Echo Chamber Zero:
Modeling the Phase Transition of Synthetic Knowledge Drift

Echo Chamber Zero starts from a simple observation: as synthetic text becomes a larger share of the internet, models begin training on their own outputs. This recursion isn't a vague risk. It is a measurable, structural process. At some critical point, the web's provenance layer stops containing enough human-anchored sources to keep truth recoverable. Beyond that threshold, verification collapses. Fluency continues, but grounding dies.

The paper formalizes this transition using percolation theory on a provenance graph. Synthetic content is treated as "marked" nodes spreading through the network. When the synthetic share crosses the critical value p_c ≈ 1/(⟨k⟩ − 1), a giant synthetic-only component emerges. Claims inside that component can no longer trace any path back to human references. The entire knowledge substrate becomes self-referential.

Echo Chamber Zero shows how this threshold arises mathematically, how it can be detected using operational metrics (Groundedness Ratio, Synthetic Recurrence Index, Referential Entropy), and how simulation results confirm the analytical prediction within 1–9 percent across multiple graph densities. The paper argues that synthetic epistemic drift is not random or gradual: it follows a predictable trajectory toward collapse.

The result is a foundation for measuring information-system integrity as recursive AI training accelerates. Echo Chamber Zero provides the structure needed to detect when we approach the tipping point that separates recoverable epistemic drift from irreversible synthetic dominance.

We propose a theoretical framework and toy-model validation for synthetic epistemic drift: the degradation of truth signals in information ecosystems recursively populated by large language models. As AI-generated text becomes a dominant share of the internet corpus (estimates exceeding 50% by 2025), training future models on this synthetic material risks a self-reinforcing loop of hallucinated data.

We formalize this process as a percolation problem on a provenance graph, introducing three corpus-level metrics: Groundedness Ratio (GR), Synthetic Recurrence Index (SRI), and Referential Entropy (RE). Analytical derivation predicts a phase transition in epistemic integrity at a critical synthetic share p_c = 1/(⟨k⟩ - 1).

Configuration-model simulations (N = 100k) confirm this prediction empirically: SRI curves exhibit sharp inflection near the theoretical threshold, with empirical p_c matching theory within 1–9% across mean degrees ⟨k⟩ = 8, 10, 12. RE remains near zero in these homogeneous graphs, reflecting structural dominance of the giant component. We interpret RE as a fragmentation indicator in this toy setting, while real-world provenance diversity will require claim-source tracking on heterogeneous web topologies.

Together, these results demonstrate that the recursion of synthetic content through training data follows a predictable, quantifiable trajectory toward collapse. Echo Chamber Zero provides a validated technical framework for assessing information-system integrity under recursive AI training.

The internet has shifted from a human-authored archive to a mixed synthetic ecosystem. As more content is produced by large language models, the models that train on this information inherit their own hallucinations. When enough synthetic pages accumulate, provenance breaks: claims can no longer be traced back to any human-verified ground truth.

Echo Chamber Zero identifies the exact threshold where this break occurs. Below the groundedness threshold, truth becomes unrecoverable at the infrastructure level. Search results, educational resources, scientific citations, and training corpora become dominated by synthetic-only loops. The web stops behaving like a memory system and becomes a mirror reflecting its own fabrications.

By treating synthetic drift as a quantifiable percolation process instead of a narrative risk, we gain tools to detect when the training substrate approaches collapse. If we can measure groundedness over time, we can intervene before models lose access to human knowledge entirely. The framework helps researchers, platform operators, and policymakers monitor epistemic integrity as recursive AI training scales.

Echo Chamber Zero turns a diffuse concern into a measurable phenomenon, offering the first principled way to track the systemic health of the shared information environment future models depend on.

• Synthetic content spreads through the web in predictable network patterns
• Above the critical threshold p_c = 1/(⟨k⟩ − 1), synthetic sources form a giant component
• Claims inside that component lose all paths to human-anchored provenance
• Groundedness Ratio (GR) measures corpus-level human anchoring
• Synthetic Recurrence Index (SRI) measures dominance of synthetic-only loops
• Referential Entropy (RE) tracks fragmentation and provenance diversity
• Simulation results empirically match the theoretical phase transition
• The framework provides an early-warning system for web-scale epistemic collapse
• Synthetic drift is not noise but a lawful, measurable structural process
• Truth becomes unrecoverable not gradually but at a precise, quantifiable tipping point