The Topology of Multimodal Fusion: Why Current Architectures Fail at Creative Cognition

• URL: http://arxiv.org/abs/2604.04465v1
• Date: Mon, 06 Apr 2026 06:23:55 GMT
• Title: The Topology of Multimodal Fusion: Why Current Architectures Fail at Creative Cognition
• Abstract summary: This article identifies a limitation in current multimodal AI architectures that is topological rather than parametric.<n>A cruciform framework positions xiang at the pathological intersection, executing dual huacai (transformation-and-cutting) along both axes.<n>This generates a dual-layer dynamics: chuanghua (creative transformation as spontaneous event) and huacai (its institutionalization into repeatable form)<n>We propose UOO implementation via LIPs with topological regularization, the ANALOGY benchmark with error-type metricratio, and the archetypes-TOP three-tier benchmark testing cross-civilizational topological isomorphism
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper identifies a structural limitation in current multimodal AI architectures that is topological rather than parametric. Contrastive alignment (CLIP), cross-attention fusion (GPT-4V/Gemini), and diffusion-based generation share a common geometric prior -- modal separability -- which we term contact topology. The argument rests on three pillars with philosophy as the generative center. The philosophical pillar reinterprets Wittgenstein's saying/showing distinction as a problem rather than a conclusion: where Wittgenstein chose silence, the Chinese craft epistemology tradition responded with xiang (operative schema) -- the third state emerging when saying and showing interpenetrate. A cruciform framework (dao/qi x saying/showing) positions xiang at the intersection, executing dual huacai (transformation-and-cutting) along both axes. This generates a dual-layer dynamics: chuanghua (creative transformation as spontaneous event) and huacai (its institutionalization into repeatable form). The cognitive science pillar reinterprets DMN/ECN/SN tripartite co-activation through the pathological mirror: overlap isomorphism vs. superimposition collapse in a 2D parameter space (coupling intensity x regulatory capacity). The mathematical pillar formalizes these via fiber bundles and Yang-Mills curvature, with the cruciform structure mapped to fiber bundle language. We propose UOO implementation via Neural ODEs with topological regularization, the ANALOGY-MM benchmark with error-type-ratio metric, and the META-TOP three-tier benchmark testing cross-civilizational topological isomorphism across seven archetypes. A phased experimental roadmap with explicit termination criteria ensures clean exit if falsified.

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