HoloMind: A Minimal Autonomous Learning System Driven by Curiosity, Confidence, and Self-Termination

HoloMind is a minimal autonomous learning system capable of self-directed learning, curiosity-driven inquiry, and confidence-regulated learning termination. It operates without external reward functions, reinforcement learning, or human feedback. The system architecture includes an embedding-first memory design (STM, MTM, LTM), a curiosity engine, and a reasoning module, demonstrating autonomous learning behavior validated through reproducible test logs.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-20
DOI
https://doi.org/10.5281/zenodo.18002320
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
0.00
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article

HoloMind: A Minimal Autonomous Learning System Driven by Curiosity, Confidence, and Self-Termination

Ghlich oghli Hossein
Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
article

HoloMind: A Minimal Autonomous Learning System Driven by Curiosity, Confidence, and Self-Termination

Ghlich oghli Hossein
article en

Abstract

HoloMind is a minimal autonomous learning system capable of self-directed learning, curiosity-driven inquiry, and confidence-regulated learning termination. It operates without external reward functions, reinforcement learning, or human feedback. The system architecture includes an embedding-first memory design (STM, MTM, LTM), a curiosity engine, and a reasoning module, demonstrating autonomous learning behavior validated through reproducible test logs.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 19%
Reinforcement Learning in Robotics
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HoloMind: A Minimal Autonomous Learning System Driven by Curiosity, Confidence, and Self-Termination — Ghlich oghli Hossein · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS