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<span style="background-color: rgb(255, 255, 255);"><b>First International Workshop on LLMs and KRR for Trustworthy AI (LMKR-TrustAI 2025)</b></span></div>
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<b><br>
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Held in conjunction with <a title="https://kr.org/KR2025/" href="https://kr.org/KR2025/">
KR 2025</a></div>
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Half day, 11-13 November 2025 (TBD) </div>
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Paper submission: August 4, 2025</div>
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Workshop web site: <a href="https://sites.google.com/view/lmkr-trustai-2025/home">
https://sites.google.com/view/lmkr-trustai-2025/home</a> </div>
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<b>Call for Papers<br>
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<b>Overview</b></div>
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The emergence of large language models (LLMs) has brought significant opportunities for developing scalable and generalisable AI applications more easily. Compared to knowledge representation and reasoning (KRR) methods, LLMs demonstrate remarkable capability
in encoding linguistic knowledge, enabling them to generate human-like text and generalise across diverse tasks with minimal domain-specific training. However, LLMs’ reliance on statistical patterns rather than explicit reasoning mechanisms raises concerns
about factual consistency, logical coherence, vulnerability to hallucinations, bias and misalignment with human values. This workshop focuses on an emerging research paradigm: the integration of LLMs with KRR techniques to enhance transparency, verifiability
and robustness in AI systems. We explore approaches that incorporate structured knowledge (ontologies, knowledge graphs, symbolic logic, etc.), neuro-symbolic methods, formal reasoning frameworks and explainability techniques to improve the trustworthiness
of LLM-driven decision-making.</div>
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The workshop will feature invited talks from leading experts, research paper presentations, and interactive discussions on bridging probabilistic learning with symbolic reasoning for trustworthy AI. By bringing together researchers from KRR and deep learning,
this workshop aims to foster new collaborations and technical insights to develop AI systems that are both powerful and trustworthy.</div>
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Topics of interest include but are not limited to:</div>
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<div role="presentation" class="elementToProof">Knowledge-grounded language models</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Hybrid neuro-symbolic architectures</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Reasoning-aware prompt engineering</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Logical consistency checks in LLM outputs</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Uncertainty and automated verification</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Causality and reasoning</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Explainability and controllability</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Commonsense reasoning integrating LLMs and KRR</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Reinforcement learning for ensuring safety and trustworthiness</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Alignment and preference-guided LLMs</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Multi-agent AI frameworks</div>
</li><li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36);">
<div role="presentation" class="elementToProof">Benchmarks, datasets and quantitative evaluation metrics</div>
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<div role="presentation" class="elementToProof">Evaluation and user studies in real-world applications</div>
</li></ul>
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<b>Organising Committee</b></div>
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<li style="font-family: "Segoe UI", "Segoe UI Web (West European)", "Helvetica Neue", sans-serif; font-size: 15px; color: rgb(36, 36, 36); direction: ltr; align-self: start;">
Maurice Pagnucco, UNSW, Australia<br>
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Yang Song, UNSW, Australia</li></ul>
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<b>Program Committee</b></div>
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<div role="presentation" class="elementToProof">Professor Tony Cohn, University of Leeds, UK</div>
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<div role="presentation" class="elementToProof">Dr Mingming Gong, University of Melbourne, Australia</div>
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<div role="presentation" class="elementToProof">Professor Gerhard Lakemeyer, RWTH Aachen, Germany</div>
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<div role="presentation" class="elementToProof">Professor Fangzhen Lin, HKUST, China</div>
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<div role="presentation" class="elementToProof">Professor Tim Miller, University of Queensland, Australia</div>
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<div role="presentation" class="elementToProof">Dr Nina Narodytska, VMware Research, USA</div>
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<div role="presentation" class="elementToProof">Associate Professor Abhaya Nayak, Macquarie University, Australia</div>
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<div role="presentation" class="elementToProof">Professor Ken Satoh, National Institute of Informatics, Japan</div>
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<div role="presentation" class="elementToProof">Professor Michael Thielscher, University of New South Wales, Australia</div>
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<div role="presentation" class="elementToProof">Professor Guy Van den Broeck, UCLA, USA</div>
</li></ul>
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<b>Important Dates</b><br>
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Paper submission: August 4, 2025</div>
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Paper notification: August 25, 2025</div>
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Workshop date and time: Half-day during November 11-13, 2025 (TBD)</div>
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<b>Submissions</b></div>
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Contributions may be regular papers (up to 9 pages) or short/position papers (up to 5 pages), including everything. Submissions should follow the KR 2025 formatting guidelines and be submitted through the submission page. Each submission will be reviewed by
at least two program committee members. We also welcome submissions that have recently been accepted in top AI conferences. At least one author of each accepted paper will be required to attend the workshop to present the contribution.<br>
Submission link: <a href="https://openreview.net/group?id=kr.org/KR/2025/Workshop/LMKR-TrustAI">
https://openreview.net/group?id=kr.org/KR/2025/Workshop/LMKR-TrustAI</a> </div>
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Best regards,</div>
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Maurice Pagnucco, Yang Song</div>
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Organisers, KR 2025 Workshop on LLMs and KRR for Trustworthy AI </div>
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and that any use, dissemination, forwarding, printing, or copying of this email and any file attachments is strictly prohibited. If you have received this email in error, please notify me immediately by return email and destroy this email.</em></p>
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