Decentralizing AI Memory: SHIMI, a Semantic Hierarchical Memory Index for Scalable Agent Reasoning

• URL: http://arxiv.org/abs/2504.06135v1
• Date: Tue, 08 Apr 2025 15:31:00 GMT
• Title: Decentralizing AI Memory: SHIMI, a Semantic Hierarchical Memory Index for Scalable Agent Reasoning
• Authors: Tooraj Helmi,
• Abstract summary: We present SHIMI (Semantic Hierarchical Memory Index), a unified architecture that models knowledge as a dynamically structured hierarchy of concepts.<n>SHIMI is designed for decentralized ecosystems, where agents maintain local memory trees and synchronize them asynchronously across networks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) and vector-based search have become foundational tools for memory in AI systems, yet they struggle with abstraction, scalability, and semantic precision - especially in decentralized environments. We present SHIMI (Semantic Hierarchical Memory Index), a unified architecture that models knowledge as a dynamically structured hierarchy of concepts, enabling agents to retrieve information based on meaning rather than surface similarity. SHIMI organizes memory into layered semantic nodes and supports top-down traversal from abstract intent to specific entities, offering more precise and explainable retrieval. Critically, SHIMI is natively designed for decentralized ecosystems, where agents maintain local memory trees and synchronize them asynchronously across networks. We introduce a lightweight sync protocol that leverages Merkle-DAG summaries, Bloom filters, and CRDT-style conflict resolution to enable partial synchronization with minimal overhead. Through benchmark experiments and use cases involving decentralized agent collaboration, we demonstrate SHIMI's advantages in retrieval accuracy, semantic fidelity, and scalability - positioning it as a core infrastructure layer for decentralized cognitive systems.

Related papers

• Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation [69.01029651113386]
  Embodied-RAG is a framework that enhances the model of an embodied agent with a non-parametric memory system.<n>At its core, Embodied-RAG's memory is structured as a semantic forest, storing language descriptions at varying levels of detail.<n>We demonstrate that Embodied-RAG effectively bridges RAG to the robotics domain, successfully handling over 250 explanation and navigation queries.
  arXiv  Detail & Related papers  (2024-09-26T21:44:11Z)
• Federated Contrastive Learning for Personalized Semantic Communication [55.46383524190467]
  We design a federated contrastive learning framework aimed at supporting personalized semantic communication. FedCL enables collaborative training of local semantic encoders across multiple clients and a global semantic decoder owned by the base station. To tackle the semantic imbalance issue arising from heterogeneous datasets across distributed clients, we employ contrastive learning to train a semantic centroid generator.
  arXiv  Detail & Related papers  (2024-06-13T14:45:35Z)
• Spatial Semantic Recurrent Mining for Referring Image Segmentation [63.34997546393106]
  We propose Stextsuperscript2RM to achieve high-quality cross-modality fusion. It follows a working strategy of trilogy: distributing language feature, spatial semantic recurrent coparsing, and parsed-semantic balancing. Our proposed method performs favorably against other state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-05-15T00:17:48Z)
• DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks [64.62314068155997]
  We present textitDecentralized Embedding Framework for Heterogeneous Information Network (DeHIN) in this paper. DeHIN presents a context preserving partition mechanism that innovatively formulates a large HIN as a hypergraph. Our framework then adopts a decentralized strategy to efficiently partition HINs by adopting a tree-like pipeline.
  arXiv  Detail & Related papers  (2022-01-08T04:08:36Z)
• Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images [28.560068780733342]
  A novel context aggregation network (CATNet) is proposed to improve the feature extraction process. The proposed model exploits three lightweight plug-and-play modules, namely dense feature pyramid network (DenseFPN), spatial context pyramid ( SCP), and hierarchical region of interest extractor (HRoIE)
  arXiv  Detail & Related papers  (2021-11-22T08:55:25Z)
• Deep Fusion Clustering Network [38.540761683389135]
  We propose a Deep Fusion Clustering Network (DFCN) for deep clustering. In our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder. Experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods.
  arXiv  Detail & Related papers  (2020-12-15T09:37:59Z)
• Adaptive Linear Span Network for Object Skeleton Detection [56.78705071830965]
  We propose adaptive linear span network (AdaLSN) to automatically configure and integrate scale-aware features for object skeleton detection. AdaLSN substantiates its versatility by achieving significantly higher accuracy and latency trade-off. It also demonstrates general applicability to image-to-mask tasks such as edge detection and road extraction.
  arXiv  Detail & Related papers  (2020-11-08T12:51:14Z)
• Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference [55.35176938713946]
  We develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its multi-stochastic-layer generative network. We propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a downward generative model. The efficacy and scalability of our models are demonstrated on both unsupervised and supervised learning tasks on big corpora.
  arXiv  Detail & Related papers  (2020-06-15T22:22:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.