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8 results for “tracing”
How to find failures without drowning in tracing data
The article discusses challenges in distributed systems observability—specifically the data volume and cost burden of full-trace collection—and presents sampling strategies (head, tail, dynamic) as pragmatic solutions to make tracing operationally viable.
Sep 5, 2026
Microsoft Links 30+ Rotating Domains to MacSync Stealer Infrastructure
Microsoft Defender Experts attributed over 30 rotating domains to the MacSync Stealer malware infrastructure by correlating endpoint and network behavioral signals across infrastructure changes.
Aug 19, 2026
Is Convergence Inevitable? Tracing Output Homogeneity Back to Base Models
A new arXiv preprint argues that semantic convergence (output homogeneity) in large language models originates in pretraining—not alignment—and is merely revealed or amplified during instruction tuning, challenging prevailing assumptions about where and how to intervene.
Aug 13, 2026
Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators
A new arXiv preprint introduces two adapted statistical estimators to disentangle aleatoric and epistemic uncertainty sources in deep learning predictions using approximate Fisher Information Matrices, aiming to improve model robustness in real-world applications.
Aug 11, 2026
LLMs Can Annotate Attribution Graphs
Researchers propose using LLMs to automate the manual grouping of neural features into supernodes for circuit tracing—a step toward scalable interpretability of language models.
Aug 5, 2026
Synth historian Oli Freke will spend big on a good bicycle
Oli Freke, a musician and journalist, has authored 'Beat Gems', a coffee-table book on drum machine history published by Bjooks, featuring archival photography, historical context, and practical gear guidance.
Jul 25, 2026
Ray tracing massive amounts of animated geometry using tetrahedral cages
A Hacker News thread titled 'Ray tracing massive amounts of animated geometry using tetrahedral cages' contains user comments discussing a technical graphics rendering technique, with no original article, report, or primary source provided.
Published Jul 23, 2026 · Analyzed Jul 28, 2026
From Approximation to Emergence: A Theory of Deep Learning
A new arXiv monograph proposes a unified theoretical framework for deep learning, positioning emergence—not just approximation—as the central organizing principle of modern AI theory.
Published Jul 3, 2026 · Analyzed Jul 6, 2026