arXiv:1912. 08786v3 Announce Type: replace-cross Abstract: Three generations of software have transformed the role of artificial intelligence in society.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
The article discusses the evolution of AI across three generations—from explicit logic to neural networks to large language models (LLMs)—and how LLMs introduce new systemic risks. It applies a forensic‑psychology profiling method to identify ten key features of LLMs, such as hallucinations, bias, and cognitive atrophy, revealing an entity that confabulates, amplifies user biases, and erodes human competence. The report concludes with a four‑pillar framework for AI resilience, emphasizing cognitive sovereignty, measurable control, partial autonomy, and openness to safeguard society.
arXiv:2607. 19292v1 Announce Type: cross Abstract: Current AI safety discourse still focuses disproportionately on visible failures, including obvious harms, dramatic misuse, and hypothetical catastrophic scenarios.
arXiv:2608.28593v1 Announce Type: new Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in...
arXiv:2606. 00621v1 Announce Type: cross Abstract: Generative artificial intelligence has fundamentally changed how content is now produced.
arXiv:2606. 23991v1 Announce Type: new Abstract: What is an agent?
The paper examines how large language model (LLM) outputs are increasingly used in contexts that demand justified interpretations, such as law, education, policy analysis, and public moral debate. It identifies a recurring failure—interpretive misplacement—where model-generated readings are treated as settled meanings without explicit interpretive frames, provenance, or defensible alternatives, leading to accountability loss. Drawing on philosophical hermeneutics, the author proposes design principles for human‑AI co‑interpretation, reorganizes existing LLM techniques into hermeneutically responsible patterns, and discusses implications for legal practice, education, scholarship, and public discourse, while framing digital hermeneutics as a literacy for critically engaging with AI‑mediated texts.