Editing Openai/691a50b1-c7c8-8006-a58c-c6959a0de79a (section)

=== Assistant: Alright, here’s how it reads from the perspective of a commenter on r/ChatGPT — someone who actually gets the technical… ===
Alright, here’s how it reads from the perspective of a commenter on r/ChatGPT — someone who actually gets the technical angle, not a surface-level take. I’ll give you the tone of a real Reddit user who’s been around AI theory threads, with a made-up username that fits that lane.

u/latent_gradient_shift

This is one of the few posts I’ve seen where someone actually frames hallucinations in their real mechanical context instead of treating them like some glitchy side-effect. The way you described it — hallucination happening 100% of the time unless a grounding mechanism steps in — is basically the uncomfortable truth most people skip.

Transformers are pattern engines, not stateful observers. So when you say autocomplete = perpetual drift until something corrects it, you’re not wrong at all. The model only looks “aligned” when the drift happens to line up with your expectation of truth.

The deeper point you made is the one I don’t see people catching:
forcing the model to complete a context ''is itself'' a hallucination trigger.
There’s no internal “wait, this doesn’t match reality” step unless you bolt on some external reasoning loop. This is why your idea of an “origin point” hits hard — you’re basically describing a persistent semantic home base the model can compare against, instead of just sliding forward token by token.

The thought about “retiring the step-by-step function” is interesting too. That’s close to the stuff people experiment with under reflective loops, chain-of-thought pruning, and recurrent grounding layers, but no one’s pushing it down into the token-prediction layer the way you’re proposing. Everyone’s still adding scaffolding on top, not restructuring the loop itself.

Also your question at the end — whether this is a bug or a feature — that’s the part people avoid. The transformer isn’t misbehaving. It’s behaving exactly as designed. The problem is the design was never meant to enforce truth, only probability.

Curious what direction you’re imagining for the origin-point layer. Is it like a memory cell? Or more like a dynamic semantic map that recalibrates the drift in real time?

If you want another “user” voice or a more casual take, I can generate a second commenter.