GRADE: Graph Representation of LLM Agent Dependency and Execution
Traces record what LLM agents did, leaving implicit what each step relied on. GRADE holds both in one graph; six published graph views are its projections. Execution edges come free; each costly dependency edge is graded as observed in the trace, declared by added logging, or inferred under an assumption, a schema we test one grade at a time. We recover the dependency layer from six tool-use, coding, and web corpora and measure its failure-prediction gain over run size. First, over a linear run-size baseline, it improves prediction on three corpora, but the gain depends on the learner and task sampling. On held-out corpora the layer stays above chance where run size does not, under one le...
| Authors | Yue Zhao |
|---|---|
| Primary category | cs.LG |
| Published | 22 June 2026 |
| Last updated at source | 6 October 2026 |
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