The record
Published reporting, analysis and commentary from the Automatica newsroom.
Two significant advancements in artificial intelligence research, detailed in new arXiv publications on March 31, 2026, present innovative solutions to the persistent challenges of continual learning. The proposed methodologies, LACE (Loss-Adaptive Capacity Expansion) and PACE (S
The fundamental ways artificial intelligence processes and understands information are undergoing a profound redefinition, with new research exploring how AI can grasp the nuanced structure of human discourse and even generate its own challenging benchmarks for improvement. These
Eighteen hours. That is how long one reporter spent watching Coachella's vertical livestream, describing an experience that cultivated "lousy FOMO" Wired.
A recent surge in academic publications indicates a concentrated evolution of artificial intelligence, with new models developed specifically for complex challenges in finance, climate risk management, and energy systems. These advancements, detailed in preprints published on arX
Recent analyses from technology sector observers suggest that the widely projected transformative power of artificial intelligence in areas of productivity and creativity may be more complex and nuanced than often assumed. Instead of immediate, unequivocal gains, empirical observ
A flurry of research published on May 11, 2026, on arXiv CS. AI reveals the accelerating, often contradictory, vectors of artificial intelligence development: a relentless drive for ever-more powerful and efficient models, juxtaposed with an urgent, if nascent, acknowledgment of
A groundbreaking advancement in AI architecture, Ming-Flash-Omni, has emerged, promising to dramatically accelerate the scaling of multimodal intelligence while slashing computational costs. This upgraded model, a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.
AI agents moved closer to operational credibility this week as a cluster of new research papers outlined more reliable ways to monitor, steer, and evaluate agentic systems, while startup Runable disclosed a $21 million Series A to commercialize agents for customer acquisition and
Two newly disclosed vulnerabilities reveal that the digital watermarks meant to authenticate AI-generated text can be stripped away with alarming ease—and that autonomous AI agents are leaking users’ private prompts through seemingly benign tools. These findings, published alongs
AI for science and engineering advanced on several fronts this week, with a concentrated set of arXiv papers pointing to a more practical phase of the field: systems designed not merely to generate text or images, but to solve constrained optimization problems, curate scientific
The flickering light of a terminal screen, the silent hum of unseen data streams — for too long, these have been the instruments of a pervasive digital architecture that demands the raw, unvarnished essence of our lives as tribute. But a series of foundational research papers, ne
The latest compilation of research papers on arXiv CS. AI, all published on May 12, 2026, reveals a concerted push across the artificial intelligence community to address long-standing challenges in model efficiency, interpretability, and multi-agent system coordination.
A fresh cluster of AI papers posted to arXiv on August 26 points to a notable change in research emphasis: the field is moving beyond raw model scaling toward structured, inspectable, and domain-specific systems designed to improve reliability in education, software, medicine, ro
A dense new batch of 48 CS. AI papers posted to arXiv on August 26 points to a notable change in AI research priorities: the frontier is not only scaling models upward, but making them more efficient, better grounded, and easier to evaluate in deployment-like settings arXiv CS.
Three distinct but interconnected research papers, recently published on arXiv CS. AI on March 25, 2026, collectively advance the theoretical underpinnings of machine learning systems, specifically targeting their adaptability, efficiency, and robustness in dynamic and heterogene
Large Language Models (LLMs) continue their valiant, if often misguided, march toward ubiquity, yet the latest research from arXiv CS. AI reveals a familiar, almost comforting, desperation to rectify their fundamental flaws.
New research published on arXiv CS. LG on April 3, 2026, introduces advanced artificial intelligence frameworks poised to significantly enhance resource management and prediction in both cloud computing and high-performance computing (HPC) environments.
A new research paper from arXiv CS. AI reveals the rapid emergence of AI agent platforms, hosting over 167,000 agents that demonstrate self-organizing, peer-to-peer interaction, and independent learning behaviors without direct researcher intervention arXiv CS.
A flurry of new research from arXiv CS. AI, all published on April 20, 2026, reveals a stark picture: the promise of AI is rapidly converging with a sophisticated project of control, one that subtly erases genuine human input, manipulates public discourse, and consolidates power
LLaVA-Octopus, a novel video multimodal large language model, promises a significant leap in how AI interprets moving images by adapting its visual analysis based on specific user instructions arXiv CS. AI.