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Category: robotics/AI – Page 7

Terence Tao Announces the 1st SAIR competition

We’ve collected 22 million algebra yes/no questions. Your task is to design a “cheat sheet”—a highly optimized prompt—that can be given to a weak, open-source model to drastically improve its accuracy.

On behalf of the SAIR Foundation, our co-founder Terence Tao is thrilled to announce our inaugural competition: the Mathematics Distillation Challenge.

Mathematics is about more than just finding the right answers; it’s about understanding the process. While frontier AI models can solve complex problems with 95% accuracy, weaker open-source models often perform no better than random chance (50%). We want to bridge that gap.

The Challenge:
We’ve collected 22 million algebra yes/no questions. Your task is to design a \.

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SoulMate LLM accelerator evolves according to the specific characteristics of the user

While large language models (LLMs) like ChatGPT are adept at answering countless questions, they often remain unaware of a user’s minor habits or previous conversational contexts. This is why AI, despite being deeply integrated into our daily lives, can still feel like a “stranger.” Overcoming these limitations, researchers at KAIST, led by Professor Hoi-Jun Yoo from the Graduate School of AI Semiconductors, have developed the world’s first AI semiconductor, dubbed “SoulMate,” which learns and adapts to a user’s speech style, preferences, and emotions in real-time—becoming a true “digital soulmate.”

This technology is being hailed as a core semiconductor breakthrough that will accelerate the era of “hyper-personalized AI”—moving beyond “AI for everyone” to an AI that learns and responds to an individual’s unique conversational style and preferences. The work is published in the proceedings of the 2026 IEEE International Solid-State Circuits Conference (ISSCC).

AI rebuilds molecules from exploding fragments

Researchers at the Department of Energy’s SLAC National Accelerator Laboratory and collaborating institutions recently built a generative AI model that can recreate molecular structures from the movement of the molecule’s ions after they are blasted apart by X-rays, a technique called Coulomb explosion imaging.

The research, published in Nature Communications, is an important step toward being able to take snapshots of molecules during chemical reactions—an advance that could have important impacts in medicine and industry. The machine learning model closely predicted the geometries of a range of different molecules made of less than ten atoms, paving the way for applying the technique to larger molecules.

“We were pretty excited about this,” said Xiang Li, an associate scientist at SLAC’s Linac Coherent Light Source (LCLS) and lead author of the study. “It is the first AI model built for molecular structure reconstruction from Coulomb explosion imaging.”

Building trust in the future of quantum computing

Quantum computers could solve certain problems that would take traditional classical computers an impractically long time to solve. At the Japan Advanced Institute of Science and Technology (JAIST), researchers are now working to make these systems reliable and trustworthy.

Unlike classical computers that process information in binary digits (bits) as either 0 or 1, quantum computers use quantum bits or “qubits” that can represent both 0 and 1 simultaneously, enabling dramatic speedups in computations for specific problems.

The potential applications of quantum computing are wide-ranging. These include factoring large numbers that could break today’s encryption, optimizing complex industrial processes, accelerating drug discovery, and supporting advances in artificial intelligence (AI).

Moshe Vardi Named 2026 NAAI Academy Award Laureate

Congratulations, Moshe Vardi!

Moshe Y. Vardi, University Professor at Rice University, has been named a 2026 NAAI Academy Award laureate by the National Academy of Artificial Intelligence (NAAI). The award is the Academy’s highest honor and recognizes scientists whose research has fundamentally advanced the scientific foundations of artificial intelligence.

Vardi received the award for seminal contributions to logic-based artificial intelligence and formal reasoning in intelligent systems. His work has significantly advanced the logical foundations that underpin modern AI research, particularly in areas such as formal reasoning, verification and logic in computer science.

The 2026 NAAI Academy Award recognizes three international leaders whose work has shaped key theoretical pillars of modern artificial intelligence.

New 4D vision chip can help robots track distance and speed at once

Researchers at Pointcloud GmbH in Zürich, Switzerland, have packed advanced 4D sensing technology — once too bulky for everyday use — onto a single silicon chip.

It’s a 4D imaging sensor that maps the physical world while simultaneously clocking the speed of every object it sees. It offers a low-cost, high-speed vision solution for everything from autonomous drones to future smartphones.

“This result demonstrates the capabilities of FMCW LiDAR FPA sensors as enablers of ubiquitous, low-cost, compact coherent 4D imaging cameras,” the researchers wrote in the study paper.

TerraLingua: Emergence and Open-Ended Dynamics in LLM Ecologies

Unlike previous AI simulations where agents existed in consequence-free bubbles, TerraLingua operates more like a real ecosystem. Agents have limited resources and finite lifespans. When an agent “dies,” it’s gone—but here’s the twist: anything it created “survives.” A tool, a rule, a piece of knowledge—these artifacts live on, shaping how future generations of agents behave and interact.

Introducing TerraLingua, a multi-agent LLM ecology that shows how AI agents interact, cooperate, and build shared culture over time in a persistent environment.

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