H-Model: Dynamic Neural Architectures for Adaptive Processing

TL;DR

This paper introduces H-Model, a neural network architecture with dynamic, input-dependent structure and routing mechanisms, aiming to enable adaptable and interpretable computation rather than outperforming existing models.

Contribution

It proposes a novel architectural framework for neural networks that can learn and adapt their internal structure dynamically based on input data.

Findings

Initial observations show promise for the architecture's adaptability.

The model demonstrates potential for more interpretable neural computation.

Further evaluation requires more computational resources.

Abstract

This article explores the design and experimentation of a neural network architecture capable of dynamically adjusting its internal structure based on the input data. The proposed model introduces a routing mechanism that allows each layer to influence how its outputs are propagated through the network, enabling iterative and adaptive computation. This concept is loosely inspired by the idea of thought processes and dynamic reasoning, where information flow is conditioned not only on the data itself, but also on the internal state of the system. It is important to note that this work does not aim to compete with state-of-the-art language models in terms of performance. Instead, it presents a conceptual prototype-an architectural framework that opens up a new direction for exploring adaptable and potentially more interpretable networks. The goal is not optimization of existing benchmarks but rather the proposal of a system that can learn not only representations, but also the structure of computation itself. Due to practical constraints in computing resources and data, this study remains a preliminary investigation. Nevertheless, initial observations show promise, and the architecture's full potential can only be evaluated in future experiments under more favorable computational conditions.