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System evaluation demonstrates deterministic state verification for autonomous agents, highlighting quantifiable staleness and recovery latency metrics.
Autonomous LLM agents acting on external systems routinely operate on stale or hallucinated beliefs about environment state, and dominant agent architectures for example "ReAct" in particular — provide no independent mechanism to detect this before an action is executed by the LLM agent hence, failures surface only as opaque downstream corruption, if at all. We present the Zero-Trust Synchronization Hub, a deterministic verification layer inspired by hardware verification methodology (UVM) that mediates every agent to environment interaction through three sequential gates which are, schema/whitelist validation, belief-versus-ground-truth consistency , and safety-invariant enforcement which are followed by an atomic time-of-check-to-time-of-use (TOCTOU) handshake immediately before the commit. The Hub is architecturally restricted to two only outputs, MATCH or REJECT, and is forbidden from offering corrective or suggestive or advisory input to the agent; all recovery is agent-driven. Because the Hub produces a discrete REJECT event, we define two time-decomposed reliability metrics which are staleness (Z) and recovery latency (R) they are meaningful, specifically because when such an event exists; under a naive ReAct-style baseline, no equivalent event exists, so these metrics are not merely worse but they are undefined, with no independent verification step, never produces the event that would anchor these metrics at all.
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Satwik Vuppari (2026) studied this question.
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