[2603.25111] SEVerA: Verified Synthesis of Self-Evolving Agents

Computer Science > Machine Learning arXiv:2603.25111 (cs) [Submitted on 26 Mar 2026] Title:SEVerA: Verified Synthesis of Self-Evolving Agents Authors:Debangshu Banerjee, Changming Xu, Gagandeep Singh View a PDF of the paper titled SEVerA: Verified Synthesis of Self-Evolving Agents, by Debangshu Banerjee and 2 other authors View PDF HTML (experimental) Abstract:Recent advances have shown the effectiveness of self-evolving LLM agents on tasks such as program repair and scientific discovery. In this paradigm, a planner LLM synthesizes an agent program that invokes parametric models, including LLMs, which are then tuned per task to improve performance. However, existing self-evolving agent frameworks provide no formal guarantees of safety or correctness. Because such programs are often executed autonomously on unseen inputs, this lack of guarantees raises reliability and security concerns. We formulate agentic code generation as a constrained learning problem, combining hard formal specifications with soft objectives capturing task utility. We introduce Formally Guarded Generative Models (FGGM), which allow the planner LLM to specify a formal output contract for each generative model call using first-order logic. Each FGGM call wraps the underlying model in a rejection sampler with a verified fallback, ensuring every returned output satisfies the contract for any input and parameter setting. Building on FGGM, we present SEVerA (Self-Evolving Verified Agents), a three-stage fra...