HYBRA MIRAGE Epistemic Traps for Autonomous AI Agents in the Space of Plausible Interpretations, Where the Very Existence of a Single Truth Cannot Be Proven

Abstract

Modern information protection methods are primarily focused on increasing computational complexity: it is assumed that a task becomes secure if finding the true message requires too many resources. However, virtually all existing models --- from classical cryptanalysis to autonomous AI agents and retrospective analysis systems (Harvest \& Analyze) --- rely on one common assumption: there exists a verification signal that allows distinguishing the true interpretation from the set of false ones. In this work, we present the \textbf{HYBRA MIRAGE} storage architecture, which is based on a different problem formulation. Instead of increasing computational complexity, we propose to eliminate the very criterion of truth upon which directed search is based. The system constructs a space of plausible interpretations and physically excludes the possibility of repeated access to the used reference space~$V$: each of its vectors is applied exactly once and then destroyed on both sides. As a result, the function $\mathsf{Assemble}(C,K,p)$ remains deterministic and total, and any valid access parameter produces a formally correct result. Even with full access to the reference space $V$ and the PIN code, an autonomous analytical agent does not obtain a mechanism to confirm that the found interpretation corresponds to the original message: each vector from $V$ was used exactly once and physically destroyed. The $\mathsf{Assemble}$ algorithm is a trade secret and is not available to the analyst. Consequently, knowledge of $V$ without knowledge of the algorithm allows generating $10^{35}+$ equivalent interpretations, but does not allow singling out the single true one among them. The proposed approach does not make brute force computationally impossible; it makes the claim that the found interpretation is precisely the one embedded by the sender unprovable. Increasing computational resources, applying more sophisticated models, or massive enumeration can produce more candidates, but do not create a procedure that allows mathematically justifying the choice of a single true interpretation. For autonomous AI agents, this leads to the disappearance of the verification signal necessary for directed search. The loss function surface degenerates into a plane with zero gradient: no iterative optimization algorithm can converge to the true parameter faster than random guessing. HYBRA MIRAGE does not compete with classical cryptographic algorithms and does not replace them. The architecture serves as an environment model for analyzing the behavior of autonomous agents under conditions of the absence of a reliable verification signal and can be used as an infrastructure layer on top of existing storage methods. The architecture does not eliminate the agent's ability to generate candidates; it eliminates the possibility of using the generation result as proof of achieving truth. The analyst finds themselves trapped in a state of epistemic equilibrium, where truth and hallucination are architecturally indistinguishable from each other.