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<p><font face="Times New Roman">Call for papers:Special Issue of The
Knowledge Engineering Review on Memory-Augmented LLM Agents</font></p>
<font face="Times New Roman"><b>Introduction</b><br>
“Memory-Augmented LLM Agents” aims to bring together cutting-edge
research at the intersection of LLMs, memory architectures, agent
systems, and knowledge engineering, to address foundational and
applied challenges in developing memory-augmented LLM agents. We
invite original research papers, applied studies, and open-source
resources (e.g. tools, benchmarks, datasets) that focus on
improving the memory, reasoning, and adaptability of LLM-based
agents through structured knowledge, continual learning, and
multi-agent coordination.<br>
<b>Topics of interest include, but are not limited to:</b><br>
1. Memory augmented architectures for LLM-based agents<br>
2. Symbolic memory integration for long term reasoning and
planning<br>
3. Knowledge Mechanisms and Mechanistic Interpretability in
LLM/Agents<br>
4. Causal learning for LLM and causal agents<br>
5. Causal Interpretability in LLM/Agents<br>
For more information,please visit:
<a class="moz-txt-link-freetext" href="https://www.maxapress.com/ker/specials/155">https://www.maxapress.com/ker/specials/155</a><br>
<b>Guest Editors:</b><br>
Dr. Kun Kuang, Zhejiang University, China<br>
Dr. Ningyu Zhang, Zhejiang University, China<br>
Dr. Hang Yu, Shanghai University, China<br>
Dr. Tongtong Wu, Monash University, Australia<br>
<b>Submission deadline: </b>December 15, 2026<br>
<b>Submission System: </b><a class="moz-txt-link-freetext" href="https://mc.manuscriptcentral.com/ker">https://mc.manuscriptcentral.com/ker</a><br>
Please feel free to reach out if you require any further
information. We look forward to receiving your high-quality
submission and would appreciate it if you could also share this
call within your research network or potential contributors.</font>
<p><font face="Times New Roman"><br>
Learn more at <a class="moz-txt-link-freetext"
href="https://www.maxapress.com/ker" moz-do-not-send="true">https://www.maxapress.com/ker</a><br>
Submission System: <a class="moz-txt-link-freetext"
href="https://mc.manuscriptcentral.com/ker"
moz-do-not-send="true">https://mc.manuscriptcentral.com/ker</a><br>
Contact us: <a
class="moz-txt-link-abbreviated moz-txt-link-freetext"
href="mailto:ker@maxapress.com" moz-do-not-send="true">ker@maxapress.com</a></font></p>
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