[2605.03159] Learning Correct Behavior from Examples: Validating Sequential Execution in Autonomous Agents
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arXiv:2605.03159 (cs)
[Submitted on 4 May 2026]
Title:Learning Correct Behavior from Examples: Validating Sequential Execution in Autonomous Agents
Authors:Reshabh K Sharma, Gaurav Mittal, Yu Hu
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Abstract:As autonomous agents become increasingly sophisticated, validating their sequential behavior presents a significant challenge. Traditional testing approaches require manual specification, exact sequence matching, or thousands of training examples. We present a novel algorithm that automatically learns correct behavior from just 2-10 passing execution traces and validates new executions against this learned model. Our approach combines dominator analysis from compiler theory with multimodal large language model-powered semantic understanding to identify essential states and handle non-deterministic behavior. The system constructs a generalized ground truth model using Prefix Tree Acceptors, merges traces through multi-tiered equivalence detection, and validates new executions via topological subsequence matching. In controlled experiments, our system achieved high accuracy in detecting product bugs and false successes using only 3 training traces. This approach provides explainable validation results with coverage metrics and works across diverse domains including UI testing, code generation, and robotic processes.
Subjects:
Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2605.03159 [cs.AI]
(or arXiv:2605.03159v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.03159
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Reshabh K Sharma [view email] [v1] Mon, 4 May 2026 21:07:56 UTC (67 KB)
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