arXiv:2510.16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals little about what actually ran or whether returned outputs faithfully reflect the intended inputs. Users lack recourse against service downgrades (model swaps, quantization, graph rewrites, or discrepancies like altered ad embeddings). Verifying outputs is hard because floating-point(FP) execution on heterogeneous accelerators is inherently nondeterministic. Existing approaches are either impractical
The increasing reliance on cloud-based AI and ML-as-a-Service, combined with the inherent non-determinism of floating-point operations on diverse hardware, makes output verification a critical and timely challenge.
This development addresses a fundamental trust and security issue in deploying AI at scale, impacting the reliability and accountability of AI systems used in services where verification is crucial.
The ability to verify outputs from remote or third-party AI inference will improve transparency and allow users to detect unauthorized model alterations or performance degradation.
- · AI service providers offering verifiable results
- · Developers of formal verification tools
- · Sectors requiring high AI reliability (e.g., finance, healthcare)
- · Black-box AI service providers
- · Users vulnerable to undetected AI model changes
- · Adversaries attempting to subtly alter AI models
Increased user confidence and adoption of ML-as-a-Service due to improved output reliability.
Demand for hardware and software solutions optimized for verifiable floating-point computations will grow.
New regulatory frameworks may emerge, requiring verifiable AI deployments in critical applications, influencing procurement and compliance standards.
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Read at arXiv cs.LG