Preserving Epistemic Integrity: A Framework for Anthropocentric Cognitive Signatures (ACS) to Mitigate Recursive Synthetic Contamination

As generative artificial intelligence systems increasingly dominate the digital information ecosystem, they initiate a recursive loop of self-consumption, training on model-generated data scraped from the web. This phenomenon, mathematically conceptualized as "model collapse," leads to the progressive erosion of the tail-ends of human data distributions and the long-term homogenization of knowledge. Current mitigation strategies, such as watermarking and heuristic classification, fail to scale or survive post-generation processing. This paper introduces the Anthropocentric Cognitive Signatures (ACS) framework, an information-theoretic and cryptographic protocol designed to identify, preserve, and certify human-origin knowledge. Recognizing that modern generative models can closely mimic human style, ACS models the high-dimensional mathematical patterns and cognitive invariants-such as semantic divergence, fractal complexity in syntax, and contextual dissonance-which are statistically truncated during synthetic sampling. We propose the architecture of the Pristine Knowledge Vault (PKV), a decentralized, zero-knowledge cryptographic ledger designed to catalog and support the sovereign epistemic integrity of human records. We demonstrate that the ACS validation filter theoretically bounds test-error propagation under recursive training loops even under non-ideal validation conditions, offering a mathematically motivated preservation framework to mitigate the potential epistemic collapse of the digital commons

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