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This paper identifies the "verification gap" in networked Physical AI, where valid proposals lack the necessary evidence or authority for execution. The authors introduce a Post-Semantic Communication Framework that delineates evidence requirements, validates records, and separates evidence sufficiency from finalization authority. Key findings reveal an asymmetry in feedback mechanisms, where sender-finalized feedback enhances evidence reachability, while receiver-finalized feedback focuses on suppressing redundancy until specific operational costs dictate a shift in strategy.
The verification gap in Physical AI reveals a critical asymmetry in how evidence is utilized, challenging conventional approaches to proposal execution.
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.
