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cs.AI2026
Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design
Manisha Dubey, Rimvydas Rubavicius, N. Siddharth +1
Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior. Bayesian inverse planning provides a principled framework for such inferen…
cs.LG2026
AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
Magdalena Proszewska, N. Siddharth
Graph Neural Networks (GNNs) have advanced significantly in handling graph-structured data, but a comprehensive framework for evaluating explainability remains lacking. Existing ev…
cs.AI2026
Towards Human Motion World Models via Executable Behaviour Representations
Rimvydas Rubavicius, Manisha Dubey, N. Siddharth +1
Human motion world models should capture motion's intentionality by being executable: adaptable to different actions and capable of assessing motion quality. To achieve this, we in…