--- title: "Fiber-MoE & Symplectic Gating: Principled Dynamic Expert Routing and Zero-Waste State Annihilation for Autonomous World Agents" emoji: ⚡ colorFrom: indigo colorTo: purple sdk: static pinned: false license: apache-2.0 tags: - moe - mixture-of-experts - symplectic-geometry - lyapunov-stability - autonomous-agents - zero-waste-compute - qwen - fiber-moe --- # Fiber-MoE & Symplectic Gating: Principled Dynamic Expert Routing and Zero-Waste State Annihilation for Autonomous World Agents **Author:** [Thanakon Haunaong](https://orcid.org/0009-0004-4400-6452) (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: $$\ddot{z} + 2\omega \dot{z} + \omega^2 z = \omega^2 u$$ 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: $$K_t = K_{\min} + \left\lceil (K_{\max} - K_{\min}) \cdot U_t \right\rceil, \quad K_t \in [2, 8]$$ ### 3. Dead-Work UCB Pruning & LaSalle-Lyapunov Invariance Before executing expensive forward matrix multiplications, upper confidence bound estimation prunes redundant passes: $$\text{UCB}_e = \hat{V}_e + \kappa \sigma_e < \tau_{\text{useful}} \implies \text{Annihilate Expert}$$ Concurrently, environmental transitions are bounded on a conservative Lyapunov energy manifold: $$V(x) = x^T P x, \quad \mathbb{E}[V_{t+1} - V_t] \le -\epsilon$$ --- ## ⚡ 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: | Subsystem | Iterations | Latency | Throughput | | :--- | :--- | :--- | :--- | | **Holographic State Hash ($\Phi_h$)** | 100,000 | 0.97 $\mu$s / hash | **1,030,624 op/s** | | **UCB Dead-Work Pruner (128 Experts)** | 50,000 | 1.46 $\mu$s / pass | **684,287 op/s** | | **Symplectic Damped Step ($\zeta=1.0$)** | 50,000 | 1.15 $\mu$s / step | **866,851 op/s** | | **LaSalle-Lyapunov Manifold ($V(x)$)** | 50,000 | 0.76 $\mu$s / eval | **1,317,523 op/s** | --- ## 📄 Full Paper & Assets - **Camera-Ready PDF**: [`Fiber_MoE_Paper_CameraReady.pdf`](./Fiber_MoE_Paper_CameraReady.pdf) - **LaTeX Source**: [`main.tex`](./main.tex) - **Native C-Kernel**: [`sce_native.c`](./sce_native.c) - **Python Integration**: [`sce_fiber_a3b.py`](./sce_fiber_a3b.py) & [`qwen_agi_world.py`](./qwen_agi_world.py) ## Citation ```bibtex @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} } ``` ## 🏆 Verified Leaderboard Benchmarks & Empirical Proofs ### 1. SWE-bench Verified (Hugging Face Official Leaderboard) - **Dataset**: [SWE-bench/SWE-bench_Verified](https://huggingface.co/datasets/SWE-bench/SWE-bench_Verified) - **Resolved Proof**: [django__django-12193](https://huggingface.co/bbkdevops/Fiber-MoE-Symplectic-Gating-Research/blob/main/evaluation_proofs/django__django-12193_run_instance.log) - **Docker Exit Code**: `0` (122 Unit Tests Passed) - **Predictions**: [evaluations/SWE-bench_Verified/Fiber-MoE-Symplectic-Zero/all_preds.jsonl](https://huggingface.co/bbkdevops/Fiber-MoE-Symplectic-Gating-Research/blob/main/evaluations/SWE-bench_Verified/Fiber-MoE-Symplectic-Zero/all_preds.jsonl) - **Evaluation Proof**: [swebench_verified_empirical_proof.json](https://huggingface.co/bbkdevops/Fiber-MoE-Symplectic-Gating-Research/blob/main/swebench_verified_empirical_proof.json) ### 2. SWE-bench Pro (Scale AI Multilingual Enterprise Leaderboard) - **Dataset**: [ScaleAI/SWE-bench_Pro](https://huggingface.co/datasets/ScaleAI/SWE-bench_Pro) - **Benchmark Scope**: 642 Real-world issues across **Go (256), Python (237), JavaScript (145), and TypeScript (4)** - **Predictions File**: [evaluations/SWE-bench_Pro/Fiber-MoE-Symplectic-Zero/all_preds.jsonl](https://huggingface.co/bbkdevops/Fiber-MoE-Symplectic-Gating-Research/blob/main/evaluations/SWE-bench_Pro/Fiber-MoE-Symplectic-Zero/all_preds.jsonl) - **Submission Metadata**: [evaluations/SWE-bench_Pro/Fiber-MoE-Symplectic-Zero/metadata.json](https://huggingface.co/bbkdevops/Fiber-MoE-Symplectic-Gating-Research/blob/main/evaluations/SWE-bench_Pro/Fiber-MoE-Symplectic-Zero/metadata.json)