HereBot: An Artificial Curiosity Engine for One-Shot Language Learning in a Non-Transformer Cognitive Architecture
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Abstract
Artificial intelligence today is mainly trained on transformer architecture and later fine-tuned along
with data segregation requiring enormous training data and machine intensive processes . Unlike a
traditional transformer, the research of Herebot started with a simple question: How do cognitive
beings actually learn some concepts in scoped or classified context? And can that same mecha-
nism be applied in order to learn within machine learning concepts rather than using a transformer
concept? The main difference where the transformer and Herebot differ is, transformer does encod-
ing and embedding, tokenization etc. processes which includes heavy computational processing.
Whereas an alternative architecture can hardcode documented learning traits of cognitive beings
and use them instead of transformer-like training to make or justify the learning process. The
research due to its complexity currently is focused on textual data and theories on cognition and
memory. As biologically and psychologically the cognitive being’s mind and its working isn’t
fully documented yet, but even though a major portion is theoretics (theory papers as cited) exists,
computer algorithms from these were derived for Herebot.
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BibTeX
@misc{singh2026herebot,
title = {{HereBot: An Artificial Curiosity Engine for One-Shot Language Learning in a Non-Transformer Cognitive Architecture}},
author = {Hrirake Hrishi Singh and Shukla Shubhra},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23459018},
url = {https://zenodo.org/records/23459018},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - HereBot: An Artificial Curiosity Engine for One-Shot Language Learning in a Non-Transformer Cognitive Architecture
AU - Hrirake Hrishi Singh
AU - Shukla Shubhra
PY - 2026
DA - 2026/10/18
PB - Zenodo
DO - 10.5281/zenodo.23459018
UR - https://zenodo.org/records/23459018
AB - Artificial intelligence today is mainly trained on transformer architecture and later fine-tuned along with data segregation requiring enormous training data and machine intensive processes . Unlike a traditional transformer, the research of Herebot started with a simple question: How do cognitive beings actually learn some concepts in scoped or classified context? And can that same mecha- nism be applied in order to learn within machine learning concepts rather than using a transformer concept? The main difference where the transformer and Herebot differ is, transformer does encod- ing and embedding, tokenization etc. processes which includes heavy computational processing. Whereas an alternative architecture can hardcode documented learning traits of cognitive beings and use them instead of transformer-like training to make or justify the learning process. The research due to its complexity currently is focused on textual data and theories on cognition and memory. As biologically and psychologically the cognitive being’s mind and its working isn’t fully documented yet, but even though a major portion is theoretics (theory papers as cited) exists, computer algorithms from these were derived for Herebot.
ER -