构建面向价值互联网的智能风险感知系统，综合多维度动态评估与响应风险。
Agentic, Context-Aware Risk Intelligence in the Internet of Value
- 融合价格、流动性、路由健康等五类引擎构建复合风险预测机制。
- 在Solana上实测27小时压力响应，验证系统可部署性并支持结果可证伪。
- 适合关注去中心化金融风险控制的开发者与系统设计者使用。
价值互联网（IoV）是一个异构且部分可信的网络，其主要风险是复合型的——涵盖路径、情绪、流动性及系统愿意承担的政策承诺，并非单一链的属性。为此，我们提出一种适配该环境的风险原语，由五个引擎组成：对价格、流动性、波动率和路由健康度的预测引擎；基于Bittensor的去中心化验证子网，用于经济化评分预测结果；融合文本、链上流动性和灰色文献的情绪融合引擎；在宪法与角色约束下的代理执行引擎；以及将预测转化为预承诺行动方案的API风险与情景生成引擎（基于蒙特卡洛模拟）。该架构以两项实证成果为锚点：在Solana上进行的27小时策略约束下流动性压力响应实验，以及168小时预测路由校准过程，明确披露了类别不平衡问题。案例研究支持系统可部署性，验证损失分解形式化且可被证伪。
原文摘要 · Abstract (English)
The Internet of Value (IoV) is a heterogeneous, partially-trusted network in which the dominant marginal risk is composite (route, sentiment, liquidity, and the policy a system is willing to commit to) rather than a property of any single chain. We argue that a risk primitive adequate for this regime is a composition of five engines: a prediction engine over price, liquidity, volatility, and route health; a Bittensor verification subnet that decentralises and economically scores prediction outputs; a sentiment-fusion engine over text, on-chain flow, and grey-literature feeds; an agentic engine under constitutional, role-bound action constraints; and an API-risk and scenario engine that converts forecasts into pre-committed action programs in the sense of Monte-Carlo scenario generation. We anchor the architecture in two empirical artefacts: a 27-hour policy-constrained liquidity stress-response experiment on Solana, and a 168-hour prediction-router calibration arc reported with explicit class-imbalance honesty. The case study supports deployability; the validator-loss decomposition is stated formally and is falsifiable.
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