Unverified50% confidenceBenchmarkExact time
When labeled data and a stable schema are available, a logistic regression classifier reached 95.1% accuracy with 50 labeled blocks and 99.7% with 8000 blocks on the HDFS anomaly detection task, surpassing the 86% of the zero-shot LLM.
1
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50%
Confidence
Long-term
Relevance
10/13/2026
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UnverifiedIn experiments on the Loghub HDFS_v1 dataset, Decision-1 initially used a simple prompt and reached only 49% accuracy (AUC 0.68), lower than the 71% rule-based baseline.76% similarUnverifiedIn HDFS anomaly detection, Decision-1's raw output probabilities correlate 0.43 with event count and only 0.14 with labels; the model overreacts to lexical cues and log volume, and misjudges missing events as healthy.76% similarUnverifiedIn HDFS anomaly detection, after using structured questioning (computing lifecycle_summary and changing the question to 'is the lifecycle complete'), Decision-1 reached 86% calibrated accuracy on 1200 test blocks (threshold 0.15), with AUC reaching 0.964.74% similarUnverifiedOn the RCAEval RE2-OB microservice root cause localization dataset, Decision-1 correctly identified the root cause service 85 times out of 90 cases, with 94% accuracy, exceeding the 82% of the 'maximum metric deviation' rule-based baseline (McNemar p=0.013).69% similarUnverifiedEvidence-driven pre-improvement validation improved precision from 58.6% to 72.3%, and reduced unsupported objectives per round from 1.93 to 0.50.68% similar
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