arXiv AI By Madhulatha Mandarapu, Sandeep Kunkunuru

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

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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.

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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