Welcome!
The Workshop on Unifying Concept Representation Learning (UCRL 2026) will be co-located with ICLR 2026, in Rio de Janeiro, held on April 23-27, 2026.
About the workshop
Several areas at the forefront of AI research are currently witnessing a convergence of interests around the problem of learning high-quality concepts from data. Concepts have become a central topic of study in neuro-symbolic integration (NeSy). NeSy approaches integrate perception – usually implemented by a neural backbone – and symbolic reasoning by employing concepts to glue together these two steps: the latter relies on the concepts detected by the former to produce suitable outputs. Concepts are also used in Explainable AI (XAI) by recent post-hoc explainers and self-explainable architectures as a building block for constructing high-level justifications of model behavior. Compared to, e.g., saliency maps, these can portray a more abstract and understandable picture of the machine’s reasoning process, potentially improving understandability, interactivity, and trustworthiness, to the point that concepts have been called the lingua franca of human-AI interaction.
NeSy and XAI methods hinge on learned concepts being “high-quality”. Concepts with misaligned semantics may compromise the meaning of model explanations, reliability of NeSy architectures and human understanding of the underlying systems. Recent works propose to leverage disentangled representations to mitigate concept leakage, i.e., the presence of irrelevant information in the learned concepts. Causal Representation Learning (CRL) is a generalization of disentangled representation learning, when the latent variables are dependent on each other, e.g., due to causal relations.
The potential of leveraging CRL to learn more robust and leak-proof concepts is an emerging area of research with a growing number of approaches, but many open questions remain. In particular, what properties high-quality concepts should satisfy is unclear. Moreover, despite studying the same underlying object, research in NeSy, XAI and CRL is proceeding on mostly independent tracks, with minimal knowledge transfer. Separate branches differ in their working definitions of what concepts are and what desiderata they ought to satisfy, on what data and algorithms they should be learned with, and on how to properly assess their quality. This also means that approaches in one area often ignore insights from the others. As a result, the central issue of how to properly learn and evaluate concepts is largely unanswered.
The aim of this ICLR 2026 workshop is to bring together researchers from NeSy, XAI and CRL and from both industry and academia, who are interested in learning robust, semantically meaningful concepts.
Important Dates
- Paper submission deadline: January 30, 2026, 23:59 AoE
- Notification to authors: March 1, 2026, 23:59 AoE
- Camera-ready version: March 27, 2026, 23:59 AoE
- Workshop Date: April, 2026
Contact us at ucrl.iclr2026@gmail.com