The Topology of Hallucination: Generative Intelligence Under Reduced External Correction

The Topology of Hallucination: Generative Intelligence Under Reduced External CorrectionCivilization Physics — Intelligence, Hallucination & Open-System Governance Series This paper argues that hallucination is not a domain-specific anomaly but a general structural failure mode of generative intelligence operating under reduced external correction. Across biological cognition, large language models, and social information systems, the same pattern appears: when a system continues generating interpretations while losing sufficient contact with reality, it increasingly fills missing information with internally generated structure. Hallucination is therefore treated not as a mysterious defect but as a topological consequence of weakened correction channels . The analysis begins by reframing hallucination as a cross-domain phenomenon. In humans, hallucination-like effects appear under sensory deprivation, sleep disruption, isolation, and extreme environments such as spaceflight. In artificial intelligence, hallucination appears as fabricated facts, invented citations, and confidently incorrect outputs. In social systems, it emerges as echo chambers, semantic narrowing, and collectively reinforced misconceptions. Although the underlying mechanisms differ, the paper argues that these systems share a common structural topology: a generative core, a boundary interface, and a correction channel whose weakening increases the probability of internally plausible but externally inaccurate outputs. A key conceptual distinction is made between generative capacity and corrective capacity. Intelligent systems continuously generate predictions, explanations, and completions under uncertainty. As long as correction remains strong, generated structure remains tethered to reality. When correction weakens, internally coherent narratives begin to outrun external truth. The paper grounds this framework in predictive-processing accounts of human cognition. Under predictive-processing models, perception emerges through interaction between priors and sensory evidence. Hallucination becomes more likely when top-down expectations dominate weakened sensory signals. Reduced sensory precision effectively shifts authority away from reality and toward internally generated predictions. This dynamic is illustrated through several forms of carbon-based evidence: Spaceflight environments, where astronauts report recurrent positive visual phenomena such as phosphenes under unusual sensory and radiation conditions. Sleep and circadian disruption, which weaken attentional control and cognitive calibration. Sensory deprivation experiments, where healthy individuals begin generating hallucination-like experiences when external inputs are significantly reduced. Predictive-processing studies, showing increased reliance on priors when sensory evidence becomes unreliable. The paper emphasizes that these phenomena do not imply pathology. Hallucination emerges because generation continues while correction degrades. The same topology is then extended to silicon-based systems. Large language models are described as probabilistic completion engines optimized for plausible continuation rather than guaranteed truth. Hallucination in LLMs is therefore interpreted as an expected consequence of strong generative capacity operating under weak grounding. Several empirical findings support this interpretation: TruthfulQA, demonstrating that larger models can become more convincing without necessarily becoming more truthful. HaluEval, documenting substantial rates of fabricated or unverifiable content. Model collapse research, showing degradation when synthetic outputs recursively replace contact with primary data. Synthetic recursion studies, demonstrating declining lexical, semantic, and conceptual diversity under repeated self-training. The paper argues that these findings reveal a broader principle: stronger generation alone does not guarantee stronger intelligence. Without sufficient correction, increased generative capacity amplifies error as effectively as it amplifies insight. A third domain of evidence comes from social and linguistic systems. Echo chambers, selective exposure, and information inbreeding create environments in which correction becomes costly while confirmation becomes cheap. Under these conditions: Repetition substitutes for verification. Group priors substitute for external reference. Salience substitutes for truth. Internally coherent narratives become increasingly detached from reality. The paper connects these dynamics to earlier concepts such as information inbreeding and the heat death of language, arguing that social discourse itself becomes more hallucinatory when correction channels narrow. To formalize the framework, the paper introduces a heuristic expression: H = k·G / (P·I·C + ε) where: H = hallucination propensity. G = generative pressure. P = Presence (contact with reality). I = Integrity (quality of correction channels). C = Calibration (effectiveness of error updating). k = scaling factor. ε = stabilizing term. The interpretation is straightforward: hallucination increases when generative pressure grows faster than the combined strength of presence, integrity, and calibration. A major implication is that hallucination should be understood as a governance problem rather than merely a technical defect. The paper argues that AI safety efforts should focus less on fears of mysterious emergent intelligence and more on preserving correction pathways through: High-integrity data sources. Provenance-preserving information systems. Retrieval and tool grounding. Human oversight. Calibration-sensitive evaluation benchmarks. Continued access to primary reality-bearing data. The paper also challenges certain AGI narratives. The vision of intelligence endlessly bootstrapping itself from its own outputs appears structurally unstable when examined through model-collapse evidence. Recursive generation without correction degrades diversity, truthfulness, and adaptability. Open-system intelligence therefore remains a more plausible model than self-contained recursive autonomy. The paper concludes that hallucination is a lawful risk of any generative system capable of producing meaning faster than it can verify it. Humans exhibit this under sensory compression and weakened perception. AI systems exhibit it under weak grounding and recursive training. Social systems exhibit it under informational closure. Across all domains, the same principle emerges: when correction weakens while generation remains active, internally plausible structure increasingly replaces reality. Within the Civilization Physics framework, hallucination becomes not a special defect of intelligence, but a predictable consequence of generative systems losing sufficient contact with the world. Keywords: Hallucination · Generative Intelligence · Predictive Processing · Model Collapse · Sensory Deprivation · LLM Hallucination · Information Inbreeding · Echo Chambers · Open-System Intelligence · Civilization Physics