arXiv Machine Learning

Numbat: Building and Verifying a Self-Contained Machine-Learning Stack

The paper introduces Numbat, a self‑contained machine‑learning stack implemented entirely in Zig with no external runtime dependencies. It covers tensor computation, automatic differentiation, neural‑network modules, mixed precision, multi‑GPU training, data loading, and monitoring, and exposes a stable C ABI with over 1,400 entry points and bindings for six languages. The authors verify the stack against a reference implementation at multiple levels, uncovering ten silent recipe divergences, and demonstrate its practical capability by training a 25.9M‑parameter YOLOv8m detector on COCO 2017, achieving a competitive mAP score and matching single‑GPU performance.

arXiv Machine Learning

1d ago

OPEN-1B: A Fully Auditable Training Run

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

llms

arXiv AI

Aug 13

RealisticTritonBench: A Benchmark for Triton-Kernel Generation in Real-World AI Frameworks

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

llms benchmarks

arXiv AI

Aug 21

Write Once, Run Everywhere: The Axon DSL for Shape-Safe and Framework-Agnostic LLM Architectures

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

llms benchmarks

arXiv AI

Jun 16

Agentic Framework for Deep Learning workload migration via In-Context Learning

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

llms agents multimodal benchmarks safety

arXiv Machine Learning

Jun 24

Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs

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

llms fine-tuning

arXiv AI

Jun 9

TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks

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

llms rag diffusion efficiency safety

arXiv Machine Learning

Aug 4

Nova: An End-to-End MLIR Compiler for Deep Learning

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

efficiency

arXiv AI

Jun 15

HierSVA: A Data Synthesis Pipeline, Dataset, and Benchmark for LLM-Driven Hierarchical Hardware Formal Verification

arXiv:2606. 13706v1 Announce Type: cross Abstract: We present HierSVA, an integrated suite that combines a pipeline, dataset, and benchmark for LLM-driven hierarchical hardware formal verification.

By Maohua Nie, Jiang Zhu, Jingqun Zhang, Zhichen Zeng, Jiayi Wang, Sibo Zhang, Jialin Wang, C. -J. Richard Shi

llms agents benchmarks

arXiv AI

Jul 3

Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation

arXiv:2607. 01590v1 Announce Type: new Abstract: Developing high-performance kernels for Neural Processing Units (NPUs) is a critical industry bottleneck, requiring developers to manually navigate implicit hardware constraints and strict memory hierarchies.

By Junyi Wen, Ruiyan Zhuang, Yongjia Xu, Pengtu Li, Rui Zou, Hongyi Chen, Chingman Wan, Puxu Yang, Wuhui Chen, Yanlin Wang

llms efficiency benchmarks

arXiv AI

Jun 4

AlgoVeri: An Aligned Benchmark for Verified Code Generation on Classical Algorithms

arXiv:2602. 09464v2 Announce Type: replace-cross Abstract: Vericoding refers to the generation of formally verified code from rigorous specifications.

By Haoyu Zhao, Ziran Yang, Jiawei Li, Deyuan He, Zenan Li, Chi Jin, Venugopal V. Veeravalli, Aarti Gupta, Sanjeev Arora

llms benchmarks

arXiv AI

Jun 6

Zero knowledge verification for frontier AI training is possible

arXiv:2606. 05433v1 Announce Type: new Abstract: Frontier AI governance frameworks increasingly use cumulative training compute as the primary criterion for designating high-impact models, but enforcement rests on self-reporting because no technical verification primitive for training exists.

By Pierre Peign\'e, Ky Nguyen, Paul Wang

arXiv Machine Learning

Jun 19

The Correctness Illusion in LLM-Generated GPU Kernels

arXiv:2606. 20128v1 Announce Type: cross Abstract: Benchmarks for LLM-generated GPU kernels (KernelBench, TritonBench, GEAK) score correctness through fixed-shape, small-sample allclose-style checks.

By Dipankar Sarkar

llms benchmarks

Related stories