Abstract
The semantic gap between distributed system specifications and implementations leads to significant development costs and runtime failures. This paper presents UPIR (Universal Plan Intermediate Representation), which combines formal verification, program synthesis, and machine learning to automatically generate verified implementations from high-level specifications. The system introduces three key components: (1) a compositional verification engine using SMT solving that achieves O(1) incremental complexity through proof caching and dependency tracking, reducing verification time by up to 274x compared to monolithic approaches; (2) a synthesis engine applying Counterexample-Guided Inductive Synthesis (CEGIS) to distributed systems, generating implementations in 1.97ms average with 43-75% success rates; and (3) a constrained reinforcement learning optimizer using Proximal Policy Optimization (PPO) that improves system parameters while maintaining formal guarantees, converging within 45 episodes. Experimental validation across 100 test iterations demonstrates the feasibility of the approach. The system achieves 274x speedup for 64-component systems through compositional verification, demonstrates 89.9% pattern reuse potential through clustering analysis, and shows 60.1% latency reduction with 194.5% throughput increase in benchmark tests. The .upir intermediate representation provides a formal bridge between specifications and implementations, enabling practical application of formal methods to industrial-scale distributed systems.
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Recommended Citation
Mitra, Subhadip, "Automated Synthesis and Verification of Distributed Systems Using Universal Plan Intermediate Representation (UPIR)", Technical Disclosure Commons, (November 10, 2025)
https://www.tdcommons.org/dpubs_series/8852