Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems
The paper presents Inductive Deductive Synthesis (IDS), a novel approach for enabling AI to generate formally verified systems. IDS significantly improves the efficiency of generating implementations and proofs, achieving results much faster and cheaper than traditional expert methods. This advancement addresses the limitations of current AI agents in formal verification tasks, particularly in distributed systems.
- ▪Inductive Deductive Synthesis (IDS) synthesizes implementation and proof incrementally.
- ▪IDS achieves 7/7 specifications in about 6.8 hours and $106 per spec, which is roughly 200 times faster than expert efforts.
- ▪The approach incorporates performance feedback, yielding implementations up to 3 times faster than previously published verified systems.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23109 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2605.23109 (cs) [Submitted on 22 May 2026] Title:Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems Authors:Shubham Agarwal, Alexander Krentsel, Shu Liu, Mert Cemri, Audrey Cheng, Rui Meng, Tomas Pfister, Chun-Liang Li, Sylvia Ratnasamy, Aditya Parameswaran, Matei Zaharia, Ion Stoica, Mohsen Lesani View a PDF of the paper titled Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems, by Shubham Agarwal and 12 other authors View PDF HTML (experimental) Abstract:AI agents increasingly excel at generating, testing, and refining code. However, they fall short on tasks requiring formal guarantees of full coverage that testing alone cannot provide.
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