I’ve been exploring a hypothesis that might explain a persistent puzzle in large language models — why they hallucinate so confidently, even when trained on vast, high-quality data.

The core idea is what I’m calling velocity bias — a systematic distortion that arises when models learn from virality-optimized information ecosystems.

The Hypothesis

Vosoughi et al. (Science, 2018) showed that false information spreads roughly 70% faster than truth on social media. When large-scale language models are trained on web data drawn from those same ecosystems, the result is not random noise — it’s a statistical skew: high-velocity, low-accuracy content becomes disproportionately represented in the training corpus.

If that distributional skew is consistent across corpora, then models may internalize velocity as a proxy for validity. They learn that “fast = frequent = probable,” even when that correlation is inverted in reality.

This could mean that hallucination is not just a side effect of architecture or sampling — but an inherited property of virality-weighted data.

Key Mechanistic Pathways

Velocity–Exposure Coupling: Web-scale scrapers sample proportionally to visibility or link density, not epistemic quality. High-velocity content dominates by volume.
Confidence Transfer: Models trained on corpora where confident, attention-maximizing language outperforms cautious verification may encode stylistic confidence as a reliability prior.
Feedback Loops: As AI-generated content enters the web (estimated 18–36 month window), the velocity–accuracy anticorrelation could self-reinforce, amplifying hallucination rather than correcting it.

I outline these dynamics formally in Paper 1: Training Data Velocity Bias (Zenodo, 2025).

Systemic Extension — Vital Network Science

The companion paper, Vital Network Science Framework (Zenodo, 2025), explores the same problem at the ecosystem level.

The proposition is simple: instead of trying to regulate content, regulate tempo.

Platforms already govern the speed and exposure of information flows — they just optimize for engagement (φ) rather than coherence (ψ). The VNS framework reframes that optimization target, defining a “vitality index” (ψ) across seven measurable dimensions: coherence, resilience, civility, epistemic quality, diversity, agency, and affective balance.

The system applies proportional temporal damping when information velocity exceed...

Posts

Wikitag Contributions

Comments

Posts

Wikitag Contributions

Comments