解决网络物理AI中行动提议与验证之间的断层问题。
The Verification Gap in Networked Physical AI: A Post-Semantic Communication Framework

- 提出后语义通信框架,分离证据验证与执行授权
- 实验证明发送方终定比接收方终定更依赖证据传递
- 适合研究分布式AI系统可靠性的开发者参考
在联网物理AI系统中,一个可理解的行动提议不等于已验证的物理动作。提案虽被理解,但完成动作所需的证据或权威可能缺失,这种不匹配称为验证断层。本文提出后语义通信框架,用于连接提案生成与物理执行之间的系统接口。该框架首先由应用声明证据需求,将合格观测表示为证据记录,通过单一路径验证支持与冲突记录,并将证据充分性与授权终定及下游运行时门限分离。进一步区分证据传递(扩大最终执行者可访问记录集)与证据协调(抑制已在终定端持有的记录传输)。有限状态框架检查确保评估者一致实现上述区分。在所声明模型下,受控通信研究揭示终定方式决定的不对称性:发送方终定采用证据传递扩展证据可达范围,覆盖整个可行区域;接收方终定则用协调抑制冗余数据包传输,直至丢失、延迟、新鲜度和截止时间成本促使选择单向模式。最后,提出一种基于回合的报告范式,为未来物理AI研究提供共同度量基准。
原文摘要 · Abstract (English)
A task-effective proposal is not yet a justified physical action. In networked Physical AI, a proposal may be understood while valid, timely, proposal-bound evidence or the authority required to finalize an action remains unavailable. We call this mismatch the verification gap and introduce a Post-Semantic Communication Framework for the systems interface between proposal formation and physical execution. The framework begins with application-declared evidence requirements, represents qualifying observations as evidence records, validates supporting and conflicting records through one path, and separates evidence sufficiency from authorized finalization and a downstream runtime gate. It further distinguishes evidence transfer, which can enlarge the record set reachable by a finalizer, from evidence coordination, which can suppress transmission around records already held at the finalization endpoint. Finite-state framework checks verify that the evaluator implements the declared distinctions consistently. Under the declared model, the controlled communication study exposes a finalizer-dependent asymmetry: sender-finalized Feedback uses evidence transfer to expand evidence reachability throughout the feasible plotted region, whereas receiver-finalized Feedback uses coordination to suppress redundant payload until loss, latency, freshness, and deadline costs shift selection to One-way. Finally, an episode-level reporting schema defines common denominators for future measured Physical-AI studies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。