Achieving Active Inference in Embedded Systems: Bridging the Gap between Symbolic Reasoning and Neural Embeddings

This paper introduces the HoloMind AGI framework, a novel hybrid architecture that implements Active Inference on resource-constrained embedded systems. By utilizing a multi-layered memory system (STM, MTM, LTM) and C++ optimization, the system achieves real-time uncertainty minimization and adaptive learning without the need for high-end GPU clusters

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-17
DOI
https://doi.org/10.5281/zenodo.17960388
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Achieving Active Inference in Embedded Systems: Bridging the Gap between Symbolic Reasoning and Neural Embeddings

Zenodo (CERN European Organization for Nuclear Research)
Parallel Computing and Optimization Techniques
article

Achieving Active Inference in Embedded Systems: Bridging the Gap between Symbolic Reasoning and Neural Embeddings

article en

Abstract

This paper introduces the HoloMind AGI framework, a novel hybrid architecture that implements Active Inference on resource-constrained embedded systems. By utilizing a multi-layered memory system (STM, MTM, LTM) and C++ optimization, the system achieves real-time uncertainty minimization and adaptive learning without the need for high-end GPU clusters

Zenodo (CERN European Organization for Nuclear Research)
Decent work and economic growth
Openalex Percentile: Top 35%
Parallel Computing and Optimization Techniques
DOIOpen Access PDFOpenAlex
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Achieving Active Inference in Embedded Systems: Bridging the Gap between Symbolic Reasoning and Neural Embeddings · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS