bbkdevops / Fiber-MoE-Symplectic-Gating-Research

Fiber-MoE & Symplectic Gating: Principled Dynamic Expert Routing and Zero-Waste State Annihilation for Autonomous World Agents

Author: Thanakon Haunaong (ORCID: 0009-0004-4400-6452 )
Organization: Autonomous Systems Research Laboratory
Affiliation: Independent Researcher / AI Systems Lab

📌 Abstract

Current Mixture-of-Experts (MoE) architectures and autoregressive world models suffer from three structural pathologies:

1. Router Thrashing : Limit-cycle oscillation across heterogeneous expert domains on adjacent sequence tokens.
2. Static Over-Allocation : Inflexible compute allocation ($K=8$ experts/token) on low-entropy boilerplate tokens.
3. Epistemic World Drift : Compounding simulation errors over extended rollouts lacking conservative dynamical invariants.

In this work, we present SCE-Fiber , an energy-conserving control substrate for massive sparse models (demonstrated on 35B parameter scales with 128 physical experts). By restructuring flat expert topographies into eight semantic domain fibers and applying a critically damped Hamiltonian update ($\zeta = 1.0$) , our framework eliminates oscillatory domain switching while reducing active parameters via dynamic Upper Confidence Bound (UCB) dead-work pruning .

Furthermore, we formulate an invariant world manifold that bounds simulated transitions via LaSalle-Lyapunov invariance ($V(x) = x^T P x$) . Backed by a sub-microsecond CPython native kernel ($0.76 - 1.46\ \mu\text{s}$ latency), empirical benchmarks on an NVIDIA GeForce RTX 3090 demonstrate a 37.5% - 75% reduction in active FLOPs while preserving foundational baseline accuracy and providing instant zero-waste state caching.

🔬 Core Mathematical Formulation

1. Critically Damped Router Dynamics ($\zeta = 1.0$)

Router state trajectories follow a second-order critically damped system: $\ddot{z}+2\omega \stackrel{\dot{}}{z}+{\omega }^{2}z={\omega }^{2}u\backslash ddot\{z\}\; +\; 2\backslash omega\; \backslash dot\{z\}\; +\; \backslash omega^2\; z\; =\; \backslash omega^2\; u$ z ¨ + 2 ω z ˙ + ω 2 z = ω 2 u Enforcing critical damping ($\zeta = 1.0$) guarantees that router specialization converges to optimal domain allocations without overshoot or high-frequency thrashing.

2. Two-Stage Fiber-MoE Routing & Dynamic-K

We group $E = 128$ physical experts into $F = 8$ semantic domain fibers (Physics, Spatial, Temporal, Tool, Memory, Agent, Logic, Self-Correction). Routing occurs hierarchically with sequence uncertainty $U_t$ dynamically governing the active budget: $K_t=K_{\min }+\lceil (K_{\max }-K_{\min })\cdot U_t\rceil ,K_t\in [2,8]K\_t\; =\; K\_\{\backslash min\}\; +\; \backslash left\backslash lceil\; (K\_\{\backslash max\}\; -\; K\_\{\backslash min\})\; \backslash cdot\; U\_t\; \backslash right\backslash rceil,\; \backslash quad\; K\_t\; \backslash in\; [2,\; 8]$ K t ​ = K m i n ​ + ⌈ ( K m a x ​ − K m i n ​ ) ⋅ U t ​ ⌉ , K t ​ ∈ [ 2 , 8 ]

3. Dead-Work UCB Pruning & LaSalle-Lyapunov Invariance

Before executing expensive forward matrix multiplications, upper confidence bound estimation prunes redundant passes: UCB e ​ = V ^ e ​ + κ σ e ​ < τ useful ​ ⟹ Annihilate Expert Concurrently, environmental transitions are bounded on a conservative Lyapunov energy manifold: $V\left(x\right)=x^{T}Px,E[V_{t+1}-V_t]\le -ϵV(x)\; =\; x^T\; P\; x,\; \backslash quad\; \backslash mathbb\{E\}[V\_\{t+1\}\; -\; V\_t]\; \backslash le\; -\backslash epsilon$ V ( x ) = x T P x , E [ V t + 1 ​ − V t ​ ] ≤ − ϵ

⚡ Empirical Hardware Benchmarks (NVIDIA RTX 3090)

The entire control manifold is implemented as an optimized C-Kernel ( libsce_native.so ) executed with zero Python GIL overhead:

📄 Full Paper & Assets

• Camera-Ready PDF : Fiber_MoE_Paper_CameraReady.pdf
• LaTeX Source : main.tex
• Native C-Kernel : sce_native.c
• Python Integration : sce_fiber_a3b.py & qwen_agi_world.py

Citation

@article{haunaong2026fibermoe,
  title={Fiber-MoE & Symplectic Gating: Principled Dynamic Expert Routing and Zero-Waste State Annihilation for Autonomous World Agents},
  author={Haunaong, Thanakon},
  journal={Autonomous Systems Research Laboratory},
  year={2026},
  url={https://huggingface.co/papers}
}