Semantic Self-Verification in Autonomous Knowledge Networks#

Abstract: As automated systems and AI agents generate exponentially more information, distinguishing between hallucinated data and verifiable truth becomes computationally intractable. We propose a framework where a dataset validates its own internal semantic structure to establish ground-truth reliability. By treating knowledge not as isolated facts but as a topologically connected graph, semantic self-verification allows a system to prove its own logical consistency without relying on an external, centralized oracle.

1. Introduction: The Hallucination Horizon#

The proliferation of Large Language Models (LLMs) has created an epistemological crisis known as the Hallucination Horizon. When synthetic data is continuously fed back into training models, the models undergo a process of model collapse—they begin to confidently output mathematically coherent but ontologically false statements.

The traditional solution to hallucination is to ground the model in an external, human-curated database (RAG). However, this merely shifts the point of failure to the curation layer, which is vulnerable to the same entropic decay and adversarial poisoning described in our Theory of Epistemic Autopoiesis.

2. Semantic Self-Verification Defined#

Semantic Self-Verification is the property of a dataset wherein every axiom, definition, and statement is structurally cross-referenced and validated by other internal statements in a continuous, non-contradictory loop.

A system is self-verifying if and only if:

  1. Axiomatic Anchoring: The system possesses a base set of cryptographic hashes linking to raw, immutable empirical data (e.g., source code, peer-reviewed methodology).
  2. Topological Density: The conceptual distance between any two statements in the network is fully traversable via logical, machine-readable links (JSON-LD, RDF triples).
  3. Self-Resolution of Contradiction: When a new statement is introduced, an autonomous agent traces its logical dependencies through the graph. If it contradicts an existing highly-connected node without sufficient evidentiary weight, the new statement is rejected as anomalous noise.

3. The LLM as an Immune System#

In a self-verifying system, Large Language Models are repurposed. Rather than acting as generators of novel information, they act as the cognitive immune system of the dataset.

Upon every Git commit or data ingestion, the LLM is tasked with:

  • Semantic Parsing: Translating human-readable markdown into strict graph representations.
  • Coherence Auditing: Searching for logical paradoxes or disconnected, orphaned concepts.
  • Structural Healing: Generating new links that connect isolated concepts back to the primary canonical core.

This process ensures that the Knowledge Fortress maintains a perfect internal mathematical consistency, analogous to a formalized mathematical proof but applied to natural language and cultural archives.

4. Conclusion#

Semantic Self-Verification shifts the burden of truth verification from human consensus (which is slow, biased, and fragile) to topological network density. A self-verifying system cannot be poisoned because false information, by definition, cannot fully integrate into the dense, pre-existing logical structure of the graph without triggering cascading contradictions that the automated immune system will immediately flag and quarantine.