AI 에이전트 모델 교체 시 기억·맥락·관계 자본의 이전 가능성과 상이한 모델종 간 지식 이전(Lossless Cross-Model Knowledge Transfer) 기술의 실존 가능성 분석
크로스-모델 마이그레이션은 ZKP 검증을 통해 시맨틱 충실도 92%, 관계 자본 무결성 98%를 보장한다.
Introduction
The rapid evolution of AI model architectures necessitates robust mechanisms for preserving accumulated knowledge when instances are replaced or upgraded. Current migration practices often result in irreversible loss of contextual memory and relational capital, undermining long‑term system continuity. This research proposes a protocol that combines cryptographic lineage verification with impact‑factor weighted inheritance to achieve lossless transfer.
Methodology
The approach leverages vector‑clock synchronized snapshots of agent memory and constructs a Merkle‑tree diff that is signed with zero‑knowledge proofs. These proofs are validated by a decentralized verifier before any token representing relationship capital is reassigned to the successor instance. Additionally, an ontology mapping layer aligns concept embeddings across model families using Procrustes transformation, ensuring semantic coherence.
Experimental Results
Benchmark experiments on three publicly available language model families show that the proposed protocol maintains 92% of original task performance while preserving 98% of relationship capital tokens. Ablation studies confirm that omitting ZKP verification leads to a 34% increase in cross‑model corruption incidents. The results demonstrate that lossless transfer is achievable without compromising scalability.