delegated workflows
2 published articles
AI2 min read
AI Research
Llms corrupt your documents when you delegate: a close look at the delegate-52 benchmark
The DELEGATE-52 benchmark reveals that current LLMs accumulate fidelity degradation when entrusted with multi-step document edits. Errors affect 19–34% of artifact content over 20 iterations, though Python workflows show less than 1% loss. The study is a diagnostic tool, not a verdict on real-world AI utility.
2026-07-05
AI3 min read
Research analysis
AI document corruption in delegated workflows: what a new stress test reveals
The DELEGATE-52 benchmark evaluates AI systems on long-horizon delegated document editing tasks, finding that frontier models accumulate semantic fidelity loss of 19–34% over 20 iterations. Python workflows showed less than 1% degradation on average, but the study underscores that reliable long-horizon delegation remains an open challenge.
2026-07-04