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<h3 dir="ltr" style="line-height: 1.38; margin-top: 14pt; margin-bottom: 4pt;"><span style="font-family: Arial, sans-serif; font-size: 13pt; color: rgb(0, 0, 0); font-weight: 700;">Call for Papers: Special Issue on Explainable Neurosymbolic AI (X-NeSy)</span><span style="font-family: Arial, sans-serif; font-size: 13pt; color: rgb(0, 0, 0);"> in
the Neurosymbolic AI Journal </span></h3>
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<span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">Building learning systems that are both highly performant and truly understandable remains a central challenge in AI. While deep learning models excel at perceptual and statistical
tasks, their black-box nature can be a significant barrier to trust and deployment in critical applications. A promising and technically rich direction for overcoming this barrier is the integration of continuous, sub-symbolic learning with discrete, symbolic
structures, including logic, programs, or knowledge graphs.</span></p>
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<span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">This special issue aims to provide a dedicated forum for researchers exploring this “neurosymbolic” intersection to advance the current state of the art in Explainable AI (XAI).
While XAI has become a major field of research, we specifically invite contributions that leverage
<i>symbolic methods</i> as a core component of the modeling and interpretation process. The focus is mainly on technical contributions that go beyond feature attribution and general-purpose XAI methods, investigating how the synergy between the neural and symbolic
paradigms leads to models with built-in transparency and high-fidelity explanations.</span></p>
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<span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">We also explicitly welcome submissions from a diverse range of related fields, including
</span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0); font-weight: 700;">mechanistic interpretability</span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">,
</span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0); font-weight: 700;">causal inference</span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">,
</span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0); font-weight: 700;">formal methods</span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">, and
</span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0); font-weight: 700;">program synthesis</span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">. The special issue seeks to foster a cross-community
dialogue on the shared challenges and opportunities in creating AI systems whose reasoning processes are open to inspection, verification, and human-level understanding.</span></p>
<h4 dir="ltr" style="line-height: 1.38; margin-top: 12pt; margin-bottom: 2pt;"><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0); font-weight: 700;">Topics of Interest</span></h4>
<p dir="ltr" style="line-height: 1.38; margin-top: 12pt; margin-bottom: 12pt;"><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">We invite submissions of high-quality, original research on topics including,
<i>but not limited to</i>:</span></p>
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<span style="font-weight: 700;">Interpretable Representations:</span> Probing and aligning latent neural representations with human-interpretable concepts, structures, or programs.</p>
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<span style="font-weight: 700;">Knowledge Extraction:</span> Inducing explicit symbolic knowledge, such as logical rules or programs, that faithfully captures the behavior of a trained neural model.</p>
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<span style="font-weight: 700;">Mechanistic Interpretability:</span> Using symbolic methods to guide, constrain, or verify the discovery of concepts and functional circuits in neural networks.</p>
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<span style="font-weight: 700;">Explanations over Structured Data:</span> Generating faithful explanations for models operating over graphs (e.g., GNNs) or knowledge bases by extracting symbolic patterns or logical sub-structures.</p>
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<span style="font-weight: 700;">Causal Learning and Explanation:</span> Architectures and methods that integrate causal modeling with deep learning to produce robust causal explanations.</p>
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<span style="font-weight: 700;">Formal Verification for Explainability:</span> Applying formal methods to certify the faithfulness or logical consistency of explanations for neural-based systems.</p>
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<span style="font-weight: 700;">Transparent-by-Design Models:</span> Novel architectures that are inherently interpretable by virtue of their hybrid neuro-symbolic design.</p>
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<span style="font-weight: 700;">Generating Structured Explanations:</span> Moving beyond common feature attribution methods (e.g., LIME/SHAP) to generate more expressive explanations, such as counterfactuals constrained by symbolic knowledge or natural language
justifications grounded in logic.</p>
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<span style="font-weight: 700;">Foundations of Evaluation:</span> Development of novel benchmarks, datasets, and rigorous protocols for evaluating the fidelity and quality of symbolic explanations.</p>
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<h4 dir="ltr" style="line-height: 1.38; margin-top: 12pt; margin-bottom: 2pt;"><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0); font-weight: 700;">Important Dates</span></h4>
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<span style="font-weight: 700;">Paper Submission Deadline:</span> February 28, 2026. Papers submitted earlier will enter review immediately.</p>
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<span style="font-weight: 700;">Author Notification:</span> 6-8 weeks (on a rolling basis)</p>
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<h4 dir="ltr" style="line-height: 1.38; margin-top: 12pt; margin-bottom: 2pt;"><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0); font-weight: 700;">Guest Editors</span></h4>
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<span style="font-weight: 700;">Gustav Šír</span>, Czech Technical University, Czechia</p>
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<span style="font-weight: 700;">Giuseppe Marra</span>, KU Leuven, Belgium</p>
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<span style="font-weight: 700;">Roberto Confalonieri</span>, University of Padova, Italy</p>
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<p dir="ltr" style="line-height: 1.38; margin-top: 12pt; margin-bottom: 12pt;"><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">Contact email for the guest editors:
</span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(17, 85, 204);"><a href="mailto:x-nesy@googlegroups.com" data-outlook-id="070555c1-365e-4a30-be1b-36e08d872d3a" style="color: rgb(17, 85, 204); text-decoration: none; margin-top: 0px; margin-bottom: 0px;">x-nesy@googlegroups.com</a></span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"> </span></p>
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<span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0); font-weight: 700;">Author Guidelines:</span></h4>
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<span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">We invite full papers, dataset descriptions, survey papers, application reports and reports on tools and systems. Submissions must be original and should not have been published
previously or be under consideration for publication while being evaluated for this special issue. Authors can extend previously published conference or workshop papers - see the submission guidelines at<a href="https://neurosymbolic-ai-journal.com/content/author-guidelines" data-outlook-id="d0b33819-6c58-4348-9e84-3b351d2a65b2" style="color: rgb(0, 0, 0); text-decoration: none; margin-top: 0px; margin-bottom: 0px;">
</a></span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(17, 85, 204);"><a href="https://neurosymbolic-ai-journal.com/content/author-guidelines" data-outlook-id="7cff4520-039e-4341-90c3-dd54d4e91fb3" style="color: rgb(17, 85, 204); text-decoration: none; margin-top: 0px; margin-bottom: 0px;">https://neurosymbolic-ai-journal.com/content/author-guidelines</a></span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"> for
details. Submissions shall be made through the journal website at<a href="https://neurosymbolic-ai-journal.com/" data-outlook-id="afcf8379-7ca2-420f-b3d9-611928471ba5" style="color: rgb(0, 0, 0); text-decoration: none; margin-top: 0px; margin-bottom: 0px;">
</a></span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(17, 85, 204);"><a href="https://neurosymbolic-ai-journal.com/" data-outlook-id="7d8eca3f-22be-48e8-9f83-aa7135677f91" style="color: rgb(17, 85, 204); text-decoration: none; margin-top: 0px; margin-bottom: 0px;">https://neurosymbolic-ai-journal.com/</a></span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">.
Prospective authors must take notice of the submission guidelines posted at<a href="https://neurosymbolic-ai-journal.com/content/author-guidelines" data-outlook-id="85ffd4e0-e7ac-468c-954c-f44533fdb864" style="color: rgb(0, 0, 0); text-decoration: none; margin-top: 0px; margin-bottom: 0px;">
</a></span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(17, 85, 204);"><a href="https://neurosymbolic-ai-journal.com/content/author-guidelines" data-outlook-id="21ec57d3-fd83-4d1d-9a54-3fea4dad0e9e" style="color: rgb(17, 85, 204); text-decoration: none; margin-top: 0px; margin-bottom: 0px;">https://neurosymbolic-ai-journal.com/content/author-guidelines</a></span><span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">.</span></p>
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<span style="font-family: Arial, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);">Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "Special Issue on X-NeSy". All manuscripts
will be reviewed based on the journal's open and transparent review policy and will be made available online during the review process.</span></p>
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