arXiv Machine Learning August 28, 2026 By Md Afif Al Mamun, Gias Uddin, Lan Xia, Longyu Zhang

Stack Trace-Based Crash Deduplication with Transformer Adaptation

Read the original on arXiv Machine Learning →

Stack Trace-Based Crash Deduplication with Transformer Adaptation introduces dedupT, a transformer‑based method that models entire stack traces instead of isolated frames. The approach first fine‑tunes a pretrained language model on stack traces and then trains a fully‑connected network to rank duplicate crashes. Experiments on four public datasets show dedupT improves Mean Reciprocal Rank by over 15% versus the best deep‑learning baseline and up to 10% over traditional methods, while also achieving higher ROC‑AUC for unique crash detection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 28

Beyond Shapley: An Influence-Based Data Auditing Pipeline for LLM Alignment and Evaluation

arXiv:2607. 22766v1 Announce Type: cross Abstract: The alignment of Large Language Models (LLMs) is increasingly bottlenecked by data quality.

By Yunting Song, Matthew Watson, Peter Grabowski, Jun Qin

llms reinforcement-learning benchmarks safety data-engineering

arXiv AI

Aug 28

Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences

The paper proposes a new framework for automated fault diagnostics in modern vehicles by treating diagnostic trouble codes (DTCs) as a high‑dimensional language. It introduces Transformer‑based models for predictive maintenance, scalable causal discovery methods, and a multi‑agent system that automatically generates Boolean error‑pattern rules. The approach aims to replace costly manual grouping of DTCs with scalable, data‑driven techniques.

By Hugo Math

llms agents safety

arXiv Machine Learning

4d ago

Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition

arXiv:2609.36686v1 Announce Type: new Abstract: Identifying the root cause of an anomaly among hundreds of sensors is critical for preventing safety incidents and costly downtime in complex monitored...

By Hada Melino Muhammad, Luan Pham, Laure Barri\`ere, Sachin Shetty, Leonardo Pulga, Flora D. Salim

llms rag benchmarks safety

arXiv Machine Learning

2d ago

From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learning

arXiv:2609.38402v1 Announce Type: cross Abstract: We introduce C2C (From Codebase to Culprit), a framework for precise bug localization that progressively reduces the debugging search space across mu...

By Ankur Garg, Corey Yang-Smith, Rishav Rishav, Ahmad Abdellatif, Samira Ebrahimi Kahou

rag reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jul 15

Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis

arXiv:2607. 12868v1 Announce Type: cross Abstract: Deep learning systems often fail due to subtle implementation faults that alter training behavior.

By Sigma Jahan

benchmarks

arXiv Machine Learning

Aug 4

TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference

arXiv:2608. 01975v1 Announce Type: cross Abstract: Large language model (LLM) inference has evolved from an offline workload into a continuously operated software service, yet root-cause analysis remains difficult because a single request spans the inference engine, Python/C++ backend, host CUDA APIs, GPU kernels, and distributed communication.

By Ruilin Xu, Junyi Li, Pengfei Chen, Zongxuan Xie

llms nlp multimodal

One email a morning, machine-written

Related stories

Beyond Shapley: An Influence-Based Data Auditing Pipeline for LLM Alignment and Evaluation

Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences

Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition

From Codebase to Culprit (C2C): Reducing the Search Space for Bugs with Semantic Retrieval and Hierarchical Reinforcement Learning

Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis

TELLER: Non-intrusive Cross-Layer Root-Cause Analysis for LLM Inference