arXiv AI
Oct 23

Every Alarm Has a Cause: An Open Generator of Telecom Alarm Storms with Exact Root Causes, Bound to NORIA-O, RFC 8632 and TMF642

The paper introduces an open generator for telecom alarm storms that produces exact root causes and propagation paths for every alarm on real backbone topologies, with each emitted field tied to standardized terms from NORIA‑O, RFC 8632, RFC 8345, or TMF642. Unlike previous benchmarks, the generator does not rely on confidential operator logs and includes fixed configurations, splits, a scorer, and nine baselines. A simple rule—ranking the device that raised the earliest critical alarm—outperforms more complex methods on both synthetic and real operator data, highlighting a key property of alarm streams.

By Madhulatha Mandarapu, Sandeep Kunkunuru
arXiv Machine Learning
Sep 17

Capability Emergence Can Be Forecast: Per-Seed, In Advance, With Calibrated Intervals, Certified False Alarms, and a Blind Pre-Registered Gate

The paper demonstrates that emergent capabilities in machine learning models can be forecasted with lead time, calibrated uncertainty, and controlled false‑alarm rates. Using per‑seed analysis on transformers, the authors show that the formation time of a previous‑token head predicts the emergence of an induction head with Spearman ρ = 0.977 and a median lead of 975 training steps. Conformal intervals, blind pre‑registered tests, and a multiplicative rule relating anchor and event times further validate the predictive framework across multiple model families and configurations.

By Gunner Levi Howe
arXiv Machine Learning
Sep 24

Does Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really Measuring

The paper investigates whether graph structure improves microservice root‑cause analysis by conducting a controlled study on the RCAEval benchmark. Using identical features, optimizers, and evaluation protocols across three model variants, the authors find no consistent advantage for graph‑based models over flat models, with a negligible Avg@5 difference (0.003, p=0.844). They identify two benchmark properties—limited fault injection and a non‑uniform telemetry schema—that bias results, and propose a new model, PSC‑GRCA, which achieves higher Avg@5 mainly through a system prior rather than graph information.

By Imad Bulji\'c