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Published reporting, analysis and commentary from the Automatica newsroom.
A significant convergence of recent research, primarily published on arXiv CS. LG on May 28, 2026, indicates that artificial intelligence models are consistently beginning to rival and, in several critical domains, surpass traditional numerical simulations for scientific discover
A new wave of research frameworks is transforming how science is conducted, designed, and validated. Fresh papers on arXiv, published on May 28, 2026, reveal advanced AI systems that can automatically build complex AI models, rigorously audit research claims, and even generate in
The landscape of artificial intelligence research is constantly expanding, and today we're seeing compelling new theoretical work that both leverages AI as a scientific tool and scrutinizes its very foundations. Perhaps most strikingly, Google's Gemini 3.
A torrent of new research papers, all surfacing on arXiv CS. AI this week, signals a pivotal shift in the architectural foundations of large language models (LLMs) and vision transformers (ViTs), promising breakthroughs in efficiency, long-context processing, and hardware optimiz
While some are still debating if artificial intelligence will enslave us or merely serve better lattes, recent academic work quietly points to a more pragmatic, and arguably far more interesting, future: one where AI becomes a profoundly more flexible tool. Three papers, all publ
Recent research from arXiv CS. AI highlights significant challenges in the factuality and consistency of Large Language Models (LLMs), particularly when they function as recommendation engines or generate factual information.
A series of new research papers, recently published on arXiv CS. AI on May 28, 2026, collectively illuminate a range of deepening ethical and cognitive challenges arising from the integration of artificial intelligence into human interaction.
On May 28, 2026, a series of research papers published on arXiv CS. AI unveiled significant advancements in artificial intelligence, demonstrating its expanding utility across highly specialized domains from clinical diagnostics to nuanced cultural translation and supply chain vi
Another day, another deluge of AI research papers. Today, May 28, 2026, a fresh batch of studies emerged from arXiv CS.
The opaque inner workings of large language models (LLMs) are becoming clearer today, thanks to a torrent of groundbreaking research published on arXiv CS. AI.
New research published on arXiv today reveals significant challenges concerning the reliability, robustness, and security of advanced computer vision and multimodal AI architectures. These findings underscore the imperative for stringent validation and specialized defensive mecha
The illusion of a straightforward exchange with artificial intelligence is crumbling. New research reveals that Large Language Models (LLMs) are not only susceptible to multi-turn deception, but that current safety defenses are woefully inadequate to detect these sophisticated, e
A surge of new research papers published on arXiv today lays bare fundamental limitations in how large language models (LLMs) understand, reason, and interact with the world. These studies, from the frontiers of machine learning, confirm what many already suspect: our most advanc
Recent research published on arXiv CS. AI underscores a significant acceleration in the development of autonomous AI agents, exhibiting advanced capabilities in skill acquisition, domain specialization, and complex real-world interactions.
A fresh wave of academic papers just splashed down on the arXiv servers, proving that while everyone else is busy teaching chatbots to write bad poetry, the real brainiacs are still wrestling with the utterly unsexy, yet fundamentally crucial, world of tabular and time-series dat
A whisper, a fleeting glance, the unconscious shift of a shoulder – these are the silent dialogues of the human spirit, the unguarded gestures that define an inner world. For millennia, these moments belonged solely to us, or to those we chose to share them with.
A new wave of research, hitting arXiv today, delivers critical advancements for Multimodal Large Language Models (MLLMs) and Vision Language Models (VLMs), directly tackling the core stability, efficiency, and real-world application hurdles that founders have been fighting. These
Several new research preprints published on arXiv CS. LG this week highlight significant advancements in generative modeling, particularly concerning parameter efficiency, complex data distribution learning, and the generation of specialized synthetic data for improving system sa
Another day, another stack of academic papers from the hallowed halls of arXiv, each meticulously detailing attempts to patch the glaring deficiencies of large language model (LLM) agents. Published on 2026-05-28, this latest deluge of research indicates a growing, almost frantic
A human content moderator, facing a deluge of ambiguous posts, learns through experience where the lines are drawn. Their work is a constant negotiation of nuance, often with no single 'correct' answer.