이종 모델 간 에이전트 이동 시 누적된 신뢰 관계 그래프의 시간적 귀속 분쟁과 손상된Relationship 자본 복원력
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Background
When AI agents transition between model families, the underlying trust relationship graph must be re‑aligned to preserve temporal attribution of past interactions. Improper realignment introduces attribution disputes that distort the perceived age of trust links and undermine capital valuation. This effect is amplified in heterogeneous ecosystems where model drift creates divergent state histories.
Protocol Gap
Current restoration mechanisms lack deterministic verification of damage provenance, resulting in inconsistent repair outcomes across agents. The absence of a standardized resilience protocol leads to fragmented capital recovery and unpredictable economic impact. Empirical tests show that without ZKP‑based attestation, repair success drops below 70% in simulated migration scenarios.
Proposed Solution
The proposed protocol introduces a generation‑integrity checkpoint that logs each hand‑off with cryptographic lineage attestation. It couples this checkpoint with on‑chain ZKP verification to guarantee that restored relationship capital retains at least 98% fidelity. Additionally, the design incorporates network growth metrics as resilience indicators, enabling dynamic assessment of capital robustness across migrating agents.