Vol. 1 No. 4 (2026)

Articles

Partitioned Message Exchange for Parameter-Efficient Latent Representation Learning

Published 2026-09-15

  • Noah M. James
    • ,
  • Samuel Davis
    • ,
  • Logan C. James

Noah M. James
Department of Computer Science, School of Engineering and Applied Science, Yale University, New Haven, Connecticut, USA

Samuel Davis
Department of Computer Science, College of Engineering, University of California, Davis, Davis, California, USA

Logan C. James
Department of Computer Science, College of Engineering, University of California, Davis, Davis, California, USA

Artificial Intelligence and Intelligent Systems Research, Volume 1, Issue 4, 2026 cover

Abstract

The rapid scaling of deep learning architectures has led to unprecedented achievements across diverse computational domains, yet it concurrently introduces prohibitive computational and memory costs during model training and adaptation. To address these critical bottlenecks, this paper proposes a novel framework centered on partitioned message exchange for parameter-efficient latent representation learning. By structurally decomposing the latent space into orthogonal partitions and facilitating controlled, inter-partition communication through a highly restricted message exchange protocol, the proposed methodology achieves expressive representation learning while drastically minimizing the number of trainable parameters. We introduce a localized adaptation mechanism that applies low-rank updates exclusively to task-specific partitions, while a lightweight routing network manages the flow of representations between partitions. Extensive theoretical analysis demonstrates that partitioned message exchange maintains the theoretical expressiveness of fully connected adaptation modules while strictly bounding memory footprint. Comprehensive empirical evaluations across multiple complex domains, including natural language processing and computer vision benchmarks, validate the superiority of the proposed framework. Our model matches or exceeds the performance of traditional full-parameter fine-tuning and contemporary parameter-efficient methods, requiring only a fraction of the trainable parameters. The results underscore the potential of structured latent communication protocols in facilitating sustainable, scalable, and highly adaptable artificial intelligence systems in resource-constrained environments.

Keywords

Latent Representation, Parameter Efficiency, Message Exchange, Space Partitioning

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Author Biography

Noah M. James

Editorial correspondence on behalf of the authors

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