The "Robert Boulton" Singularity: Semantic Tunneling and Manifold Unfolding in Recursive AI

• URL: http://arxiv.org/abs/2602.02526v1
• Date: Tue, 27 Jan 2026 01:19:50 GMT
• Title: The "Robert Boulton" Singularity: Semantic Tunneling and Manifold Unfolding in Recursive AI
• Abstract summary: We identify a novel failure mode termed "Semantic Tunneling"<n>We show that Adaptive Spectral Negative Coupling acts as a topological operator that actively induces "Manifold Unfolding"<n>MNCIS forces the model to expand its effective rank from the anisotropic baseline of 3.62 to a hyper-diverse state of 5.35.
• Score: 1.4213973379473657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The stability of generative artificial intelligence trained on recursive synthetic data is conventionally monitored via Perplexity (PPL). We demonstrate that PPL is a deceptive metric in context-stabilized regimes (L=128). Using a rigorous sliding-window protocol (N=1500), we identify a novel failure mode termed "Semantic Tunneling." While the Baseline model maintains high grammatical fluency (PPL approx. 83.9), it suffers a catastrophic loss of semantic diversity, converging within seven generations to a single, low-entropy narrative attractor: the "Robert Boulton" Singularity. This phenomenon represents a total collapse of the latent manifold (Global Effective Rank 3.62 -> 2.22), where the model discards diverse world knowledge to optimize for statistically safe syntactic templates. To address this, we apply the Multi-Scale Negative Coupled Information Systems (MNCIS) framework recently established in Hou (2026) [arXiv:2601.11594]. We demonstrate that Adaptive Spectral Negative Coupling (ASNC) acts as a topological operator that actively induces "Manifold Unfolding." MNCIS forces the model to expand its effective rank from the anisotropic baseline of 3.62 to a hyper-diverse state of 5.35, effectively constructing an "Artificial Manifold" that resists the gravitational pull of semantic attractors and preserves the long-tail distribution of the training data.

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