{"ID":2866182,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03729","arxiv_id":"2511.03729","title":"Beyond Chat: a Framework for LLMs as Human-Centered Support Systems","abstract":"Large language models are moving beyond transactional question answering to act as companions, coaches, mediators, and curators that scaffold human growth, decision-making, and well-being. This paper proposes a role-based framework for human-centered LLM support systems, compares real deployments across domains, and identifies cross-cutting design principles: transparency, personalization, guardrails, memory with privacy, and a balance of empathy and reliability. It outlines evaluation metrics that extend beyond accuracy to trust, engagement, and longitudinal outcomes. It also analyzes risks including over-reliance, hallucination, bias, privacy exposure, and unequal access, and proposes future directions spanning unified evaluation, hybrid human-AI models, memory architectures, cross-domain benchmarking, and governance. The goal is to support responsible integration of LLMs in sensitive settings where people need accompaniment and guidance, not only answers.","short_abstract":"Large language models are moving beyond transactional question answering to act as companions, coaches, mediators, and curators that scaffold human growth, decision-making, and well-being. This paper proposes a role-based framework for human-centered LLM support systems, compares real deployments across domains, and id...","url_abs":"https://arxiv.org/abs/2511.03729","url_pdf":"https://arxiv.org/pdf/2511.03729v1","authors":"[\"Zhiyin Zhou\"]","published":"2025-09-25T20:33:58Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}