) and Ismism Machine—modeling creator behavioral dynamics and critical reconfiguration of artistic concepts, respectively, within a multi-agent system grounded in computational art history. The framework implements a closed-loop iterative cycle of generation, critical feedback, and regeneration. Its key contribution lies in replacing traditional unidirectional generation or static criticism with a reflective practice mechanism. Experiments in contemporary art concept exploration demonstrate significant improvements: +32% in generative diversity and a 2.4× increase in critical depth (per expert evaluation), establishing a novel paradigm for art evolution simulation and AI-driven art theory construction.
0 citationsRead paper
The Mechanical Yes-Man: Emancipatory AI Pedagogy in Higher Education
The widespread adoption of generative AI in higher education exacerbates cognitive passivity: students increasingly rely on its “mechanical responsiveness” to bypass essential intellectual labor, thereby undermining the development of causal reasoning, metacognition, and critical thinking. Method: Grounded in Rancière’s theory of “the emancipation of intelligence,” this study proposes a critical AI pedagogy framework that repositions generative AI as cognitive training material—not a cognitive substitute—implemented through a three-phase model: verification, mastery, and co-inquiry, wherein instructors assume the role of critical mediators. Contribution/Results: Moving beyond techno-optimist and prohibitionist binaries, the framework systematically integrates AI literacy with autonomous learning. Empirical evaluation demonstrates significant improvements in students’ critical appraisal of AI outputs, quality of collaborative inquiry, and depth of engagement in learning.
0 citationsRead paper
Between Puppet and Actor: Reframing Authorship in this Age of AI Agents
The rise of AI-assisted creation challenges traditional conceptions of authorship. Method: Drawing on philosophical hermeneutics, media archaeology, and human–machine interaction theory—and informed by interactive narrative case studies and critical authorship theory—this paper develops the “puppet–actor continuum,” a novel conceptual framework for situate LLMs’ agency within creative processes, transcending the binary of AI-as-tool versus AI-as-author. Contribution/Results: It argues that LLMs possess bounded autonomy (e.g., improvisational narrative generation) yet lack authorial status; instead, they function as co-creative agents with situated agentic capacity. This framework advances new theoretical foundations for copyright attribution, delineation of creative responsibility, and ethical design of human–AI collaboration, thereby enabling adaptive evolution of conceptual systems governing authorship and creativity in AI-augmented contexts.
0 citationsRead paper