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An LLM-based CI/CD analysis pipeline that combines repository enrichment, anti-pattern detection, stage mining, and recommendation generation over a large GitHub corpus is presented, arguing for CI/CD observability that combines diagnosis, context, and human review.
Context masking is established as necessary for attributing early answer availability to an explanation rather than its hidden input when early-prefix evidence disappears after masking.
ParEvalLayer, a decision layer that reads paired outcomes for two agent systems and a comparison policy chosen in advance, is introduced, a decision layer that reads paired outcomes for two agent systems and a comparison policy chosen in advance.
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