arXiv:2602.22631v2 Announce Type: replace-cross Abstract: Neural networks are increasingly deployed in scientific, safety critical, and mission critical pipelines, yet verification and analysis are often performed outside the programming environment that defines and runs the model. This creates a semantic gap between the executed network and the analyzed artifact: guarantees can depend on implicit conventions about operator semantics, tensor layouts, preprocessing, floating-point behavior, graph transformations, accelerated kernels, and external certificates. We present TorchLean, a unified fr
As AI models are increasingly deployed in critical applications, the need for formal verification is becoming paramount, driving innovation in tools like TorchLean.
Formalizing neural networks bridge the semantic gap between model development and verification, enhancing trustworthiness and reliability for AI in sensitive domains.
The ability to formally verify AI models within their development environments significantly reduces the risk of deployment errors and enhances auditability for regulated industries.
- · AI safety researchers
- · High-stakes AI integrators
- · Regulatory bodies
- · Academic institutions
- · AI developers ignoring verification
- · Companies with opaque AI systems
Increased adoption of formal verification methods within AI development pipelines.
Higher compliance standards and regulatory scrutiny for AI systems in critical infrastructure.
The acceleration of AI deployment into highly sensitive and autonomous roles due to enhanced trust and provable guarantees.
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Read at arXiv cs.LG