arXiv:2509.23806v2 Announce Type: replace-cross Abstract: Concolic testing for neural networks alternates concrete execution with constraint solving to search for inputs that flip model decisions. We present a concolic tester for Transformer classifiers that uses SHAP estimates to rank pending path predicates by their impact on the current prediction. To support self-attention with multiple heads in execution backed by SMT solving, we implement attention semantics in pure Python that are compatible with the solver and make the softmax boundary explicit by concretizing exponentiation arguments.
The rapid deployment of Transformer models in critical applications necessitates robust testing methods to ensure reliability and safety, driving this research into advanced concolic testing.
Improving the robustness and explainability of Transformer models is crucial for their trustworthy integration into sensitive systems, addressing key concerns around AI safety and reliability.
The ability to systematically test and identify vulnerabilities in Transformer models for 'decision flips' enhances their reliability and shifts focus towards explainable and verifiable AI systems.
- · AI Safety Researchers
- · Organizations deploying AI classifiers
- · AI security firms
- · Regulation & Compliance (AI)
- · Developers of unstable AI models
- · Adversarial AI actors
More robust and auditable AI models become available for deployment in critical sectors.
Increased public and institutional trust in AI systems due to improved reliability and understanding of failure modes.
New certification and assurance standards emerge for AI, potentially accelerating their adoption in highly regulated industries.
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Read at arXiv cs.LG