Model-to-Model Collision (M2MC): A Conceptual Framework for Quantifying Cross-Model Semantic Invalidation in Autonomous Multi-Vendor AI Ecosystems

As generative AI transitions from isolated consumer utilities to deeply integrated autonomous enterprise pipelines, a critical systems-level risk domain remains unaddressed in existing literature: the real-time semantic degradation that emerges when heterogeneous, multi-vendor AI models consume, reinterpret, and re-synthesize each other's outputs at scale. Existing frameworks - including Model Collapse and the Synthetic Data Contamination Index ; address recursive intra-model degradation but do not account for the compounding interpretive distortions introduced at cross-model boundaries. This paper introduces Model-to-Model Collision (M2MC) and formalizes the concept of Cross-Model Semantic Invalidation (CMSI) -a progressive loss of semantic fidelity occurring when architecturally divergent models interact without human anchoring layers. We identify three primary structural mechanics driving this phenomenon: Tokenization Phase Shift, Guardrail Reinforcement Compression, and Latent Geometry Asymmetry. A conceptual quantitative formulation is proposed via the Cross-Model Semantic Invalidation Index (CMSII), grounded in Shannon entropy, Jaccard divergence, and Kullback-Leibler distribution distance as theoretical constructs. The paper further proposes a strategic mitigation architecture comprising the Epistemic Anchor Protocol (EAP), Tokenization Isolation Layers, and Dynamic Divergence Resetting - implemented as a Zero-Dependency Sovereign Validation prototype in native PHP. All thresholds and metrics are declared as preliminary heuristics pending empirical calibration. This work is intentionally positioned as a conceptual and architectural foundation paper. It does not assert empirical conclusions but establishes a formal vocabulary, risk taxonomy, and structural direction for future interdisciplinary investigation into AI interoperability, semantic integrity governance, and autonomous multi-model system safety.

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