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

arXiv – CS AI|Aman Kumar, Deepak Narayan Gadde, Luu Danh Minh, Vaisakh Naduvodi Viswambharan, Keerthan Kopparam Radhakrishna, Sivaram Pothireddypalli|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers have enhanced the Saarthi AI framework for formal verification, achieving 70% better accuracy in generating SystemVerilog assertions and 50% fewer iterations to reach coverage closure. The framework uses multi-agent collaboration and improved RAG techniques to move toward domain-specific AI intelligence for verification tasks.

Key Takeaways

→Saarthi framework improved from 40% to significantly higher efficacy through structured rulebook and GraphRAG integration.
→The enhanced system achieved 70% improvement in assertion accuracy and 50% reduction in required iterations.
→Framework targets Short Term, Short Context (STSC) capabilities specifically for formal verification tasks.
→Multi-agent collaboration approach addresses LLM hallucination issues in complex verification scenarios.
→Benchmarking used NVIDIA's CVDP formal verification test cases to validate improvements.

#artificial-intelligence #formal-verification #multi-agent #rag #systemverilog #nvidia #benchmark #agi #domain-specific-ai

Read Original →via arXiv – CS AI

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