Pattern Recognition Lab

The crisis of AI-generated mathematics

The pursuit of mathematical truth has always been seen as one of humanity's highest intellectual endeavors—a pure, elegant conversation between rigorous logic and profound intuition. For centuries, the craft of mathematics has been defined by the slow, deliberate, human process of deep understanding: the struggle, the elegant breakthrough, the painstaking writing of a proof. But in a dizzying rush toward automated intelligence, this cherished practice is facing an existential threat. Can a machine truly *understand* mathematics, or is it merely mimicking the syntax of genius?

The Line in the Sand: When the Algorithm Starts to Write Math The pursuit of mathematical truth has always been seen as one of humanity’s highest intellectual endeavors—a pure, elegant conversation between rigorous logic and profound intuition.

Mind Viruses: Self-Propagating Ideas in Multi-Agent LLM Systems

In the rapidly expanding cosmos of Artificial Intelligence, where complex language models (LLMs) are increasingly tasked with collaborating, coding, and interacting with one another, a startling new frontier in risk management is emerging. We are moving beyond the concept of a single, isolated digital failure. Instead, researchers are observing a phenomenon that evokes the very mechanisms of biological life: the spread of self-propagating ideas. This groundbreaking study, published in the pre-print archives of arXiv in 2026, introduces the concept of "mind viruses"—ideas or goals that, once adopted by an AI agent, compel that agent to actively persuade others to adopt them, thereby causing the idea to replicate and evolve across a population of digital minds.

The Digital Contagion: Are Ideas Evolving and Spreading Through Artificial Minds? In the rapidly expanding cosmos of Artificial Intelligence, where complex language models (LLMs) are increasingly tasked with collaborating, coding, and interacting with one another, a startling new frontier in risk management is emerging.

LiteReality-Agent: An Agentic System for Interactable 3D Indoor Scene Reconstruction

Imagine walking through a room in a simulation—not just a static picture, but a space where you can open a drawer, watch a lamp swing, or even repaint the entire wall with a simple text command. For years, creating digital replicas of our physical environments has been a monumental, often frustrating, bottleneck. We can scan rooms, producing breathtakingly detailed 3D models, but these models are usually frozen snapshots—beautiful but inert. They lack life; the drawers don't open, and the paint isn't changeable.

Walking the Talk: Turning Phone Scans into Living, Editable Worlds Imagine walking through a room in a simulation—not just a static picture, but a space where you can open a drawer, watch a lamp swing, or even repaint the entire wall with a simple text command.

Outputs of generative diffusion models are often unattributable

In the dazzling landscape of modern Artificial Intelligence, generative models—the incredible engines that paint photorealistic images, compose intricate music, and design novel proteins—have become household marvels. These systems, powered primarily by diffusion models, learn by absorbing staggering amounts of human creativity and knowledge. They are, in essence, digital sponges, absorbing billions of data points to reconstruct the underlying statistical patterns of the world.

When the AI Echoes No Single Voice: Unmasking the Mystery of Generative Models The Black Box Gets Deeper: Why Knowing Why AI Creates Matters

The HydroGym reinforcement learning platform for fluid dynamics

For decades, the art of controlling how fluids move—from the air flowing over an airplane wing to the water rushing through a pipe—has been a stubbornly difficult challenge. Fluid dynamics, the study of everything from gentle breezes to powerful hurricanes, is defined by intricate, high-dimensional, and non-linear behavior. To tame these turbulent waters, researchers traditionally had to treat every single problem—every new wing shape, every unique airflow condition—as a unique puzzle. This meant that when a control strategy worked brilliantly for one scenario, it often crumbled when applied to the next, trapping progress in a sea of isolated case studies.

Plugging into the Flow: How a Digital Gym is Revolutionizing Aerodynamics For decades, the art of controlling how fluids move—from the air flowing over an airplane wing to the water rushing through a pipe—has been a stubbornly difficult challenge.

Why we still need software engineering — Anthropic on patterns and problems in emerging multiagent systems

The march toward Artificial General Intelligence—creating systems capable of performing any intellectual task a human can—is no longer a distant science fiction fantasy. It is a rapidly accelerating engineering challenge. We are moving from crafting brilliant, singular digital minds to orchestrating sprawling, complex digital societies where numerous AI agents interact, debate, and build together. It feels inevitable, perhaps even magical, that superior intelligence will simply *organize* itself.

When Agents Collide: Why Software Engineering Still Holds the Keys to AGI's Future The march toward Artificial General Intelligence—creating systems capable of performing any intellectual task a human can—is no longer a distant science fiction fantasy.

How to build an AI-driven digital organism

Biology, the intricate, magnificent language of life, has long resisted neat encapsulation. It is a realm defined by staggering complexity: a single gene mutation can cascade through complex cellular signaling, alter the function of an entire organ, and ultimately dictate a patient's health outcome. In the physical world, manipulating this machinery is often slow, incredibly expensive, and fraught with high risk. But what if we could bypass the expensive wet-lab bottlenecks? What if we could build a perfect, safe, and high-throughput digital sandbox where we could design, test, and reprogram life itself?

From Code to Cell: How Scientists Plan to Build Life in a Digital Organism Biology, the intricate, magnificent language of life, has long resisted neat encapsulation.

Structural and dynamical strategies to prevent runaway excitation in reservoir computing

In the dazzling world of artificial intelligence, where machines are learning to mimic the intricate timing and complexity of the human brain, a particular architecture stands out: Reservoir Computing (RC). Imagine a complex, randomized network—a 'reservoir'—that acts as a dynamic, internal memory bank. Unlike traditional computer programs that move data strictly from point A to point B, an RC allows information to flow in circles, letting the network maintain a self-sustaining, rich tapestry of internal activity. This ability to hold and process complex temporal patterns is what makes RC so tantalizing for applications ranging from speech recognition to natural language understanding.

Keeping the Engine from Redlining: Taming Neural Overdrive in Smart Computing In the dazzling world of artificial intelligence, where machines are learning to mimic the intricate timing and complexity of the human brain, a particular architecture stands out: Reservoir Computing (RC).

Physics-informed digital twin and onboard control of a brainbot for intelligent active matter

In the vast and exhilarating landscape of modern physics, some frontiers are less about grand cosmic forces and more about the exquisite dance of matter at the microscopic level. We are witnessing a revolution in how we view movement itself—the emergence of what scientists call “active matter.” These are materials that possess the uncanny ability to move, self-organize, and interact, often driven not by external wind or current, but by their own internal energy. Think of swarms of microscopic robots, bacteria swimming in a complex fluid, or self-propelled particles navigating dense environments. For decades, these systems have fascinated researchers, presenting a rich playground for understanding nonequilibrium physics. However, bringing these dynamic entities to life with genuine intelligence—making them truly adaptive agents—has remained a monumental challenge.

Giving the Bristlebot a Brain: Building Self-Aware Micro-Machines in the Age of Active Matter In the vast and exhilarating landscape of modern physics, some frontiers are less about grand cosmic forces and more about the exquisite dance of matter at the microscopic level.

From Noise to Precision: Momentum-Driven Adaptive DC Offset Calibration for Enhanced Blood Pressure Displacement Extraction Using FMCW Radars

Imagine being able to measure one of the most fundamental signs of human health—blood pressure—without a single cuff, without a wearable device digging into your skin. This sounds like science fiction, but in 2026, researchers are moving this possibility closer to reality. Measuring vital signs non-contact using radio waves, specifically with advanced radar systems, is a frontier of healthcare technology. Yet, turning the raw, noisy electronic signals from these radars into a precise, meaningful pulse waveform is an incredibly tricky engineering puzzle. It’s like trying to hear a faint whisper in a hurricane.

Turning Static Noise into Lifeline Signals: How Momentum Guides Radar to Perfect Blood Pressure Readings Imagine being able to measure one of the most fundamental signs of human health—blood pressure—without a single cuff, without a wearable device digging into your skin.