arXiv:2604. 03881v2 Announce Type: replace-cross Abstract: Encouraging pro-environmental behavior remains a major challenge for sustainable cities.
arXiv:2606. 10861v1 Announce Type: cross Abstract: LLM-powered chatbots are increasingly embedded in everyday workflows, raising sustainability concerns due to their energy use.
arXiv:2607. 25726v1 Announce Type: new Abstract: Recommender systems mediate everyday consumption, offering a promising channel for encouraging sustainable choices.
arXiv:2608. 12350v1 Announce Type: cross Abstract: The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cost electricity for AI-driven data center development.
arXiv:2607. 24341v1 Announce Type: new Abstract: Recent studies use Large language models (LLMs) to simulate human opinions and decisions by prompting models with demographic, attitudinal, or persona-based descriptions.
arXiv:2608. 20320v1 Announce Type: new Abstract: Travel behavior research increasingly combines digital data collection with predictive modeling, yet these stages are often developed and evaluated separately.
arXiv:2607. 17437v1 Announce Type: new Abstract: Large language model (LLM) agents offer a generative approach to simulating human behavior under conditions that may have few or no direct historical analogues, a common challenge in disaster and infrastructure-disruption planning.
arXiv:2509. 05364v2 Announce Type: replace-cross Abstract: Residential buildings contribute significantly to energy use, health outcomes, and carbon emissions.
arXiv:2608. 12582v1 Announce Type: cross Abstract: AI journaling tools can tailor prompts to a person's own sensed behavior, but it is unclear which behaviors respond to them.
arXiv:2606. 18258v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a wide range of human-like behaviors, from expressing thoughts and emotions, to engaging in relationship-building with users, to refusing requests and maintaining boundaries.
This scoping review examined 48 studies on generative AI chatbots designed to deliver motivational interviewing (MI). It found that most systems were text‑based and disembodied, with about half incorporating dynamic adaptation, and that safety reporting was inconsistent. While user perceptions were generally positive and many studies reported MI‑consistent interactions, evidence for sustained behavioral or functional change remains limited.
arXiv:2603. 23433v3 Announce Type: replace Abstract: AI agents are becoming active decision-makers on the Internet.
PersonaMem-v3 is a benchmark and evaluation harness designed to assess omni-platform personal intelligence for AI agents. It is built from over one million anonymized real-world engagement histories, covering social media, chatbots, calendars, and AI companions, and tracks user preferences and habits over time. The benchmark tests agents on personalization, LLM-powered recommendation, proactiveness, agentic tool use, and geo-temporal reasoning, evaluating their ability to infer holistic user understanding, personalize responses, rerank recommendations, follow user steering, and avoid inappropriate personalization.