arXiv: cs.AI preprintsInternational11 November 2026
Robustness Verification in Neural Networks
This is an official announcement record
Firsthand records what arXiv: cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2403.13441v2 Announce Type: replace Abstract: In this paper we investigate formal verification problems for Neural Network computations. Of central importance will be various robustness and minimization problems such as: Given symbolic specifications of allowed inputs and outputs in form of Linear Programming instances, one question is whether there do exist valid inputs such that the network computes a valid output? And does this property hold for all valid inputs? Do two given networks compute the same function? Is there a smaller network computing the same function? The complexity of
Read the official announcement
Opens arxiv.org
More from arXiv: cs.AI preprints
- Auditable Human-Agent Teams for Autonomous X-ray Experimentation11 November 2026
- EarthRecord: From Pixels to Land Change with Multimodal Agents over Four Decades of Satellite Imagery11 November 2026
- Train on prefill, monitor at decode: Using sparse autoencoders for topic monitoring11 November 2026
- LandslideFM: A Foundation Model Towards Unified Landslide Perception and Understanding11 November 2026
- Tracing Psychometric Inference in Large Language Models11 November 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.