[2603.03175] Saarthi for AGI: Towards Domain-Specific General Intelligence for Formal Verification

Computer Science > Artificial Intelligence arXiv:2603.03175 (cs) [Submitted on 3 Mar 2026] Title:Saarthi for AGI: Towards Domain-Specific General Intelligence for Formal Verification Authors:Aman Kumar, Deepak Narayan Gadde, Luu Danh Minh, Vaisakh Naduvodi Viswambharan, Keerthan Kopparam Radhakrishna, Sivaram Pothireddypalli View a PDF of the paper titled Saarthi for AGI: Towards Domain-Specific General Intelligence for Formal Verification, by Aman Kumar and 5 other authors View PDF Abstract:Saarthi is an agentic AI framework that uses multi-agent collaboration to perform end-to-end formal verification. Even though the framework provides a complete flow from specification to coverage closure, with around 40% efficacy, there are several challenges that need to be addressed to make it more robust and reliable. Artificial General Intelligence (AGI) is still a distant goal, and current Large Language Model (LLM)-based agents are prone to hallucinations and making mistakes, especially when dealing with complex tasks such as formal verification. However, with the right enhancements and improvements, we believe that Saarthi can be a significant step towards achieving domain-specific general intelligence for formal verification. Especially for problems that require Short Term, Short Context (STSC) capabilities, such as formal verification, Saarthi can be a powerful tool to assist verification engineers in their work. In this paper, we present two key enhancements to the Saarthi fr...