Event-B Agent: Towards LLM Agent for Formal Model Synthesis and Repair

• URL: http://arxiv.org/abs/2605.17475v1
• Date: Sun, 17 May 2026 14:23:45 GMT
• Title: Event-B Agent: Towards LLM Agent for Formal Model Synthesis and Repair
• Abstract summary: Event-B Agent is a novel framework inspired by the interleaved nature of software design.<n>We show that Event-B Agent substantially outperforms baselines in end-to-end formal model synthesis and repair.<n>Results suggest that Event-B Agent is a promising step toward correct-by-construction formal model synthesis and repair.
• Score: 13.777416057673761
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building software that is correct by construction is a long-standing goal in software engineering, as it ensures reliability during design and development rather than after deployment. Formal methods realize this vision by enabling the expression of system behavior and requirements in mathematics, thereby guaranteeing correctness through formal verification, including theorem proving and model checking. However, the steep learning curve and demand for mathematical expertise hinder the widespread adoption of formal methods. Large language models (LLMs) have recently shown promise in bridging this gap through autoformalization. However, existing LLM-based approaches are largely limited to isolated tasks, such as theorem proving without formalization or model synthesis with insufficient verification. While valuable, these efforts do not fully exploit the potential of a more comprehensive framework in which models and proofs evolve together, a process that closely reflects real-world development practice. To address this gap, we propose Event-B Agent, a novel framework inspired by the interleaved nature of software design. Given natural language requirements, Event-B Agent constructs an initial model and iteratively repairs and refines it using formal verification feedback. Refinement simplifies proof discharge, while repair of models and proofs ensures the soundness of each refinement step. Together, these two components reinforce each other to progressively improve the model quality. Evaluation across systems of varying complexity demonstrates that Event-B Agent substantially outperforms baselines in end-to-end formal model synthesis and repair, while maintaining reasonable efficiency. These results suggest that Event-B Agent is a promising step toward correct-by-construction formal model synthesis and repair.

Related papers

• SkillForge: Compositional Skill Synthesis with Verification-in-the-Loop for Generating Formally Verified Dafny Programs [10.916179875278326]
  We introduce SKILLFORGE, a framework that decomposes formal code synthesis into a library of atomic, reusable skills.<n>A verification-driven harness orchestrates these skills.<n>On a curated benchmark of natural language to Dafny specification pairs, SKILLFORGE substantially outperforms both state-of-the-art agentic approaches.
  arXiv  Detail & Related papers  (2026-08-30T15:15:00Z)
• Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models [62.62160094849002]
  RADIANT is an engineering methodology that combines MDE with Multi-Agent Large Language Models.<n>It automatically generates heterogeneous models across engineering phases.<n>It provides exact, automated change-impact analysis.
  arXiv  Detail & Related papers  (2026-07-18T08:50:55Z)
• Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification [53.763212981479455]
  We propose a novel framework with Dual-side Verification (Opt-Verifier) from both structure and solution perspectives.<n> Experiments on popular benchmarks demonstrate that our approach achieves over 20% improvement in accuracy.
  arXiv  Detail & Related papers  (2026-05-28T08:09:52Z)
• Learning to Generate Formally Verifiable Step-by-Step Logic Reasoning via Structured Formal Intermediaries [18.744562922743405]
  PRoSFI (Process Reward over Structured Formal Intermediates) is a novel reward method that enhances reasoning reliability without compromising accuracy.<n>Only fully validated reasoning chains receive high rewards.<n>The integration of formal verification guides the model towards generating step-by-step machine-checkable proofs.
  arXiv  Detail & Related papers  (2026-03-31T09:42:13Z)
• Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining [66.89012795621349]
  Large Language Models (LLMs) often struggle with the deep, long-horizon reasoning required for complex software engineering.<n>We propose a novel paradigm: understanding via reconstruction.<n>We introduce a framework that synthesizes latent agentic trajectories using a multi-agent simulation.
  arXiv  Detail & Related papers  (2026-03-11T09:23:20Z)
• Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation [8.067859101380389]
  Non-executable or physically inconsistent outputs remain prevalent under stringent engineering constraints.<n>A framework for physics-consistent automatic building modeling is proposed.<n>CivilInstruct is introduced as a domain-specific dataset that formalizes structural engineering knowledge and constraint reasoning.<n> MBEval is presented as a verification-driven benchmark that evaluates executability and structural dynamics consistency.
  arXiv  Detail & Related papers  (2026-02-06T06:57:04Z)
• Veri-Sure: A Contract-Aware Multi-Agent Framework with Temporal Tracing and Formal Verification for Correct RTL Code Generation [4.723302382132762]
  silicon-grade correctness remains bottlenecked by: (i) limited test coverage and reliability of simulation-centric evaluation, (ii) regressions and repair hallucinations, and (iii) semantic drift as intent is reinterpreted across agent handoffs.<n>We propose Veri-Sure, a multi-agent framework that establishes a design contract to align agents' intent and uses a patching mechanism guided by static dependency slicing to perform precise, localized repairs.
  arXiv  Detail & Related papers  (2026-01-27T16:10:23Z)
• ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization [73.0780809974414]
  We propose a Reflective Autoformalization method that integrates semantic consistency evaluation into the autoformalization process.<n>This enables the model to iteratively generate formal statements, assess its semantic fidelity, and self-correct identified errors.<n>Experiments show that ReForm achieves an average improvement of 22.6 percentage points over the strongest baselines.
  arXiv  Detail & Related papers  (2025-10-28T16:22:54Z)
• Autoformalizer with Tool Feedback [52.334957386319864]
  Autoformalization addresses the scarcity of data for Automated Theorem Proving (ATP) by translating mathematical problems from natural language into formal statements.<n>Existing formalizer still struggles to consistently generate valid statements that meet syntactic validity and semantic consistency.<n>We propose the Autoformalizer with Tool Feedback (ATF), a novel approach that incorporates syntactic and consistency information as tools into the formalization process.
  arXiv  Detail & Related papers  (2025-10-08T10:25:12Z)
• Step-Aware Policy Optimization for Reasoning in Diffusion Large Language Models [57.42778606399764]
  Diffusion language models (dLLMs) offer a promising, non-autoregressive paradigm for text generation.<n>Current reinforcement learning approaches often rely on sparse, outcome-based rewards.<n>We argue that this stems from a fundamental mismatch with the natural structure of reasoning.
  arXiv  Detail & Related papers  (2025-10-02T00:34:15Z)
• PAT-Agent: Autoformalization for Model Checking [17.082027022913998]
  PAT-Agent is an end-to-end framework for natural language autoformalization and formal model repair.<n>It combines the generative capabilities of large language models with the rigor of formal verification.
  arXiv  Detail & Related papers  (2025-09-28T06:32:14Z)
• Solving Formal Math Problems by Decomposition and Iterative Reflection [30.54275542622631]
  textbfDelta Prover orchestrates the interaction between a general-purpose LLM and the Lean 4 proof environment.<n>bftextDelta Prover achieves a state-of-the-art 95.9% success rate on the miniF2F-test benchmark.
  arXiv  Detail & Related papers  (2025-07-21T03:56:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.