Research

Something historic happened on January 1, 2026. The Association for Computing Machinery (ACM), the world’s largest organization of computing professionals, flipped the switch on one of the most significant changes in academic publishing history. Every single article, conference paper, and research artifact in the ACM Digital Library is now completely free to access. No subscriptions. No paywalls. Just open knowledge for everyone. This isn’t a small collection we’re talking about. The ACM Digital Library houses over 600,000 full-text articles spanning decades of computer science research, from foundational algorithms to cutting-edge AI breakthroughs. If you’ve ever tried to access a research paper and hit a paywall asking for $25 or more for a single PDF, you understand why this matters. ...

Key Highlights The Big Picture: Google’s 2025 AI research pushes models from tools to true utilities, with Gemini 3 leading the charge. Technical Edge: Gemini 3 Flash delivers Pro‑grade reasoning at Flash‑level latency and cost, redefining performance per watt. The Bottom Line: These advances power smarter Pixel phones, faster Search, and breakthrough science—so you’ll feel AI’s impact today. Google’s 2025 AI research breakthroughs mark a shift from “AI as a feature” to “AI as a partner.” From multimodal reasoning in Gemini 3 to open‑source efficiency in Gemma 3, the company is turning cutting‑edge research into everyday advantages. ...

Key Highlights The Big Picture: RNACOREX reveals hidden miRNA‑mRNA regulatory maps across dozens of tumor types. Technical Edge: AI‑level survival prediction with transparent, interpretable explanations. The Bottom Line: Researchers can now prioritize real‑world biomarkers faster, accelerating precision‑cancer research 🎉. RNACOREX is the newest open‑source platform from the University of Navarra that turns massive gene‑expression data into an easy‑to‑read molecular map. By integrating curated databases with TCGA tumor data, the tool uncovers the invisible networks that drive cancer survival—and it does so with the accuracy of modern AI while staying fully explainable. ...

Key Highlights Over 80 new scientific systems powered by NVIDIA accelerated computing platform have been unveiled globally in the last year America’s largest academic supercomputer, the 300-petaflop Horizon system, is set to accelerate breakthroughs in science and engineering NVIDIA-accelerated supercomputers are fueling research in areas such as healthcare, weather and climate modeling, robotics, and materials science The world of research is undergoing a significant transformation, driven by the increasing adoption of accelerated computing. This move reflects broader industry trends, where researchers are leveraging the power of artificial intelligence (AI) and high-performance computing to tackle complex problems. At the forefront of this revolution is NVIDIA, whose accelerated computing platform is being used to power a new wave of supercomputers around the globe. ...

Simply telling someone a claim is “false” rarely changes their mind. Researchers have now built something far more sophisticated: an AI system that doesn’t just label misinformation—it creates full-blown debates to dismantle it. The catch? When this system gets it wrong, it’s persuasive enough to spread the very misinformation it was designed to fight. The Core Concept: AI Agents Duke It Out in Structured Debates The ED2D (Evidence-based Debate Detection) framework operates like a high-stakes debate tournament. Instead of one AI making a judgment call, the system creates two competing teams: ...

When AI agents dive into complex research tasks, they face a challenge remarkably similar to a detective drowning in case files. Traditional AI research methods pile up information like an endless scroll, eventually collapsing under their own weight. Enter IterResearch—a breakthrough approach that’s rewriting the rules of how AI handles deep research. The Core Problem: Why Traditional AI Research Hits a Wall The conventional approach to AI research, known as the mono-contextual paradigm, works like a single, ever-expanding notebook. Every web search result, every piece of data, every thought process gets appended to one continuous context. It’s simple in theory, but catastrophic in practice. ...

Scientific Machine Learning (SciML) combines data-driven learning methods with traditional physics-based modeling to tackle complex problems in science and engineering. Yet designing effective SciML models remains heavily dependent on expert knowledge—a time-intensive, labor-demanding process that creates a significant bottleneck in scientific progress. What if we could automate this expertise-driven process? Enter AgenticSciML. What Is AgenticSciML? AgenticSciML is a collaborative multi-agent system composed of over 10 specialized AI “agents” working together to discover novel solutions for scientific problems. Think of it as a brainstorming team where each member brings different expertise to the table. ...

The quest for effective treatments against infectious diseases has led researchers to explore the potential of antibody therapies. This move reflects broader industry trends towards personalized medicine and targeted interventions. By designing synthetic antibodies that can neutralize specific pathogens, scientists aim to reduce the severity of infections and even cure chronic conditions like HIV. One promising area of research focuses on the H5N1 avian influenza virus, also known as bird flu. Researchers like Runhong Zhou and his team at Hong Kong University have developed innovative antibody therapies that target multiple parts of the virus, increasing their efficacy. For instance, Zhou’s team has created an antibody that targets the stem region of proteins on the virus’s surface and receptors on human cells, demonstrating superior results in cell-based experiments. ...

The intersection of artificial intelligence and mathematics is yielding groundbreaking results, with Google DeepMind’s AI for Math Initiative at the forefront of this revolution. By leveraging AI-powered tools, mathematicians can now tackle complex problems with unprecedented speed and accuracy. This move reflects broader industry trends, where AI is being increasingly used to augment human capabilities in various fields. At the heart of this initiative are five prestigious research institutions: Imperial College London, Institute for Advanced Study, Institut des Hautes Études Scientifiques (IHES), Simons Institute for the Theory of Computing (UC Berkeley), and Tata Institute of Fundamental Research (TIFR). These institutions will work together to identify areas where AI can drive mathematical breakthroughs, develop new tools and infrastructure, and accelerate the pace of discovery. ...

The rapid advancement of DNA sequencing technologies has led to an explosion of genomic data, with over 100 petabytes of information currently stored in central databases such as the American SRA and the European ENA. This move reflects broader industry trends towards big data and precision medicine. However, searching through these vast amounts of data has been a significant challenge for researchers, requiring massive computing power and resources. To address this issue, computer scientists at ETH Zurich have developed a digital tool called “MetaGraph,” which enables efficient and accurate searching of petabase-scale sequence repositories. As Professor Gunnar Rätsch notes, “It’s a kind of Google for DNA.” This innovative search engine allows researchers to quickly identify specific DNA sequences, including those related to antibiotic resistance and unknown pathogens. ...