🤖 Google Claims the AI Reasoning Crown

👑 Google Claims the AI Reasoning Crown

🔬 OpenAI Derives a New Result in Theoretical Physics

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It previously relied on linear thinking, predicting the next logical step in a sequence of steps (e.g., “A→B→C”). Now, it’s capable of intelligently simulating a tree of potential solutions to evaluate which branches are most promising and prioritizing them. This major upgrade enabled it to claim the top spot on ARC-AGI-2 with 84.6% accuracy.

ARC-AGI-2 aims to directly address overfitting, which occurs when frontier AI models learn to memorize patterns rather than generalize knowledge. It achieves this by generating square grids whose colors and shapes change based on underlying patterns. The frontier AI models must decipher these underlying patterns to predict the correct changes.

Imagine teaching your dog to roll over, but only when you’re wearing a red shirt. He listens perfectly whenever you wear a red shirt, but he ignores you when you wear a blue shirt. That’s overfitting: Your dog learned to memorize basic patterns tied to your appearance (i.e., “red shirt = roll over!”) rather than generalize knowledge (i.e., “roll over whenever told!”).

In the “A→B→C” sequence of steps, AI essentially acts like a freight train of linear thought. If there’s a single logical misstep at “A,” “B,” or “C,” the entire reasoning process is derailed.

This major upgrade significantly strengthens the resiliency of the reasoning process and dramatically reduces the likelihood of AI confidently generating inaccurate, misleading, or incorrect information.

Atoms are the fundamental building blocks of all matter in the universe, typically containing a tiny nucleus composed of protons and neutrons. These protons and neutrons consist of elementary particles called Quarks, which are held together by “gluons”: the glue that makes the existence of stars, planets, and life possible.

In particle physics, scientists study Scattering Amplitudes: mathematical formulas that calculate the probability of particles interacting, colliding, and scattering. For a long time, physicists assumed that a specific type of interaction between “gluons” was impossible because the math became too messy to solve.

“GPT-5.2” identified a persistent pattern throughout that messy math, generating plausible Formal Proofs and verifying them using Lean 4. Formal Proofs are very strict, rigorous, and meticulously detailed mathematical arguments rooted in the basic building blocks of math. Lean 4 is a programming language designed for writing and verifying Formal Proofs. This process led to the discovery of a “half-collinear loophole,” or a special situation where particles are partly aligned to interact in ways previously thought impossible.

German-born theoretical physicist Albert Einstein’s most famous equation, E = mc², tells us that energy and mass are two sides of the same coin. In other words, mass is just a highly concentrated form of energy.

Imagine a box that’s vibrating so much it’s hard to pick up. The vibrations (i.e., “energy”) make the box feel heavy (i.e., “mass”). Einstein proved that vibrations were the weight. We knew “gluons” caused the vibrations. “GPT-5.2” discovered a new way they vibrate.

Uptiq raised a $25M Series B to add AI Agents to bank workflows.

Ever closed a $31M Series A for an AI-powered auto retail platform.

tem landed a $75M Series B for an AI-native energy transaction engine.

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