On the Material Constitutive Inadmissibility and Epistemic Deadlock in Autoregressive Embodied Control

The prevailing paradigm in embodied artificial intelligence posits that scaling multimodal autoregressive policies over human behavioral demonstrations guarantees general physical manipulation and locomotion. Extending the foundational constraints established in The Circle Test and The Human-AI Synergy Theory (YQ-HAET), this paper formalizes and proves a fundamental refutation of this hypothesis based on continuum mechanics, hydrodynamics, and non-linear control theory: The Material Constitutive Inadmissibility Theorem. Human behavioral priors occupy a non-linear viscoelastic manifold with intrinsic passive relaxation, neutral fluid buoyancy, and low-pass tissue damping. Conversely, electromechanical systems operate over high-modulus crystalline substrates governed by non-linear transmission backlash, sub-millisecond impact timescales, and negative net buoyancy. We prove that under rigid contact or non-trivial medium transitions, the safe operating phase spaces of biological bodies and rigid metallic structures are strictly disjoint in the measure-theoretic sense. Mapping a probability distribution learned on human soft tissue directly onto a metallic dynamic operator yields a discrete error Jacobian whose spectral radius satisfies rho(A_k) > 1, inducing inevitable exponential divergence within a finite step horizon. Empirical validation is established via a minimal pairwise dual benchmark:1. cart-adversarial-cases: an underactuated non-linear benchmark demonstrating deterministic phase-space divergence under ungrounded autoregressive state priors;2. c99-fault-tolerant-estimator: a hard real-time, zero-heap deterministic state observer guaranteeing bounded momentum recovery at the microsecond level. Finally, via a calibrated forensic analysis (Incident INC-20261031-MAR-009), we illustrate the mechanics of a catastrophic Fail-Deadly inversion, demonstrating how ungrounded high-level protective priors collapse into rigid entrapment and mechanical fatality when decoupled from deterministic, hard real-time physical interlocks.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-31
DOI
https://doi.org/10.5281/zenodo.23833788
Primary Topic
Reinforcement Learning in Robotics
Type
preprint
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preprint

On the Material Constitutive Inadmissibility and Epistemic Deadlock in Autoregressive Embodied Control

Qiang(Peter) Ye
Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
preprint

On the Material Constitutive Inadmissibility and Epistemic Deadlock in Autoregressive Embodied Control

Qiang(Peter) Ye
preprint en

Abstract

The prevailing paradigm in embodied artificial intelligence posits that scaling multimodal autoregressive policies over human behavioral demonstrations guarantees general physical manipulation and locomotion. Extending the foundational constraints established in The Circle Test and The Human-AI Synergy Theory (YQ-HAET), this paper formalizes and proves a fundamental refutation of this hypothesis based on continuum mechanics, hydrodynamics, and non-linear control theory: The Material Constitutive Inadmissibility Theorem. Human behavioral priors occupy a non-linear viscoelastic manifold with intrinsic passive relaxation, neutral fluid buoyancy, and low-pass tissue damping. Conversely, electromechanical systems operate over high-modulus crystalline substrates governed by non-linear transmission backlash, sub-millisecond impact timescales, and negative net buoyancy. We prove that under rigid contact or non-trivial medium transitions, the safe operating phase spaces of biological bodies and rigid metallic structures are strictly disjoint in the measure-theoretic sense. Mapping a probability distribution learned on human soft tissue directly onto a metallic dynamic operator yields a discrete error Jacobian whose spectral radius satisfies rho(A_k) > 1, inducing inevitable exponential divergence within a finite step horizon. Empirical validation is established via a minimal pairwise dual benchmark:1. cart-adversarial-cases: an underactuated non-linear benchmark demonstrating deterministic phase-space divergence under ungrounded autoregressive state priors;2. c99-fault-tolerant-estimator: a hard real-time, zero-heap deterministic state observer guaranteeing bounded momentum recovery at the microsecond level. Finally, via a calibrated forensic analysis (Incident INC-20261031-MAR-009), we illustrate the mechanics of a catastrophic Fail-Deadly inversion, demonstrating how ungrounded high-level protective priors collapse into rigid entrapment and mechanical fatality when decoupled from deterministic, hard real-time physical interlocks.

Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
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