arXiv AI July 3, 2026 By Parv Agarwal, Asif Ekbal

GPUAlert: A Zero-Instrumentation Process-Boundary Monitor for Diagnosing GPU Training-Job Failures

arXiv:2607. 01409v1 Announce Type: cross Abstract: GPU training jobs fail often, roughly two in five on large production clusters, yet the operator typically learns of a failure only by reconnecting hours later.

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arXiv AI

Jun 9

From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs

arXiv:2605. 09370v3 Announce Type: replace-cross Abstract: Large-scale AI training is now fundamentally a distributed systems problem, and hardware failures have become routine operating conditions rather than rare exceptions.

By Daemyung Kang, Eunjin Hwang, Hanjeong Lee, HyeokJin Kim, Hyunhoi Koo, Jeongkyu Shin, Jeongseok Kang, Jihyun Kang, Joongi Kim, Junbum Lee, Jungseung Yang, Kyujin Cho, Youngsook Song

llms

arXiv Machine Learning

Aug 4

Real-Time Detection and Repair of LLM Agent Failures

arXiv:2608. 02464v1 Announce Type: cross Abstract: LLM agents fail mid-episode -- they loop, cascade tool errors, drift off goal, fabricate results, or silently absorb corrupted content -- and the standard remedy, judging every step with a second LLM, costs more than the agent itself.

By Sunny Dubey

llms agents

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

arXiv AI

Jun 16

Do You Really Need a GPU to Guard Your LLM? CPU-Class Classifiers and Multi-Stage Pipelines for Safety Enforcement at Scale

arXiv:2512. 19011v3 Announce Type: replace-cross Abstract: Safety classifiers that screen LLM inputs for jailbreak attempts have become standard deployment components, yet almost all production systems rely on GPU-based models: fine-tuned transformers and LLM-as-a-judge pipelines.

By Vasudev Majhi, Dhruv Gupta, Advait Singh, Matthew Barker, Dhruv Kumar

llms fine-tuning safety

arXiv Machine Learning

Sep 22

Measuring the Checker: Mutation Analysis for GPU-Kernel Benchmark Oracles

The paper introduces mutation analysis as a metric for evaluating GPU‑kernel benchmark oracles, injecting over ten thousand faults into verified CUDA implementations of 188 KernelBench problems. It shows that the current official checkers miss 16.9% of faults, with precision faults being especially problematic, and demonstrates that optimized test suites can achieve 98% detection with only two inputs per problem. The study also reveals flaws in existing patches and a fuzzing recipe that incorrectly rejects correct kernels 107 times.

By Mingzhe Du, Anh Tuan Luu, Dong Huang, See-Kiong Ng

llms benchmarks

arXiv AI

Aug 13

Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control

arXiv:2608. 12123v1 Announce Type: cross Abstract: LLM-agent services repeatedly execute small deterministic transitions between model and tool calls: route an outcome, update state, and emit the next effect.

By Josef Liyanjun Chen

llms agents

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Related stories

From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs

Real-Time Detection and Repair of LLM Agent Failures

The Correctness Illusion in LLM-Generated GPU Kernels

Do You Really Need a GPU to Guard Your LLM? CPU-Class Classifiers and Multi-Stage Pipelines for Safety Enforcement at Scale

Measuring the Checker: Mutation Analysis for GPU-Kernel Benchmark Oracles

Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control