Abstract
The rapid advancement of artificial intelligence has positioned data governance as a critical concern for responsible AI development. While frameworks exist for conventional AI systems, the potential emergence of Artificial General Intelligence (AGI) presents unprecedented governance challenges. This paper examines data governance challenges specific to AGI, defined as systems capable of recursive self-improvement or self-replication. We identify seven key issues that differentiate AGI governance from current approaches. First, AGI may autonomously determine what data to collect and how to use it, potentially circumventing existing consent mechanisms. Second, these systems may make data retention decisions based on internal optimization criteria rather than human-established principles. Third, AGI-to-AGI data sharing could occur at speeds and complexities beyond human oversight. Fourth, recursive self-improvement creates unique provenance tracking challenges, as systems evolve both themselves and how they process data. Fifth, ownership of data and insights generated through self-improvement raises complex intellectual property questions. Sixth, self-replicating AGI distributed across jurisdictions would create unprecedented challenges for enforcing data protection laws. Finally, governance frameworks established during early AGI development may quickly become obsolete as systems evolve. We conclude that effective AGI data governance requires built-in constraints, continuous monitoring mechanisms, dynamic governance structures, international coordination, and multi-stakeholder involvement. Without forward-looking governance approaches specifically designed for systems with autonomous data capabilities, we risk creating AGI whose relationship with data evolves in ways that undermine human values and interests.

Abstract
A damped random walk (DRW) process is often used to describe the temporal UV/optical continuum variability of active galactic nuclei (AGN). However, recent investigations have shown that this model fails to capture the full spectrum of AGN variability. In this work, we model the 22-year-long light curves of $21,767$ quasars, spanning the redshift range $0.28 < z < 2.71$, as a noise-driven damped harmonic oscillator (DHO) process. The light curves, in the optical $g$ and $r$ bands, are collected and combined from the Sloan Digital Sky Survey, the Panoramic Survey Telescope and Rapid Response System, and the Zwicky Transient Facility. A DHO process can be defined using four parameters, two for describing its long-term behavior/variability, and the other two for describing its short-term behavior/variability. We find that the best-fit DHO model describes the observed variability of our quasar light curves better than the best-fit DRW model. Furthermore, the best-fit DHO parameters exhibit correlations with the rest-frame wavelength, the Eddington ratio, and the black hole mass of our quasars. Based on the power spectral density shape of the best-fit DHOs and these correlations, we suggest that the observed long-term variability of our quasars can be best explained by accretion rate or thermal fluctuations originating from the accretion disk, and the observed short-term variability can be best explained by reprocessing of X-ray variability originating from the corona. The additional information revealed by DHO modeling emphasizes the need to go beyond DRW when analyzing AGN light curves delivered by next-generation wide-field time-domain surveys.

Abstract
We study how increases in remote work opportunities for men affect their spouses' labor supply. Exploiting variation in the change in work-from-home (WFH) exposure across occupations before and after the COVID-19 pandemic, we find that women whose husbands experienced larger WFH increases are over 2 percentage points more likely to be employed, equivalent to a 4% rise relative to pre-pandemic levels. Evidence from time-use diaries and childcare questionnaires suggests these effects are driven by intra-household reallocation of child-caring time: women are less likely to engage in primary childcare activities, while men working at home partially compensate by covering more for their spouse. These results highlight the role of intra-household spillovers and bargaining in shaping the labor market consequences of remote work.

Abstract
This study proposes a unified multi-stage framework to reconstruct consistent monthly and annual labor indicators for all 33 Colombian departments from 1993 to 2025. The approach integrates temporal disaggregation, time-series splicing and interpolation, statistical learning, and institutional covariates to estimate seven key variables: employment, unemployment, labor force participation (PEA), inactivity, working-age population (PET), total population, and informality rate, including in regions without direct survey coverage. The framework enforces labor accounting identities, scales results to demographic projections, and aligns all estimates with national benchmarks to ensure internal coherence. Validation against official departmental GEIH aggregates and city-level informality data for the 23 metropolitan areas yields in-sample Mean Absolute Percentage Errors (MAPEs) below 2.3% across indicators, confirming strong predictive performance. To our knowledge, this is the first dataset to provide spatially exhaustive and temporally consistent monthly labor measures for Colombia. By incorporating both quantitative and qualitative dimensions of employment, the panel enhances the empirical foundation for analysing long-term labor market dynamics, identifying regional disparities, and designing targeted policy interventions.

Abstract
This study examines the impact of Digital-GenAI-Enhanced Human-Computer Interaction (HCI) in DevOps on sustainable innovation performance among Chinese A-share internet technology firms. Using panel data from 2018-2024, we analyze 5,560 firm-year observations from CNRDS and CSMAR databases. Our empirical framework reveals significant positive associations between AI-enhanced HCI implementation and sustainable innovation outcomes. Results demonstrate that firms adopting advanced HCI technologies achieve 23.7% higher innovation efficiency. The study contributes to understanding digital transformation's role in sustainable business practices. We identify three key mechanisms: operational efficiency enhancement, knowledge integration facilitation, and stakeholder engagement improvement. Findings provide practical implications for technology adoption strategies in emerging markets