Neuton is a framework that automatically converts neuron‑level abstract interpreter specifications into tensor‑level executables, enabling the creation of new DNN certifiers without manual tensor programming. It introduces a stack‑based intermediate representation with shape analysis, domain‑specific rewrite rules for efficient tensor operations, and a novel sparse tensor format (gBCSR) tailored to certifier sparsity. The authors demonstrate that Neuton preserves neuron‑level semantics and evaluate it on eight certifiers across 32 DNN benchmarks, compiling four existing certifiers and producing executable implementations for four others, revealing new precision‑runtime trade‑offs such as SkipPoly matching DeepPoly’s precision while often running faster.
By Avaljot Singh, Yasmin Chandini Sarita, Aditya Mishra, Gagandeep Singh, Charith Mendis
The paper introduces Open-1B, a language model trained under a new fully auditable regime that ensures every training operation is reproducible on heterogeneous commodity hardware with bitwise certainty. By enforcing a fixed order on sources of nondeterminism—GPU reductions, data batch ordering, and inter/intra-node communication—the authors enable auditors to replay and verify individual training steps on a single machine. The release includes the full pretraining dataset, all intermediate checkpoints, the training codebase, and an audit harness for step-by-step verification.
By John Donaghy, Brian Wilcox, O\u{g}uzhan Ersoy, Shikhar Rastogi, Adam St Arnaud, Alexey Titov, Jordan Greenberg, Ben Fielding, Harry Grieve
arXiv:2608. 12004v1 Announce Type: cross Abstract: In modern AI frameworks, GPU kernels are key to overall system performance.
By Jinjun Huang, Zhongzhen Wen, Tongtong Xu, Meng Yan, Xin Xia, Zhongxin Liu
arXiv:2608. 19889v1 Announce Type: new Abstract: The entire ecosystem of open-source language models effectively relies on a single platform.
By Jacob Nielsen, Danial Namazifard, Lukas Galke Poech, Peter Schneider-Kamp
arXiv:2606. 15994v1 Announce Type: new Abstract: Translating deep learning models from PyTorch's flexible, object-oriented design to JAX's functional, stateless setup is usually a manual and error-prone task.
By Qiyue Liang, Steven Ingram, George Vanica, Andi Gavrilescu, Newfel Harrat, Hassan Sipra, Sethuraman Sankaran
arXiv:2603. 15510v2 Announce Type: replace Abstract: The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification.
By Ido Pinto, Yizhak Yisrael Elboher, Haoze Wu, Nina Narodytska, Guy Katz
SCOPE compiles natural‑language policies into neurosymbolic predicate programs that combine small neural models, deterministic extractors, and logical composition. The system is jointly optimized with adversarially generated counterexamples, achieving higher accuracy than prompt‑optimization, task‑decomposition, and workflow‑generation baselines on real‑world policy benchmarks. Programs trained with the weakest executor transfer to other models, enabling deployment on inexpensive models with stable, interpretable predictions.
By Arnon Mazza, Elad Levi
The paper introduces Rondo, a stateful testing framework for deep‑learning compilers that models each test as a model plus an invocation sequence. Rondo mutates sequences using a Markov chain Monte Carlo sampler guided by compilation behavior, and employs a three‑way differential oracle to detect bugs that arise only when compiler state is retained. Evaluated on four major frameworks and 24 model architectures, Rondo uncovered 117 bugs—35 of which were stateful—leading to 39 bug reports, 35 confirmed by developers, and 18 fixes, while achieving high coverage and precision.
By Jiatao Ge, Ruixiang Qian, Yibo Hu, Yanzhou Mu, Yinglong Zou, Jiawei Liu, Zhenyu Chen, Chunrong Fang
arXiv:2510. 16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces).
By Jianzhu Yao, Hongxu Su, Taobo Liao, Zerui Cheng, Huan Zhang, Xuechao Wang, Pramod Viswanath
arXiv:2608. 00029v1 Announce Type: cross Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware.
By Adwaid Suresh, Aparna A, Harshini V M, Jona Delcy C A, Killi Uma Maheswara Rao, Ram Charan Golla, Surendra Vendra
The paper proposes a zero‑knowledge verification framework for frontier AI training, combining pre‑committed specifications, network observations, and live Merkle commitments to create a succinct proof layer via a zero‑knowledge virtual machine (zkVM). It outlines how proofs can verify BF16/FP32 semantics step‑by‑step, enforce policy‑relevant claims, and provide a governance‑auditable training record, while also discussing sampling guarantees, weight smuggling risks, and the relative robustness of global training budgets versus per‑run FLOP caps. The authors compare zkVMs, custom circuits, and interactive audits, and identify remaining research and engineering challenges for external contributors.
By Pierre Peign\'e, Ky Nguyen, Paul Wang
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