Lineage-Calibrated Evidence Aggregation for Sybil-Resistant Skill Evolution

Persistent skills allow large language model agents to reuse operational knowledge across tasks and sessions. Erroneous updates can therefore affect behavior far beyond the trajectories that produced them. We identify nominal support inflation as a risk in trajectory-driven skill evolution: trajectories derived from the same upstream observation can be counted as independent evidence, allowing a single source family to exert disproportionate influence on skill admission, reinforcement, and retention. Textual similarity cannot reliably detect this dependence because semantic transformations can obscure shared evidential origins. We propose Lineage-Calibrated Admission with Root Leave-One-Out Validation (LCA-RLOO), an ancestry-aware framework for calibrating persistent skill updates. Given verifier judgments, a system-recorded execution-lineage graph, and an independent sandbox gate set, LCA-RLOO estimates effective evidence based on shared-root overlap, discounts correlated descendants while preserving agreement across independent roots, and evaluates whether an update remains supported after removing each root and all its descendants. Independent canary tasks compare candidate skills with their previous versions, supporting auditable admission, deferral, rejection, and rollback decisions. We also introduce LineageSkillBench, a controlled evaluation protocol that independently varies ancestry sharing and surface similarity and measures admission quality, downstream harm, transfer, clean utility, provenance degradation, and computational cost across evolution rounds. The framework and benchmark isolate how evidence dependence propagates into persistent agent behavior without treating source identity as a measure of credibility.