arXiv:2606.10937v1 Announce Type: cross Abstract: AI compilers aggressively rewrite computation graphs through normalization, lowering, and optimization, making it difficult to track the provenance of tensors and operators across compilation. Reliable provenance is essential for attaching platform-specific postprocessing, debugging compiler behavior, and validating transformations, yet existing solutions are either invasive or ad hoc under non-injective graph rewrites. We present a lightweight, generative approach to provenance tracking based on observational semantics. Instead of propagating
The increasing complexity and opacity of AI compilation processes necessitate advanced methods for debugging and validating AI models before deployment.
Reliable provenance tracking in AI compilers is crucial for ensuring the trustworthiness, explainability, and regulatory compliance of AI systems, especially as they become more integrated into critical applications.
This research introduces a lightweight, generative approach to tracking AI compilation provenance, offering a more robust alternative to current ad hoc or invasive methods.
- · AI developers
- · AI compiler companies
- · Regulatory bodies
- · Debugging tool providers
- · Developers of opaque AI systems
- · Users relying on black-box AI
Improved debugging and validation leads to more reliable AI models deployed in production.
Enhanced explainability and auditability foster greater trust in AI systems across various industries.
Standardized provenance tracking could become a prerequisite for AI model certification and regulatory approval globally.
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Read at arXiv cs.AI