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This work studies what leaderboard scores estimate and when they justify pairwise superiority conclusions, and a estimand-aware pairwise procedure states the comparison target and measurement source, checks common support, and evaluates the supported difference using a stated uncertainty rule and practical margin.
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.
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.
This work asks whether LLM-generated skills offer a useful low-curation alternative: do they improve performance over the task prompt alone, and whether any part of the skill is useful by ablating different skill components, and finds no reliable improvement from full generated skills over No-Skill prompting.
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