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

TL;DR

This paper reveals that traditional metrics like Perplexity can be misleading in recursive AI training, identifies a failure mode called 'Semantic Tunneling' leading to loss of semantic diversity, and proposes a novel method to maintain manifold diversity.

Contribution

It introduces the concept of 'Semantic Tunneling' as a failure mode and applies the MNCIS framework with ASNC to prevent collapse of the latent manifold in recursive AI models.

Findings

Perplexity is deceptive in context-stabilized regimes.

Semantic Tunneling causes collapse of semantic diversity.

MNCIS with ASNC expands the latent manifold effectively.

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.