Constraint-Verified LLM Graph Workflow Synthesis with Execution-Guided Self-Repair, Parallel Optimization, and Trajectory Failure Prediction
Keywords:
LLM Agents, Workflow Synthesis, Directed Acyclic Graphs, Constrained Decoding, Structural Verification, Self-Repair, Failure Prediction, Confidence Calibration, Critical-Path Scheduling, WorfbenchAbstract
Large language model agents increasingly express multi-step work as directed graphs, yet a plausible action list does not guarantee correct dependencies, structural executability, or useful parallelism. This paper presents CVR-Graph, a verification layer combining dependency scoring, typed priors, diagnostic-guided repair, transitive reduction, graph-quality failure prediction, and critical-path scheduling. Evaluation used 18,679 WorFBench training records and all 2,146 official test workflows from nine domains. After overlap removal, de-duplication, and structural filtering, 17,128 workflows remained for development. A topology-controlled protocol held reference task nodes fixed and measured edge construction directly. Linear and parallel-optimized methods achieved edge F1 0.875, path F1 0.963, and strict graph success 0.754. Reduction removed 3,836 redundant predicted edges and increased edge F1 by 0.083 without changing path F1. Validator feedback also restored structural executability for all 31 released graphs with incomplete START-to-END coverage. Gradient-boosted trajectory screening achieved ROC-AUC 0.944, PR-AUC 0.952, Brier score 0.102, and expected calibration error 0.051; accepting the lowest-risk 50% reduced failure risk by 68.7%. Gold-DAG scheduling produced mean speedup 1.369 with eight workers, whereas predicted graphs remained chains. The results show that verification and calibrated control are effective, while non-linear dependency induction remains the limiting factor under domain shift.
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