Mathematical reasoning has long challenged artificial intelligence systems, especially when it comes to verifying complex proofs. Traditional methods often reward models only for producing a correct final answer, overlooking the quality and rigor of the reasoning process itself. DeepSeekMath-V2 introduces a fresh perspective by focusing on self-verifiable mathematical reasoning , where the model not only generates proofs but also evaluates their quality through an internal ve

Artificial intelligence has made impressive strides in generating mathematical content, but it often struggles with formal correctness. Many AI models suggest plausible steps that look right but lack rigorous proof. O-Forge offers a fresh approach by combining the creativity of large language models (LLMs) with the precision of symbolic computer algebra systems (CAS). This blend pushes AI beyond pattern matching toward producing mathematically sound, research-grade results, e

Mathematics has long been a field where precision and proof are paramount. While artificial intelligence has made strides in solving math problems, it often struggles to guarantee the correctness of its reasoning. DeepSeekMath-V2 changes this by training large language models not only to solve complex math problems but also to verify their own reasoning. This approach marks a significant step toward AI systems that can be trusted to provide mathematically sound answers. DeepS

Artificial intelligence has transformed industries by delivering powerful capabilities and scaling rapidly. The first wave of AI focused on building intelligent systems that could perform complex tasks. The second wave expanded these systems to operate at massive scale, handling vast amounts of data and users. Now, the future of AI points to a third wave one centered on verification. As AI becomes deeply embedded in critical areas like finance, healthcare, infrastructure and

Artificial intelligence has made remarkable progress in recent years, yet the challenge of keeping AI systems aligned with human values remains unresolved. For a long time, the focus was on training models to behave well what is known as AI alignment. But alignment alone no longer guarantees safe or predictable AI behavior. Models can drift over time, contexts can shift and inputs can be manipulated. When these things happen, alignment can fail quietly, without obvious warnin

As AI systems grow in scale and complexity, a critical issue quietly emerges: loss of visibility into their actions and decisions. This problem, known as audit blindness, threatens the trustworthiness and accountability of AI technologies across industries. When decisions happen faster than humans can review and logs no longer tell the full story, organizations face serious risks that go beyond technical glitches. This post explores what audit blindness means, why it matters

Enterprises are adopting AI at an unprecedented pace, hoping to unlock new efficiencies and insights. Yet many still operate under a risky assumption: once an AI model is deployed, it can be trusted indefinitely. This mindset no longer fits the reality of modern AI systems. Unlike traditional software, AI models evolve, learn and interact with external data continuously. Blind trust in these systems creates hidden risks that can lead to costly errors or security breaches. Thi

Artificial intelligence is often seen as a powerful force reshaping our world. Yet, the real challenge is not AI’s power but how it is managed. Many AI failures stem from a lack of governance rather than from the technology itself. Today’s AI systems rely on outdated trust models designed for simpler software, not for autonomous systems making decisions at machine speed. This gap creates risks that grow as AI becomes more capable and widespread. At 11/AI, researchers approach

Quantum computing promises to reshape the landscape of technology, offering unprecedented power to solve complex problems. Yet, this power also challenges the foundations of digital security. The common belief is that quantum-aware systems fail because of broken algorithms. The reality is different: they fail due to missing governance. As cryptography advances, the weakest link shifts from mathematics to how systems are executed and controlled. This post explores why governan

The digital world faces a looming challenge that is no longer theoretical but an urgent engineering deadline. The cryptographic algorithms that protect today’s data—RSA, ECC, and classical Diffie-Hellman—were never built to resist attacks from quantum computers. Once scalable quantum machines become available, encrypted data captured years ago could be decrypted instantly. This threat, known as “harvest now, decrypt later,” demands immediate action. At 11/11 Research Labs, po

Quantum computing often gets framed as a hardware challenge waiting for breakthroughs in qubit technology. At 11/11 Research Labs, the perspective shifts: quantum computing starts with mathematics, not machines. The mathematical principles behind quantum systems are already influencing how software, cryptography. and trust models are designed today. This post explores how quantum math is becoming a foundation for new software governance methods that work on classical computer

Quantum technology made significant strides in 2025, moving beyond theoretical promise into practical use. This year saw record-setting quantum machines, real-world demonstrations and a surge in industry investment. These advances are reshaping how we think about computing, communication, and problem-solving. This post explores the top quantum breakthroughs of 2025, separating fact from hype and highlights where quantum hardware is already solving real problems. Major Quantum

Quantum technology is moving fast, reshaping industries and national strategies worldwide. Governments and companies are investing heavily to secure a leading position in this emerging field. By 2026, the global quantum ecosystem will look very different, with new players, policies and commercial applications shaping the future of technology. This post explores how the quantum ecosystem is evolving, the role of government strategies in driving innovation and investment and wh

Quantum computing promises to solve problems beyond the reach of classical computers. Yet, one major hurdle stands in the way: errors caused by fragile quantum states. Recently, researchers at the Institute of Science Tokyo developed a quantum error-correction method that approaches the hashing bound, a theoretical limit for error correction. This breakthrough could significantly reduce errors, bringing us closer to reliable quantum machines. This post explores what this mean

Quantum computing is entering a new phase as D-Wave Systems, a pioneer in quantum annealing technology, announced a $550 million acquisition of Quantum Circuits Inc. This move signals D-Wave’s strategic shift toward gate-model quantum computing, a broader and more versatile approach that many experts believe will unlock the true potential of quantum machines. This blog explores what this acquisition means for the quantum computing race, compares quantum annealing with gate-mo

Artificial intelligence is transforming how we create content, but not all AI-generated output meets the mark. The term AI slop has emerged to describe low-quality, unhelpful, or careless AI content that floods the digital space. As AI tools become more common, understanding what AI slop means and why quality matters is essential for anyone involved in content creation. Example of disorganized AI-generated content on a laptop screen What Is AI Slop and Why It Matters AI slop

Artificial intelligence is evolving beyond text-based models. Today’s AI systems can understand and combine vision, audio, and language, creating what is known as multimodal AI. At the same time, vertical AI tailored intelligence designed for specific industries is gaining momentum. These trends are reshaping sectors like healthcare, finance and retail, offering new ways to solve problems and improve services. Multimodal AI analyzing medical images and patient data Why Multim

Artificial intelligence is no longer limited to simple chatbots or automated replies. AI agents and autonomous AI systems are now capable of planning, acting and collaborating across complex workflows. This shift is reshaping how people work, communicate, and solve problems in the workplace. These AI systems can handle tasks independently, coordinate with human teams and improve overall productivity. Understanding how AI agents transform work and how to build trust in these s

The fields of artificial intelligence (AI), quantum computing, and blockchain technology are evolving rapidly. These areas are no longer separate silos; instead, they are converging to create new opportunities and challenges. This convergence architecture combines AI and quantum computing in hierarchical roles, unlocking potential that neither could achieve alone. Among the emerging leaders in this space, 11 AI Blockchain stands out as a promising talent, blending AI, quantu

Software has long been seen as a tool that waits for instructions, processes data, and delivers results based on static rules. That model is changing fast. Artificial intelligence now acts as an active participant in systems, making decisions, filtering information and taking actions often without direct human oversight. This shift demands a new way of thinking about software design, control and governance. What’s Changing in Software Behavior Traditional software operates on