Do frontier models fail differently than weaker models?

DELEGATE-52 surfaces an under-discussed asymmetry in how LLM document degradation looks at different capability tiers. Weaker models fail loudly: they delete content. The document gets visibly shorter, sections disappear, structure breaks. A reviewer notices.

Frontier models fail quietly. Their degradation comes from corruption of existing content — values flipped, references rewritten, edits applied in the wrong place — producing documents that look intact at a glance but contain accumulated drift. The corruption mode is more dangerous than the deletion mode precisely because it preserves the surface signal of competence. The thing that looks like a successful workflow output is the thing that has silently drifted.

This matters for adoption. The "frontier models are reliable" intuition is built from short-interaction benchmarks where the corruption mechanism barely activates. At workflow scale — the regime where delegation is actually useful — the failure changes character, and the qualitative shift toward harder-to-detect failures means that improvements in raw capability can degrade overall workflow reliability if review effort is held constant.

The implication for delegated-AI design is that capability improvements at the frontier need to be paired with detection mechanisms that target corruption-style errors, not just deletion-style errors. Diff review, document-state checksums, and constraint validators become more important as models get better, not less.