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Explanation Quality Assessment as Ranking with Listwise Rewards
Thomas Bailleux, Tanmoy Mukherjee, Emmanuel Lonca +2
We reformulate explanation quality assessment as a ranking problem rather than a generation problem. Instead of optimizing models to produce a single "best" explanation token-by-to…
Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition
Tanmoy Mukherjee, Thomas Bailleux, Pierre Marquis +1
Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epistemic uncertainty (reducible…
Evaluating Robustness of Reasoning Models on Parameterized Logical Problems
Naïm Es-sebbani, Esteban Marquer, Yakoub Salhi +1
Logic provides a controlled testbed for evaluating LLM-based reasoners, yet standard SAT-style benchmarks often conflate surface difficulty (length, wording, clause order) with the…
Generalizing Analogical Inference from Boolean to Continuous Domains
Francisco Cunha, Yves Lepage, Miguel Couceiro +1
Analogical reasoning is a powerful inductive mechanism, widely used in human cognition and increasingly applied in artificial intelligence. Formal frameworks for analogical inferen…
Towards a Neurosymbolic Reasoning System Grounded in Schematic Representations
François Olivier, Zied Bouraoui
Despite significant progress in natural language understanding, Large Language Models (LLMs) remain error-prone when performing logical reasoning, often lacking the robust mental r…
Grounding Agent Reasoning in Image Schemas: A Neurosymbolic Approach to Embodied Cognition
François Olivier, Zied Bouraoui
Despite advances in embodied AI, agent reasoning systems still struggle to capture the fundamental conceptual structures that humans naturally use to understand and interact with t…
Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings
Hanane Kteich, Na Li, Usashi Chatterjee +2
Concept embeddings offer a practical and efficient mechanism for injecting commonsense knowledge into downstream tasks. Their core purpose is often not to predict the commonsense p…