让AI不仅猜答案，还能说明理由并自检矛盾。
Beyond Prediction -- Structuring Epistemic Integrity in Artificial Reasoning Systems
- 用符号推理+知识图谱+区块链构建可审计的推理系统
- 支持命题承诺与矛盾检测，确保逻辑一致
- 适合需要可信推理的医疗、法律等高风险场景
本文提出一种面向严格认知约束的人工智能系统框架，突破传统概率语言预测，支持结构化推理、命题承诺与矛盾检测。通过形式化信念表示、元认知过程与规范验证，整合符号推理、知识图谱与基于区块链的论证溯源，实现可保真、可审计的理性认知代理，保障推理过程的透明性与一致性。
原文摘要 · Abstract (English)
This paper develops a comprehensive framework for artificial intelligence systems that operate under strict epistemic constraints, moving beyond stochastic language prediction to support structured reasoning, propositional commitment, and contradiction detection. It formalises belief representation, metacognitive processes, and normative verification, integrating symbolic inference, knowledge graphs, and blockchain-based justification to ensure truth-preserving, auditably rational epistemic agents.
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