mlsanityData to 7 Oct 2026

White-Box Neural Ensemble for Vehicular Plasticity: Quantifying the Efficiency Cost of Symbolic Auditability in Adaptive NMPC

arXiv 2602.01516 ↗First posted 2 Feb 2026cs.LG cs.AI cs.SYcited by 0

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Abstract (arXiv)

We present a white-box adaptive NMPC architecture that resolves vehicular plasticity (adaptation to varying operating regimes without retraining) by arbitrating among frozen, regime-specific neural specialists using a Modular Sovereignty paradigm. The ensemble dynamics are maintained as a fully traversable symbolic graph in CasADi, enabling maximal runtime auditability. Synchronous simulation validates rapid adaptation (~7.3 ms) and near-ideal tracking fidelity under compound regime shifts (friction, mass, drag) where non-adaptive baselines fail. Empirical benchmarking quantifies the transparency cost: symbolic graph maintenance increases solver latency by 72-102X versus compiled parametric physics models, establishing the efficiency price of strict white-box implementation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 2 Feb 2026.

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References · 11

  1. 2018On the Spectral Bias of Neural Networkscited by 1,321
  2. 2022Neural-Fly Enables Rapid Learning for Agile Flight in Strong Windscited by 80
  3. 2019acados: a modular open-source framework for fast embedded optimal controlcited by 61
  4. 2023Learning for CasADi: Data-driven Models in Numerical Optimizationcited by 11
  5. 2020BayesRace: Learning to race autonomously using prior experiencecited by 6
  6. 2023Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racingcited by 6
  7. 2025Gaussian processes for dynamics learning in model predictive controlcited by 6
  8. 2021Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Controlcited by 2
  9. 2025S2GPT-PINNs: Sparse and Small models for PDEscited by 2
  10. 2025AltNet: Addressing the Plasticity-Stability Dilemma in Reinforcement Learningcited by 2
  11. 2026Position: Certifiable State Integrity Should Be Built from Local Validity, Not Global Scalecited by 1

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