Dominika Wilczok | Searcharxiv

arXiv2025

Peakspan: Defining, Quantifying and Extending the Boundaries of Peak Productive Lifespan

The unprecedented extension of the human lifespan necessitates a parallel evolution in how we quantify the quality of aging and its socioeconomic impact. Traditional metrics focusing on Healthspan (years free of disease) overlook the gradual erosion of physiological capacity that occurs even in the absence of illness, leading to declines in productivity and eventual lack of capacity to work. To address this critical gap, we introduce Peakspan: the age interval during which an individual maintains at least 90% of their peak functional performance in a specific physiological or cognitive domain. Our multi-system analysis reveals a profound misalignment: most biological systems reach maximal capacity in early adulthood, resulting in a Peakspan that is remarkably short relative to the total lifespan. This dissociation means humans now spend the majority of their adult lives in a "healthy but declined" state, characterized by a significant functional gap. We argue that extending Peakspan and developing strategies to restore function in post-peak individuals is the functional manifestation of rejuvenative biomedical progress and is essential for sustained economic growth in aging societies. Recognizing and tracking Peakspan, increasingly facilitated by artificial intelligence and foundational models of biological aging, is crucial for developing strategies to compress functional morbidity and maximize human potential across the life course.

q-bio.QM↗

arXiv2025

The Right to Be Remembered: Preserving Maximally Truthful Digital Memory in the Age of AI

Since the rapid expansion of large language models (LLMs), people have begun to rely on them for information retrieval. While traditional search engines display ranked lists of sources shaped by search engine optimization (SEO), advertising, and personalization, LLMs typically provide a synthesized response that feels singular and authoritative. While both approaches carry risks of bias and omission, LLMs may amplify the effect by collapsing multiple perspectives into one answer, reducing users ability or inclination to compare alternatives. This concentrates power over information in a few LLM vendors whose systems effectively shape what is remembered and what is overlooked. As a result, certain narratives, individuals or groups, may be disproportionately suppressed, while others are disproportionately elevated. Over time, this creates a new threat: the gradual erasure of those with limited digital presence, and the amplification of those already prominent, reshaping collective memory. To address these concerns, this paper presents a concept of the Right To Be Remembered (RTBR) which encompasses minimizing the risk of AI-driven information omission, embracing the right of fair treatment, while ensuring that the generated content would be maximally truthful.

cs.AI↗