arXiv:2512.07306v1 Announce Type: cross
Abstract: We introduce a constraint-programming framework for generating synthetic populations that reproduce target statistics with high precision while enforcing full individual consistency. Unlike data-driven approaches that infer distributions from samples, our method directly encodes aggregated statistics and structural relations, enabling exact control of demographic profiles without requiring any microdata. We validate the approach on official demographic sources and study the impact of distributional deviations on downstream analyses. This work is conducted within the Pollitics project developed by Emotia, where synthetic populations can be queried through large language models to model societal behaviors, explore market and policy scenarios, and provide reproducible decision-grade insights without personal data.
It’s About Time: The Temporal and Modal Dynamics of Copilot Usage
arXiv:2512.11879v1 Announce Type: cross Abstract: We analyze 37.5 million deidentified conversations with Microsoft’s Copilot between January and September 2025. Unlike prior analyses of AI usage,