An External Governance Framework for AI Roles in Large Language Model Interaction

Abstract:[Objective]Aiming at the structural contradiction between the behavioral uncertainty of AI roles,caused by the probabilistic outputs of large language models(LLMs),and the human demand for role determinacy,this paper proposes the conceptual framework of a“Role Base”——a structured external governance middleware independent of the underlying model.It does not interfere with the internal parameters,inference process,or sampling strategy of LLMs.Instead,it encapsulates role identity,behavioral boundaries,and governance logic to transform probabilistic outputs into verifiable,auditable deterministic behaviors,realizing a paradigm shift from“conversation”to“contract.”[Methods]Following the design science paradigm,this paper constructs the“Role Base(RB)”framework,which encapsulates role identity,behavioral boundaries,and governance logic into portable,auditable standardized specifications.Core mechanisms include:a multi-layer semantic architecture(separation of concerns,on-demand layer assembly);a three-domain model(decoupling definition from deployment);dual-track governance(integrity digest+approval chain,cryptographically auditable);an external constraint execution architecture(compiled prompting+output safety filtering+engineering rate limiting);and an output-text-based governance drift quantification(semantic drift index+rule violation rate).[Results]A complete design artifact of the Role Base is delivered,including the theoretical framework,technical specifications(ten-layer schema,error code system,integrity digest normalization procedure,test vectors,reference implementation)and a governance lifecycle model.An architectural design methodology for producing verifiable behavioral determinacy through external guidance and output validation is presented,theoretically supporting cross-platform portability,cryptographic integrity,traceable accountability,black-box compatibility,and long-term drift monitoring.[Limitations]As a design science paper,the validity of the proposed framework has only been preliminarily assessed through logical argumentation and comparative analysis.All claims regarding determinacy levels,drift thresholds,and interception rates are design goals or theoretical commitments,awaiting prototype implementation and empirical validation.Determinacy is guaranteed only for behaviors within the predefined rule set,with no assurance for open domains.Determinacy is engineering-level statistical consistency,not mathematical certainty.The claimed 100%interception rate for known violation patterns is a theoretical target;actual deployment is constrained by rule base completeness and compiler correctness.[Conclusion]The behavioral determinacy of AI roles should be systematically produced through architectural separation of concerns,compile-time verification,integrity signatures,and runtime external constraints.The Role Base makes role expectations in human–AI interaction explicit as verifiable governance rules,realizing a paradigm shift from“conversation”to“contract.”The design framework provided in this paper lays the foundation for subsequent engineering implementation and empirical evaluation.