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Generating Workflow DAGs from Natural Language with Non-Reasoning LLMs

Anand Iyer, Bhanu Khetharpal, Srinivas Upadhya, Ramkumar Rajagopal · Aug 31, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 42% Moderate protocol signal Freshness: Hot Status: Ready
Automatic Metrics General
  • Our central diagnostic is an emission-density bottleneck: on a 635-rule benchmark of manufactured synthetic data, models select the correct graph nodes with high accuracy but increasingly misconfigure attributes and Boolean grouping as the…
  • Across four models, the full system reaches approximately 89% LLM-judge validity, approximately 90% exact-match condition accuracy, and 99-100% valid JSON while using roughly half the per-rule prompt tokens of a monolithic prompt.
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