arXiv:2511.13663v2 Announce Type: replace-cross Abstract: How to construct globally sound abstract interpreters to safely approximate program behaviors remains a bottleneck in abstract interpretation. In this paper, we show the potential of using state-of-the-art LLMs to automate this tedious process. Focusing on the neural network verification area, we synthesize non-trivial sound abstract transformers across diverse abstract domains using LLMs to search within infinite space from scratch. We formalize the synthesis task as a constrained optimization problem, for which we design a novel mathe
The accelerating capabilities of large language models (LLMs) are enabling novel applications in highly complex symbolic domains like program analysis and verification, pushing the boundaries of what these models can automate.
This breakthrough suggests that LLMs can automate highly specialized, human-intensive tasks in software verification, significantly improving the reliability and reducing the cost of complex software systems, particularly in critical applications like neural network verification.
The ability to synthesize sound abstract interpreters using LLMs changes the paradigm for formal verification, moving from manual expert design to AI-assisted automation, potentially democratizing access to advanced program analysis techniques.
- · AI/ML Developers
- · Cybersecurity Sector
- · Critical Infrastructure Operators
- · Formal Verification Tool Vendors
- · Manual Software Verification Specialists
Automated generation of more robust and secure software through advanced AI-driven verification techniques.
Increased adoption of formal methods across various industries due to lower barriers to implementation and improved efficiency.
A future where AI-designed and AI-verified software components become the norm, reducing vulnerabilities stemming from human error in complex systems.
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Read at arXiv cs.LG