@gerardsans
@_galyo “Hallucination” is still the wrong abstraction. Frontier LLMs don’t fail because they occasionally detach from truth. They fail because they never had direct truth access to begin with. Transformers are proposition generators, not assertion engines. They interpolate over corpus geometry: • coherence • co-occurrence • discourse priors • token-density topology not external reality. So when a model says something false with high confidence, that’s not necessarily a malfunction. It’s often the architecture operating exactly as designed: maximizing corpus consistency, not world verification. The key distinction: • Assertions require exogenous grounding (sensors, databases, experiments, humans) • Propositions only require endogenous plausibility LLMs only do the second one. This is why “metacognition” alone won’t solve hallucinations. Mapping probability diffuseness → hedging language (“I may be wrong…”) is useful UX, but it’s still an internal statistical reflex inside the same closed system. The map is still verifying the map. Scaling, RLHF, and self-reflection improve discourse discipline, but they don’t create epistemic grounding. The real architectural shift is separation of concerns: Generation ≠ Verification 1) LLMs generate candidate propositions. 2) External systems verify against reality. That’s the missing layer. The future probably looks less like “models that know truth” and more like: • stochastic generators • deterministic verifiers • provenance-aware reasoning stacks • explicit assertion/proposition labeling Not bigger autocomplete. Chaining LLMs doesn’t result in introspection or self-correction. They’re expanded interpolation paths. Longer reasoning traces ≠ epistemology. See the semiotic triad to see the gaps in your current mental model.
