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.

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.

Topics of interest include but are not limited to:

Knowledge-grounded language models

Hybrid neuro-symbolic architectures 

Reasoning-aware prompt engineering

Logical consistency checks in LLM outputs 

Uncertainty and automated verification

Causality and reasoning

Explainability and controllability 

Commonsense reasoning integrating LLMs and KRR

Reinforcement learning for ensuring safety and trustworthiness 

Alignment and preference-guided LLMs 

Multi-agent AI frameworks

Benchmarks, datasets and quantitative evaluation metrics 

Evaluation and user studies in real-world applications

TBD

Professor Maurice Pagnucco, University of New South Wales, Australia

Associate Professor Yang Song, University of New South Wales, Australia

Professor Tony Cohn, University of Leeds, UK

Dr Mingming Gong, University of Melbourne, Australia

Professor Gerhard Lakemeyer, RWTH Aachen, Germany

Professor Fangzhen Lin, HKUST, China

Professor Tim Miller, University of Queensland, Australia

Dr Nina Narodytska, VMware Research, USA

Associate Professor Abhaya Nayak, Macquarie University, Australia

Professor Ken Satoh, National Institute of Informatics, Japan

Professor Michael Thielscher, University of New South Wales, Australia

Professor Guy Van den Broeck, UCLA, USA

Paper submission: August 4, 2025 August 9, 2025

Paper notification: August 31, 2025

Workshop date and time: Half-day during November 11-13, 2025 (TBD)

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.

Submission link: https://openreview.net/group?id=kr.org/KR/2025/Workshop/LMKR-TrustAI