ProCerDCL is a query‑efficient framework for black‑box probabilistic verification of deep learning compiler optimizations. It uses a single sequential test to decide whether the violation probability of a binary property oracle is below a threshold, above a higher threshold plus a margin, or inconclusive, with bounded error probabilities. Applied to TorchInductor and TensorRT, it achieves higher sampling efficiency and provides diagnostic verdicts on model semantic consistency across 24 tasks.
By Zexin Li, Weibo Zhang, Linyi Li, Arvind Easwaran, Simin Chen
arXiv:2512. 02080v3 Announce Type: replace-cross Abstract: The integration of Formal Verification tools with Large Language Models (LLMs) offers a path to scale software verification beyond manual workflows.
By Pierre Dantas, Lucas Cordeiro, Youcheng Sun, Waldir Junior
arXiv:2608. 01575v1 Announce Type: new Abstract: Whether large language models perform genuine algorithmic reasoning or mere pattern completion is hard to test, because most benchmarks lack a ground truth for correct inductive inference.
By Hector Zenil, Luan Ozelim
Whether large language models perform genuine algorithmic reasoning or mere pattern completion is hard to test, because most benchmarks lack a ground truth for correct inductive inference. We introduce F-ICL, an in-context-learning benchmark that supplies one exactly.
arXiv:2607. 29431v1 Announce Type: new Abstract: Large language models increasingly generate optimization models from natural language, but existing evaluation often reduces a generated model and its ground truth to a single equivalent/not-equivalent verdict or an execution-success rate--labels that are neither independently checkable nor faithful to the multiple distinct senses in which two formulations can agree.
By Penglin Zhu, Jungang Xu
arXiv:2607. 04562v1 Announce Type: new Abstract: Large language models (LLMs) generate fluent outputs that can be wrong.
By MY Pitsane, Hope Mogale