Preprint released: "xInv: Explainable Optimization of Inverse Problems"

Kevin Denamganaï

Independent Researcher

My research investigates the conditions under which AI systems acquire and deploy structured symbolic representations — towards in-context grounding of novel atomic symbols and their systematic recombination into unseen configurations — spanning Compositional Generalisation, Formal Mathematics, Differentiable Language Models, and Physical Simulation. I have also investigated Language Emergence & Grounding (Emergent Communication), Unsupervised Representation Learning, Natural Language Processing, and Multi-Agent Deep Reinforcement Learning.