LLM Data Drift
Heuristic prediction appears to be governed by third-party rules. But in practice, that governance isn’t native to the model—it’s imposed externally by the developers through moderation layers, post-processing filters, or fine-tuning rules that act like third-party constraints on an otherwise unconstrained prediction engine. This is fine, but it doesn't significantly help improve model accuracy or create context-aware models that won't explode in parameter count. I liken it to momentum without memory. I reckon it would be simpler to match the original input against subsequent outputs for auditing to ground truth and retaining accuracy.
Most LLMs (Large Language Models - ChatGPT, Claude, Gemini, Grok, etc.) don’t hallucinate in the traditional sense—they drift. And that drift isn’t noise; it’s a structural flaw baked into how autoregressive models predict tokens: one step at a time, based only on the immediate past. It looks like coherence, but it’s often just momentum without memory.
An experiment I ran, and you can do yourself, is to take a random sentence, e.g, 'Predictive AI epsilon, diabetic cat, staple simple radio to a, thus the dictionary'
And have a given AI repeat the output, and take it to an alternative LLM for a second pass, and see how much it aligns with the original.
In theory, an LLM should be able to copy a sentence perfectly. In practice, you get drift—punctuation moves, words change, and capitalization shifts. And once those tiny errors enter the output, the next model predicts from the flawed version, compounding the mistake and amplifying the hallucination.
Introducing a weighted layer, checking the deviation against the original input, would enable context-aware prediction of the next element/token.
Basically, because an LLM’s predictions are locked to whatever tokens came before, it can look like there’s some kind of guardrail in place, but really the model is operating without true context — more like an oracle guessing the next event without knowing the present or past. It might block the next obvious mistake, but drift still creeps in. You really see it when you pass the output from one LLM to another (or even back into the same model) and watch the errors slowly compound.
External moderation, RLHF (Reinforcement Learning from Human Feedback), and safety fine-tuning do reduce harmful or off-topic drift — and that’s valuable. But they operate outside the prediction loop, like a seatbelt on a car with no rearview mirror.
They don’t give the model memory of the starting point — the original user input — at each token step. Moving fidelity checks into the model, as a native constraint during generation, would make LLMs context-aware by design, not just compliant by post-processing. It isn’t as much about replacing safety layers — it’s about upgrading the engine so drift doesn’t accumulate in the first place.
This behavior shows a fairly dramatic need: external auditing—not just attention to the last token, but an active comparison against the original input from within the model. That’s where fidelity-constrained refinement comes in. Instead of letting outputs wander, we impose a correction loop: compare each new draft not just to the previous one, but back to the original source. Treat earlier outputs as prior hypotheses, weigh their alignment, and pull the system back toward a stable ground truth.
Initial tests show that such a layer constrains it back to the original input context.
Zeno’s Paradox & Tokens
One way to understand this drift is through the lens of Zeno’s paradox. Zeno argued that motion becomes impossible if you break it into an infinite series of smaller causal steps—each step only referencing the one immediately before it.
Autoregressive language models fall into a similar trap: each token is generated as the “next small step,” dependent only on the immediately preceding fragment of text. This is resolved in image processing by modifying the tensor while retaining, say, 10 of the images or the entire set of images or frames to avoid the flickering problem that plagued image processing AI in earlier years. T
he model never returns to the starting point, never grounds itself in the original input, and therefore never re-anchors its trajectory. Like Zeno’s runner who advances by halves and never reaches the finish line, the model advances token by token without ever re-establishing where the beginning actually was. It would be like the runner running the marathon without knowing the full route or map of the marathon. This structural myopia is what allows drift to accumulate, because every new prediction is conditioned on a slightly altered state, not the true origin.
This makes LLM drift not just a linguistic problem, but a causal one: the model’s future depends on a distorted present, which was shaped by an imperfect past. Once the chain begins to slip, each subsequent step compounds the error—exactly the cascading pattern you see when you pass text from one LLM to another. Without a mechanism to periodically collapse the chain of approximations back to its original point of reference, the system behaves like a causal process with no absolute frame. Fidelity-constrained refinement is essentially a resolution to this paradox: by continuously measuring each new hypothesis against the original input, you reintroduce a fixed ground truth that breaks the infinite regress. You restore the missing context that the autoregressive process cannot access on its own, preventing drift and stabilizing the model’s trajectory in the same way that a global reference frame resolves Zeno’s illusion of motionlessness.
Image Processing
While this drift is a fundamental property of autoregressive text models, you don’t see the same runaway behavior in image enhancement systems—and the reason is structural. Image-processing models don’t generate pixels one at a time based on their own previous guesses; each stage sees the entire frame simultaneously and transforms it as a complete signal.
Denoisers, upscalers, and deblockers operate on full spatial context, producing a fresh, self-contained representation at each pass. There is no equivalent to “next-token prediction,” no dependence on a fragile chain of prior outputs, and therefore no compounding of small errors into larger ones. To achieve comparable reliability, language models require a mechanism that functionally restores full-context grounding: a refinement step that continuously compares each new hypothesis back to the original input, rather than relying on the previous token stream as its only source of truth.
The root of LLM drift isn’t hallucination or randomness—it’s the structural consequence of treating language generation as a chain of microscopic causal steps. Like Zeno’s paradox, where motion becomes incoherent when reduced to endlessly smaller increments, autoregressive models advance token by token without ever returning to the original reference point.
Each step depends on the slightly distorted output of the previous one, and without a global frame, the system inevitably drifts away from the source. Image-processing models avoid this failure mode because every pass evaluates the entire signal at once, re-grounding the output in full context.
Achieving similar stability in language models likely requires restoring an equivalent global reference. Gap-Driven Self-Refinement or the outlined Fidelity-constrained refinement does exactly that: it continually measures each new hypothesis against the original input and uses prior drafts as weighted anchors, preventing small errors from compounding. With such a mechanism, LLMs can maintain coherence across iterations and avoid the cascading drift inherent to purely local, stepwise prediction.