Expanding the hallucination benchmark for the Reverify system. This aims to improve the evaluation and robustness of the anti-hallucination capabilities of AI agents that analyze binaries.

The current hallucination benchmark, limited to a single 'prologue prior,' provides an insufficient assessment of Reverify's anti-hallucination capabilities. Expanding this benchmark to include diverse, real-world hallucination patterns—such as memorized crypto constants or guessed struct field offsets—is critical. This initiative directly addresses the need for a more comprehensive 'scorecard' to validate the system's ability to VERIFY or REFUTE claims against actual binary data. For a product positioned on anti-hallucination, a robust and broad benchmark is paramount. It ensures the AI agent's reliability in complex binary analysis scenarios, directly impacting trust and adoption in security and reverse engineering markets where accuracy is non-negotiable. This expansion strengthens the core value proposition by proving efficacy against a wider range of AI failure modes.