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 learner, with the same task-sampling caveat. Second, structure can pass for signal it does not carry: a layer inferred under full history restates run size. Within a corpus, a preregistered test does not separate the recovered layer from a degree-matched counterfeit that rewires each step's dependencies but keeps their number. The recovered layer's largest transfer lead over it reverses when the held-out corpus's task family is withheld. Before crediting structure, grade each edge, keep a run-size baseline, and score degree-matched attachment controls.
Comments: 35 pages. Expanded analyses and revised figures. Code: yzhao062/grade
Mentions
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 learner, with the same task-sampling caveat. Second, structure can pass for signal it does not carry: a layer inferred under full history restates run size. Within a corpus, a preregistered test does not separate the recovered layer from a degree-matched counterfeit that rewires each step's dependencies but keeps their number. The recovered layer's largest transfer lead over it reverses when the held-out corpus's task family is withheld. Before crediting structure, grade each edge, keep a run-size baseline, and score degree-matched attachment controls. Comments: 35 pages. Expanded analyses and revised figures. Code: yzhao062/grade