Integrated Calendar

Two major theories compete to explain the origin of aging. The first, proposed by Leo Szilard in 1959, attributes aging to DNA damage. The second, articulated by Robin Holliday in the 1980s, emphasizes epigenetic alterations. While both reveal plausible molecular origins of aging, they leave important puzzles unresolved. First, mutation and epimutation burdens increase linearly with age, whereas aging phenotypes exhibit strongly nonlinear behavior. Second, key aging phenotypes cannot be traced to specific genetic or epigenetic changes; instead, they emerge collectively from their cumulative effects on cellular function.In this talk, I will present a network resilience theory of aging that resolves these puzzles. Network resilience is formalized as the ability of a network to sustain its basic functions under changes in its topology and dynamical variables. Our theory conceptualizes aging as a progressive loss of network resilience as cells approach a novel critical mutation-epigenetic line. We identify two regimes of cellular stability, with young cells remaining resilient while older cells exhibit increased susceptibility. Using GTEx data and numerical simulations, we link transcriptional noise to cellular susceptibility and reproduce delayed immune activation observed in aging. Overall, our theory offers a novel perspective on aging based on resilience and critical phenomena.

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.

One hardly needs to convince theWeizmann community how excitingmathematics and science can be. Yet alltoo often these subjects in school aredreary and mundane, taught as a set offacts that need to be memorized andprocedures that need to be mastered.This does little to help inspire the nextgeneration of mathematicians andscientists. Education researchers havebeen investigating ways to narrow thegap between scientific disciplines andtheir school counterparts for decades,yet this gap has its institutionalrationalities, making the gap frustratinglypersistent. In the talk, I will discuss whythis is a “wicked” problem and presentsome research on approaches to bringthe ethos of the academic disciplinesinto the school subjects.

Dear Colleagues,You are cordially invited to a scientific and application focused webinar entitled Vesiculab: Advancing the Extracellular Vesicle Workflow. This webinar will present state of the art approaches for improving reproducibility, analytical rigor, and translational relevance in extracellular vesicle research, with an emphasis on practical solutions for everyday laboratory workflows. The presentation will be delivered by Dr Dimitri Aubert, PhD, CEO of Vesiculab. Scientific topics include:Fast size exclusion chromatography for efficient EV isolation,Total EV staining strategies for in vitro and in vivo studies,Optimized EV sample preparation for analytical and functional assays,Calibration principles for nanoflow cytometry and fluorescence NTA,Best practices for EV handling, storage, and preservation.