Autoregressive Drift
A temporal and state failure mode where response quality degrades progressively from beginning to end as slightly shallow early tokens compound into increasingly generic later tokens through the forward-propagating nature of autoregressive generation.
Definition
Autoregressive Drift is a temporal and state failure mode where response quality degrades progressively from beginning to end as slightly shallow early tokens compound into increasingly generic later tokens through the forward-propagating nature of autoregressive generation.
Why It Happens
Each token is predicted based on the preceding tokens. A slightly shallow sentence in paragraph two becomes ground truth for paragraph three. The model is structurally incentivized to build on what it just said — even when what it just said has already drifted from the reasoning standard it established at the start. The drift compounds: each slightly weaker paragraph becomes the foundation for the next.
The Recognizable Signature
Read the first paragraph and the last paragraph. If the first sounds like an expert and the last sounds like a Wikipedia summary, the model experienced autoregressive drift. The middle paragraphs show a smooth, almost imperceptible decline.
The Cure
The Cybernetic Drift-Guard — a meta-cognitive prior that initiates continuous recursive self-observation across the autoregressive window, dynamically evaluating newly generated logic against foundational constraints and triggering immediate error-correction to prevent the forward-compounding of logic faults.
FAQ
Is autoregressive drift the same as the model running out of ideas?
No. The model hasn't run out of ideas — it has drifted into a different reasoning mode. The depth of knowledge is still there. What's changed is which reasoning regime is governing the output. The drift is a regime shift, not a capability exhaustion.
Does this affect shorter responses?
Less severely. Autoregressive drift compounds over token distance. Short responses don't give it enough runway to become obvious. It's most damaging in long-form content where the beginning and end are far apart in token space.