Events

"Loop Soup" is a classical statistical physics model, which has a deep connection with many other models, including Gaussian free fields, conformal loop ensembles, loop-erased random walks, uniform spanning trees, the FK-Ising model, (possibly) the phi^4 model, etc. This talk will give an elementary introduction to this model, and present our recent result that the clusters of loop soups on R^3 and the metric graph of Z^3 have different fractal dimensions. This result corrects a key prediction in Wendelin Werner’s blueprint for the scaling limit of metric graph loop soups, and leads to a bunch of open questions. This is a joint work with Jian Ding (Peking University).

Large language models often produce errors—factual inaccuracies, biases, and reasoning failures known as "hallucinations". We show that LLMs' internal representations encode rich information about truthfulness, but in surprising ways. First, truthfulness information concentrates in specific tokens, allowing a dramatic improvement in error detection compared to using other token locations. However, these detectors don't generalize across datasets, revealing that truthfulness encoding is multifaceted rather than universal. Second, internal representations can predict the types of errors a model will make, enabling targeted mitigation strategies. Finally, we uncover a striking discrepancy: LLMs sometimes internally encode correct answers while consistently generating incorrect ones. We'll also discuss follow-up work building on these findings and their implications for developing more reliable language models.

Bio:
Hadas is a Research Fellow at Harvard's Kempner Institute, where she studies the internal mechanics of large AI models to improve their robustness, safety, and reliability. She focuses on problems where scaling compute and data falls short—such as hallucinations, harmful outputs, and biases—with the broader goal of developing controllable AI systems. She completed her PhD in the Technion under the supervision of Yonatan Belinkov.

Financial systems are becoming increasingly digital and decentralized, demanding a practical fusion of distributed systems security and economic theory. A key enabler of this change, blockchain technology, promises more private and egalitarian economic mechanisms, built by facilitating consensus between pseudonymous actors. However, the theoretical security of these systems may mask significant real-world risks. In this talk, I will present recent advances in bridging this gap between theory and practice. First, I will discuss the resolution of a decade-old puzzle: the lack of observed attacks on major consensus mechanisms. I will then distill the lessons learnt into a holistic approach to designing robust mechanisms for distributed pseudonymous systems and demonstrate its adoption in practice using several lines of work.