Model Revolution

URL https://arxiv.org/pdf/2510.04871.pdf Stage Model Revolution Paradigm framing The paper operates within the field of AI reasoning models. It challenges the dominant paradigm of Large Language Models (LLMs), which leverage massive scale and data to solve problems. The paper identifies a crisis in this paradigm, noting LLMs' struggles with specific hard puzzle tasks. It proposes a […]

URL https://arxiv.org/pdf/2509.00244.pdf Stage Model Revolution Paradigm framing The paper addresses the emerging paradigm of AI-powered agentic systems, specifically Deep Research Tools (DRTs) that leverage Large Language Models for automated information synthesis. The dominant paradigm involves DRTs with fixed, developer-defined research strategies. This work challenges that model by identifying a crisis of rigidity and lack of

URL https://arxiv.org/pdf/2509.20303.pdf Stage Model Revolution Paradigm framing The paper operates within the established paradigms of cell biology and biophysics concerning eukaryotic chemotaxis, which include mechanistic models like the Brownian ratchet and conflicting chemosensing frameworks like compass-based versus pseudopod-centered models. It proposes a new, unifying paradigm where cell protrusive dynamics are governed by energy optimization. This

URL https://assets-eu.researchsquare.com/files/rs-70238/v1_covered.pdf?c=1631840663 Stage Model Revolution Paradigm framing The established paradigm in mathematical epidemiology for modeling disease diffusion relies on compartmental models (e.g., SIR) and complex network techniques. These models are effective at describing the initial exponential growth of a single pandemic wave. A more recent, alternative framework, the epidemic Renormalisation Group (eRG), draws on principles