The Epistemic Deadlock of Embodied AI: Why Partial Observation and Autoregressive Priors Inevitably Induce Catastrophic Physical Divergence

Current end-to-end and Vision-Language-Action (VLA) architectures operate under the implicit assumption that scaling discrete multimodal tokens and autoregressive self-attention guarantees physical commonsense and control stability. In this work, we formalize the fundamental limits of this paradigm as an epistemic deadlock arising from the non-commutative coupling between observation truncation and prior-dominated inference. We formulate the underlying dynamics as a constrained nonlinear system wherein continuous physical states on a compact manifold Mᵣₑₐₗ ⊂ RN are projected via discrete quantization and smooth immersion into a lower-dimensional observation subspace Rᵐ (m N): Axiom I (Geometric Unobservability / Blind-Men-Touching-Elephant): The differential kernel (dhₓ) induces an intrinsic nullspace where the data Fisher Information Matrix (FIM) is perpetually rank-deficient (rank(Idata) ≤ m < N). Axiom II (Algebraic Pseudo-Convergence / Understanding-Imitating): Along unobservable directions, mutual information gain vanishes, collapsing maximum a posteriori (MAP) estimation into a singular attractor governed exclusively by corpus empirical priors (∇ₓ ln pLLM). Axiom III (Catastrophic Bifurcation / Physical Rupture): Latent autoregressive decision boundaries break local Lipschitz continuity (L → ∞), causing the Lyapunov derivative of the augmented port-Hamiltonian error manifold to breach dissipative boundaries (V̇ > 0), driving the plant into Thom's cusp catastrophe. To resolve this impasse, we establish the Instrument Paradigm—modeling continuous, multi-scale spatiotemporal tensor fields R(x,t) under pre-action orthogonal chi-square innovation gating—and introduce a deterministic, zero-heap C99 state estimator (Guardian) enforcing Lie-algebraic convex hulls and bounded momentum reacquisition in hard real-time.