Autoregressive and diffusion frameworks compared
Research context
theory comparison · established · As of 2026-10-10
An autoregressive model factors a sequence law into conditional next-element distributions, while a diffusion model generates through a learned reverse noise process.
Key knowledge
An autoregressive model factors a sequence law into conditional next-element distributions, while a diffusion model generates through a learned reverse noise process. Transformers can implement autoregressive conditionals but are an architecture rather than the factorization itself. These frameworks trade sequential access, conditioning, likelihood/training objectives and sampling cost. Neither framework intrinsically guarantees factuality, differential privacy or robustness; the evidence must match the task and mechanism.
Conditions and scope
- Compare implementations under the same data, conditioning, compute and evaluation protocol.
- Model fit, sampling fidelity and verified knowledge retrieval are separate outputs.
Sources · 2
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
Evidence and review
Author checked this English statement, displayed equation where present and stated hypotheses against these source locations: §3.2 scaled dot-product attention, encoder/decoder and translation experiments; §§2–3 forward Gaussian chain, learned reverse process and simplified denoising objective; §4 experiments. The check is bounded to the recorded claims; no complete proof reconstruction, software proof replay or universal literature completeness is claimed.
2026-10-10 · Author source-body/statement/equation/domain check, with graph associations scoped individually. Separate independent content/source/inventory review is required before branch closure.
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