Davide Murari

Reasoning traces with identical final answers and identical entropy curves can conceal radically different internal dynamics, from protracted multi-hypothesis competition to sudden late-stage revision.

Identifying piecewise-smooth dynamical systems could revolutionize our understanding of complex systems in climate dynamics and mechanics.

Achieving robust neural network performance on non-Euclidean spaces could redefine stability benchmarks in machine learning applications.

Lipschitz-constrained Transformers, built from gradient flows, can provably approximate any Lipschitz-continuous function, offering a path to more robust and stable architectures.