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ARXIV:2603.25111 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.25111AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEDebangshu Banerjee · Changming Xu · Gagandeep Singh · arXiv
A framework for generating self-evolving LLM agents with formal guarantees of safety and correctness.
Opportunity summary
Pain A framework for generating self-evolving LLM agents with formal guarantees of safety and correctness.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A framework for generating self-evolving LLM agents with formal guarantees of safety and correctness. In this paradigm, a planner LLM synthesizes an agent program that invokes parametric models, including LLMs, which are then tuned…
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. In this paradigm, a planner LLM synthesizes an agent program that invokes parametric models, including LLMs, which are then tuned per task to improve…
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A framework for generating self-evolving LLM agents with formal guarantees of safety and correctness.
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10.48550/arXiv.2603.25111A framework for generating self-evolving LLM agents with formal guarantees of safety and correctness.
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 framework: Search synthesizes candidate parametric programs containing FGGM calls; Verification proves correctness with respect to hard constraints for all parameter values, reducing the problem to unconstrained learning; and Learning applies scalable gradient-based optimization, including GRPO-style fine-tuning, to improve the soft objective while preserving correctness. We evaluate SEVerA on Dafny program verification, symbolic math synthesis, and policy-compliant agentic tool use ($τ^2$-bench). Across tasks, SEVerA achieves zero constraint violations while improving performance over unconstrained and SOTA baselines, showing that formal behavioral constraints not only guarantee correctness but also steer synthesis toward higher-quality agents.
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PROBLEM
A framework for generating self-evolving LLM agents with formal guarantees of safety and correctness. 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.
METHOD
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 improv...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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.
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework for generating self-evolving LLM agents with formal guarantees of safety and correctness. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for generating self-evolving LLM agents with formal guarantees of safety and correctness.
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4.0/10 public viability
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