Unverified50% confidenceBenchmarkExact time
In 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.
1
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50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
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UnverifiedIn 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.75% similarUnverifiedWhen 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.74% similarUnverifiedIn sample testing, the interpretable model achieved 100% test accuracy, with the strongest driving factor being age (importance 0.57), followed by monthly_spend.65% similarUnverifiedIn 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.65% similarUnverifiedOn a test set of 100 tickets, SupportOps-AI had an escalation error rate of 18%, a hallucination rate of 6%, an infinite-loop rate of 3%, and a complete failure/unresolved rate of 4%.62% similar
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