opt-routing-framework

A conceptual framework for interpreting and structuring routing in Mixture-of-Experts models using the OPT cognitive pathway model.

OPT Routing Framework

This repository presents a conceptual framework for interpreting and structuring routing mechanisms in modular large language models.

Papers

OPT as a Cognitive Routing Layer for Modular LLM Architectures

PDF | DOI

Toward Path-Constrained Routing in Mixture-of-Experts Models

PDF | DOI

The work is developed in two stages:

Stage 1 — Interpretive Framework

"OPT as a Cognitive Routing Layer for Modular LLM Architectures"

This paper proposes that routing in Mixture-of-Experts (MoE) systems can be interpreted as pathway-based signal propagation, where expert selection approximates the activation of cognitive pathways.

Stage 2 — Toward Operationalization

"Toward Path-Constrained Routing in Mixture-of-Experts Models"

This paper extends the interpretive framework by introducing a pathway-constrained formulation of routing, where routing decisions are defined over structured source–sink pathway units rather than expert indices.

A minimal computational framework is outlined, including:

• Source representation
• Sink projection
• Pathway construction via outer product
• Pathway-based routing and aggregation

Scope

This work is conceptual and structural:

• No training algorithm is proposed
• No empirical validation is provided

The objective is to define a structured perspective on routing that may inform future work on interpretability and modular architectures.

Status

Early-stage research framework. Further work is required for empirical validation.