gate-skills-sampled-coalition-knockouts-then-mask-after TEC-0129 · proposedGate skills on sampled coalition knockouts, then mask after
At acceptance time, score a candidate bank by the averaged value loss from knocking out randomly sampled skill subsets rather than by aggregate reward alone; at deployment, greedily drop transferred skills whose removal improves retrieval s
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.
At acceptance time, score a candidate bank by the averaged value loss from knocking out randomly sampled skill subsets rather than by aggregate reward alone; at deployment, greedily drop transferred skills whose removal improves retrieval s Filed by the drafting stage from Coalition-Aware Skill Reliability for Self-Evolving Agents (2608.22610) 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: Matched-protocol comparison against MemSkill and six memory-agent baselines: the coalition gate accepted 1 of 12 candidates versus MemSkill's 6, and a K-controlled ablation showed MemSkill's extra additions were -1.38 pp on average; label-free masking improved all six trained banks on HotpotQA-50 LLM-judge (+3.59 pp average on coalition-gated banks, +7.17 pp on baseline banks), and the retrieval-quality signal correlated with label-based skill ranking at rho=+0.76 versus -0.18 for a policy-entropy signal.. 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.
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