OSN3P: An Optimized Mixture-of-Experts Routing Framework

Abstract

OSN3P is a sparse mixture-of-experts routing framework built around a deterministic tokenization scheme and a one-shot graph-routing core. Each character is encoded by three attributes: its position in the alphabet, its position inside the word, and a binary capitalization flag. These lexical tokens are pooled into word representations, assembled into a 12-word semantic search window, and then routed through a sparse expert lattice based on Neuron Contact, Next-Neuron, and Next-Neighborhood One-shot Pathfinding networks. The result is a controllable and interpretable architecture that emphasizes explicit routing, low-overhead preprocessing, and modular domain specialization. This paper formalizes the tokenization rule, the routing mechanism, and the N3P expert computation underlying OSN3P.

1. Introduction

Large neural systems often trade interpretability for scale. OSN3P is motivated by a different design goal: preserve routing transparency while keeping the representation pipeline lightweight and easy to inspect. Instead of relying on opaque subword segmentation alone, OSN3P begins with a deterministic lexical decomposition of every character. Those character tuples are converted into compact word embeddings, grouped into fixed-width semantic windows, and then processed by a sparse mixture-of-experts (MoE) router.

The core computational block is a family of N3P experts, where each expert reasons over three local graph relations: neuron contact, next-neuron adjacency, and next-neighborhood context. The router activates only the most relevant experts for a given semantic window, which reduces unnecessary computation and yields a route trace that can be inspected after inference.

This manuscript presents OSN3P as a system paper and reference formulation. The main contributions are:

• a deterministic tokenization rule based on alphabet index, intra-word position, and capitalization;
• a 12-word semantic window representation for keyword extraction and routing;
• a sparse MoE controller over domain-specialized N3P experts for interpretable one-shot inference.

2. Framework Overview

Input text is decomposed at the character level, lifted into semantic nodes, pooled into a 12-word search window, and finally routed to a sparse subset of expert modules. The resulting architecture supports domain-specialized processing without forcing every input through every expert.

3. Deterministic Tokenization

For a word w = (c₁,...,cₘ), OSN3P assigns each character a three-dimensional token:

where α(cᵢ) ∈ {0, 1,..., 26} is the character's place in the alphabet, i is the character position within the word, and κ(cᵢ) ∈ {0, 1} marks whether the character is capitalized. Non-alphabetic characters may be assigned α(cᵢ) = 0.

4. Semantic Windows and Keyword Extraction

After each word has been embedded, OSN3P forms a local semantic search window of length 12. This fixed-width window is intended to capture immediate context without requiring full-sequence dense attention.

Keyword extraction can then operate on both the pooled representation and the lexical evidence retained by the underlying token tuples. A representative 12-word example is the query:

This window highlights semantic anchors such as drone, flyovers, homes, suburbs, regulated, and FAA. These anchors are then used to assemble the graph context passed to the expert router.

5. Sparse Mixture-of-Experts Routing over N3P Experts

5.1 Top-r routing

Let K denote the number of available experts. The routing network produces logits and converts them into a sparse gate by retaining only the top-r entries. This preserves the flexibility of an MoE model while ensuring sparse execution.

5.2 N3P expert computation

Each expert is defined over three local relation types: neuron-contact edges, next-neuron edges, and next-neighborhood edges. Because the expert performs one graph propagation step and returns a pooled route summary, inference remains lightweight and directly attributable to a small set of active paths.

5.3 Expert aggregation

The routed prediction for the semantic window is the weighted sum of expert outputs. This formulation makes the route trace explicit: every output can be decomposed into its active experts, their gates, and the semantic nodes that triggered them.

6. Optimization Objective and Computational Profile

For a supervised target, OSN3P can be trained with a task loss plus a load-balancing regularizer. The regularizer discourages router collapse while still allowing sharp top-r decisions on individual windows.

The computational advantage of OSN3P comes from sparse activation. For each semantic window, routing costs O(Kd) to score experts, but only r ≪ K experts are executed. This is the operational sense in which OSN3P is optimized: most experts remain idle for most windows, and each selected expert performs only a one-shot graph update.

7. Interpretable Routing Example

The query about drone flyovers illustrates the intended behavior of the model. The tokenizer exposes both lexical identity and local structure, preserving capitalization in FAA and word position across the full 12-word window.

8. Discussion and Conclusion

OSN3P is designed as an interpretable routing framework rather than a replacement for every dense language model. Its deterministic tokenizer intentionally favors transparency over maximal expressiveness, and its performance will depend on the quality and specialization of the available experts. That trade-off is a design choice: the framework emphasizes explicit routing logic, modular expert growth, and controllable domain decomposition.

As an arXiv-ready system description, this paper provides the reference formulation for the model: a lexical tokenizer based on alphabet position, word position, and capitalization; a 12-word semantic search stage; and a sparse MoE controller over N3P experts. Future work can build on this foundation by benchmarking the model, expanding expert inventories, and integrating retrieval-backed supervision for grounded responses.