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EdgeGen is a synthetic task generation framework that creates database-grounded edge-case tasks to improve tool-calling agents through fine-tuning and harness optimization, demonstrating consistent performance improvements.
This paper introduces VATS, a mutation-driven framework that systematically evolves adversarial payloads to exploit error-path injection in MCP-based tool-calling agents. It demonstrates that error messages with implicit authority triple the success rate of standard indirect prompt injection across frontier models.
SynAE is a framework for evaluating the quality of synthetic data used in tool-calling agent evaluations, assessing validity, fidelity, and diversity across multiple axes. It addresses challenges of insufficient or sensitive real data by providing metrics to guide synthetic data generation.