rank-pooled-multi-run-outputs-cross-run-vote TEC-0166 · proposedRank pooled multi-run outputs by cross-run vote count
Treat each LLM run as a black-box retrieval system casting a binary vote per candidate requirement, sum the votes (optionally weighted by each model family's estimated true-positive/false-positive log-odds), and hand reviewers the resulting
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Treat each LLM run as a black-box retrieval system casting a binary vote per candidate requirement, sum the votes (optionally weighted by each model family's estimated true-positive/false-positive log-odds), and hand reviewers the resulting Filed by the drafting stage from Ensembling LLMs for AI-Augmented Cybersecurity Software Requirements Generation (2609.10316) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Tie-aware average precision and ROC AUC over the 183-candidate judged universe: uniform vote fusion exceeded the best single configuration by 0.142 AP and 0.118 ROC AUC; Naive-Bayes reliability weighting added 0.039 and 0.052, reaching 0.864 and 0.869. Gains stayed positive in at least 92% of out-of-bag bootstrap resamples and in every structured-perturbation sample.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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Sources
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