Talks of Invited Speakers in presence (alphabetically ordered)

Invited Speakers	Talks
Cristina Cornelio Samsung AI in Cambridge	Derivable Scientific Discovery Scientific progress has long relied on discovering new laws through domain expertise and experimental validation. Modern AI can now generate candidate hypotheses at unprecedented scale and speed. Yet this creates a critical bottleneck: without rigorous, scalable verification, the volume of AI-generated hypotheses risks overwhelming discovery rather than accelerating it. Verification is not an afterthought but the foundation of meaningful AI-assisted science. This talk presents a vision for scientific discovery in which models are derivable from explicit axioms while using minimal experimental data. This allows understanding why a law holds, when to trust it, and what must change when it fails. I will present three complementary systems we developed to achieve this: 1) AI-Descartes: uses symbolic regression to propose candidate models from data, then applies logical reasoning to select those most consistent with established axioms; 2) AI-Hilbert: integrates polynomial optimization with logical constraints, enforcing theoretical consistency and empirical validity simultaneously; and 3) AI-Noether: when current theory cannot derive a hypothesis, it proposes a minimal set of new axioms that make the hypothesis derivable. Together, these methods establish a new paradigm of "derivable scientific discovery", where integrating data and logic transforms AI from a mere hypothesis generator into a system that produces meaningful, verifiable laws.
Anders C. Hansen Cambridge	Necessary mechanisms for super AI and stopping hallucinations--The consistent reasoning paradox and the indeterminacy function Creating Artificial Super Intelligence (ASI) (AI that surpasses human intelligence) is the ultimate challenge in AI research. This is, as we will discuss, fundamentally linked to the problem of avoiding hallucinations (wrong, yet plausible answers) in AI. We will describe a key mechanism that must be present in any ASI. This mechanism is not present in any modern chatbot and we will discuss how, without it, ASI will never be achievable. Moreover, we reveal that AI missing this mechanism will always hallucinate. Specifically, this mechanism is the computation of what we call an indeterminacy function. An indeterminacy function determines when an AI is correct and when it will not be able to answer with 100% confidence. The root to these findings is the Consistent Reasoning Paradox (CRP), which is a new paradox in logical reasoning that we will describe in the talk. The CRP shows that the above mechanism must be present as – surprisingly – an ASI that is ‘pretty sure’ (more than 50%) can rewrite itself to become 100% certain. It will compute an indeterminacy function and either be correct with 100% confidence, or it will not be more than 50% sure. The CRP addresses a long-standing issue that stems from Turing’s famous statement that infallible AI cannot be intelligent, where he questions how much intelligence may be displayed if an AI makes no pretence at infallibility. The CRP answers this – consistent reasoning requires fallibility – and thus marks a necessary fundamental shift in AI design if ASI is to ever be achieved and hallucinations to be stopped.
Alessandro Sperduti Università di Padova	Learning neuro-symbolic convergent term rewriting systems Building neural systems that can learn to execute symbolic algorithms is a challenging open problem in artificial intelligence, especially when aiming for strong generalization and out-of-distribution performance. In this talk, I introduce a general framework for learning convergent term rewriting systems using a neuro-symbolic architecture inspired by the rewriting algorithm itself. I present two modular implementations of such architecture: the Neural Rewriting System (NRS) and the Fast Neural Rewriting System (FastNRS). As a result of algorithmic-inspired design and key architectural elements, both models can generalize to out-of-distribution instances, with FastNRS offering significant improvements in terms of memory efficiency, training speed, and inference time. We evaluate both architectures on four tasks involving the simplification of mathematical formulas and further demonstrate their versatility in a multi-domain learning scenario, where a single model is trained to solve multiple types of problems simultaneously.
Kelin Xia NTU Singapore	Mathematical AI: from topological data analysis to topological deep learning A central challenge in artificial intelligence (AI)-driven molecular science lies in efficiently representing molecular data and developing learning architectures that capture intrinsic structure-function relationships. In this work, we introduce advanced mathematics-based molecular representations and learning frameworks. Molecular structures and interactions are encoded using high-order topological and algebraic representations, including Rips complexes, Alpha complexes, Neighborhood complexes, Dowker complexes, Hom-complexes, Tor-algebras, Rhomboid tilings, Sheaves, Categories, etc. Building on these foundations, we design physics-informed geometric and topological deep learning models that systematically integrate high-order, multiscale, and periodic information of molecular systems. These models have been successfully applied to diverse molecular datasets across chemistry, biology, and materials science, demonstrating their versatility and effectiveness in uncovering complex structural-functional relationships.
Barbara Tversky Stanford University & Columbia Teachers College	Mind in Motion: How Action Shapes Thought I will argue that spatial thinking is the foundation of thought, not the entire edifice, but the foundation. I will bring support from neuroscience, language, gesture, and visualizations and bring them together with the notion of spraction, actions in space create abstractions. I will also put forth that these findings about human thought and creativity present challenges to current GenAI.