arXiv:2605.28729v1 Announce Type: cross Abstract: Robustness of neural networks is commonly quantified via local or global Lipschitz constants. However, Lipschitz continuity can be overly coarse or overly restrictive as global robustness measure, failing to capture nuanced, data-dependent behavior. We propose a data-driven, architecture-agnostic framework based on the discrete modulus of continuity (DMOC), a non linear generalization of Lipschitz continuity that provides a finer notion of robustness. Unlike many existing approaches, DMOC does not require access to model internals and instead e
The increasing deployment of neural networks in critical applications necessitates more robust and reliable methods for quantifying their behavior, moving beyond the limitations of current Lipschitz-based approaches.
This work introduces a more nuanced and data-driven way to assess AI robustness, which is critical for trustworthy AI development and deployment across various industries.
The ability to quantify AI model robustness without internal access broadens the scope of robust AI evaluation and could lead to more reliable and explicable AI systems.
- · AI developers focused on safety and reliability
- · Industries deploying AI in high-stakes environments
- · Researchers exploring AI explainability and trustworthiness
- · AI evaluation methods relying solely on global Lipschitz constants
- · Systems with opaque AI models that are difficult to verify
- · Developers neglecting robust AI practices
Improved understanding and quantification of AI model robustness becomes more widespread.
New standards and regulatory frameworks for AI robustness could emerge, leveraging data-driven metrics.
Increased public and institutional trust in AI systems due to better verifiable reliability and safety measures.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG