arxiv
PublishedMay 25, 2026 at 4:00 AM
—neutral
Lipschitz Optimization for Formal Verification of Homographies
Publisher summary· verbatim
arXiv:2605.23203v1 Announce Type: cross Abstract: The adoption of vision neural networks in regulated industries requires formal robustness guarantees, especially in safety-critical domains such as healthcare, autonomous vehicles, and aerospace. However, current approaches are confined to incomplete
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivA Consensus-Based Framework for Relative Preference Evaluation of Large Language Models17harxivProbing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders17harxivData Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA17harxivEnjoy Your Talk: A Human-Centered Benchmark for Multi-Turn Dialogue with Decoupled User Simulation, Target Modeling, and Judging17hThe Bubble Brief
WEEKLYRead computer-vision insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗