Expansion Artifacts

Matt Ström-Awn argues that flaws in LLM outputs are “expansion artifacts,” not compression artifacts, and cites 2024 evidence that they can be tracked. He notes Stanford researchers estimated AI-drafted text in 17.5% of recent CS papers and 16.9% of peer reviews from post-ChatGPT word-frequency shifts, and contrasts this with a JPG after 10,000 recompressions reaching PSNR 14.59. The point for practitioners is forensic: these artifacts expose both model aesthetics and generation provenance.

Why it matters: HKR-H lands on the “expansion artifacts” hook; HKR-K adds concrete numbers and a testable provenance claim; HKR-R hits peer-review trust and detection anxiety. It stays at 73 because this is personal-blog commentary, not a primary research or product release event.