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@ARTICLE{Zajzon:1052250,
author = {Zajzon, Barna and Bouhadjar, Younes and Fabre, Maxime and
Schmidt, Felix and Ostendorf, Noah and Neftci, Emre and
Morrison, Abigail and Duarte, Renato},
title = {{S}ym{S}eq{B}ench: a unified framework for the generation
and analysis of rule-based symbolic sequences and datasets},
publisher = {arXiv},
reportid = {FZJ-2026-00867},
year = {2025},
abstract = {Sequential structure is a key feature of multiple domains
of natural cognition and behavior, such as language,
movement and decision-making. Likewise, it is also a central
property of tasks to which we would like to apply artificial
intelligence. It is therefore of great importance to develop
frameworks that allow us to evaluate sequence learning and
processing in a domain agnostic fashion, whilst
simultaneously providing a link to formal theories of
computation and computability. To address this need, we
introduce two complementary software tools: SymSeq, designed
to rigorously generate and analyze structured symbolic
sequences, and SeqBench, a comprehensive benchmark suite of
rule-based sequence processing tasks to evaluate the
performance of artificial learning systems in cognitively
relevant domains. In combination, |SymSeqBench offers
versatility in investigating sequential structure across
diverse knowledge domains, including experimental
psycholinguistics, cognitive psychology, behavioral
analysis, neuromorphic computing and artificial
intelligence. Due to its basis in Formal Language Theory
(FLT), SymSeqBench provides researchers in multiple domains
with a convenient and practical way to apply the concepts of
FLT to conceptualize and standardize their experiments, thus
advancing our understanding of cognition and behavior
through shared computational frameworks and formalisms. The
tool is modular, openly available and accessible to the
research community.},
keywords = {Neurons and Cognition (q-bio.NC) (Other) / Artificial
Intelligence (cs.AI) (Other) / Machine Learning (cs.LG)
(Other) / Neural and Evolutionary Computing (cs.NE) (Other)
/ FOS: Biological sciences (Other) / FOS: Computer and
information sciences (Other)},
cin = {IAS-6 / PGI-15},
cid = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)PGI-15-20210701},
pnm = {5234 - Emerging NC Architectures (POF4-523) / BMBF
16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien
der künstlichen Intelligenz für die Elektronik der Zukunft
- NEUROTEC II - (BMBF-16ME0398K) / BMBF 16ME0399 -
Verbundprojekt: Neuro-inspirierte Technologien der
künstlichen Intelligenz für die Elektronik der Zukunft -
NEUROTEC II - (BMBF-16ME0399) / BMBF 03ZU1106CB - NeuroSys:
Algorithm-Hardware Co-Design (Projekt C) - B
(BMBF-03ZU1106CB) / BMFTR 03ZU2106CB - NeuroSys:
Algorithm-Hardware Co-Design (Projekt C) - B
(BMBF-03ZU2106CB) / WestAI - AI Service Center West
(01IS22094B)},
pid = {G:(DE-HGF)POF4-5234 / G:(DE-82)BMBF-16ME0398K /
G:(DE-82)BMBF-16ME0399 / G:(DE-Juel1)BMBF-03ZU1106CB /
G:(DE-Juel1)BMBF-03ZU2106CB / G:(BMBF)01IS22094B},
typ = {PUB:(DE-HGF)25},
doi = {10.48550/arXiv.2512.24977},
url = {https://juser.fz-juelich.de/record/1052250},
}