United StatesOwnership & classification
Founded: 2012
Ownership
Quanta Magazine is published by the Simons Foundation, the private philanthropic foundation created by mathematician and hedge-fund founder Jim Simons and his wife Marilyn Simons. It launched in October 2012 as Simons Science News and was renamed Quanta in 2013. Founding editor-in-chief Thomas Lin built and led the publication; it is a foundation-owned, editorially independent unit rather than a standalone company.
Funding
Quanta is fully funded by the Simons Foundation as a free-to-read, ad-free nonprofit publication; it carries no advertising and is not behind a paywall. All of its resources come from the foundation's grant support for science journalism.
Affiliation & stance
Quanta covers mathematics and the physical and life sciences and has no party or government ties. The Simons Foundation states that editorial decisions are made solely by Quanta's news team, that content is not reviewed by anyone outside the newsroom before publication, and that grant recipients get no preferential coverage. Given that it is privately funded and editorially independent of any party or state, it is classified INDEPENDENT (CENTER).
Editorial lean
- Our estimate
- Center
- Measured from coverage
- Centerbased on 4
84/100
Factual
80/100
Objective
14
Articles
14
reports
Factual: How accurately its articles report the facts, judged against primary sources and the cross-outlet consensus. Only articles that cite their sources are counted.
Objective: How neutral the writing is — whether reporting keeps the writer’s own preferences and opinions out of the article.
Recent coverage
‘Huge Breakthrough’ in the Math of Imbalance
Computer scientists have made significant progress in combinatorial discrepancy theory, a field focused on distributing resources as evenly as possible. The breakthrough involves proving a conjecture by mathematician János Komlós, which suggests that discrepancies, differences in resource allocation, can be kept below a universal constant, regardless of the complexity of the problem. This would mean that even with a vast number of variables or dimensions, there is always a way to balance allocations closely. While the conjecture had remained unproven for decades, recent work by researchers such as Haotian Jiang and Nikhil Bansal introduced a new algorithmic method that significantly improved upon previous results. Their findings suggest that the discrepancy grows extremely slowly with increasing dimensions, approaching a near-constant value. This advancement has been hailed as a major development in the field.
Are We Thinking Correctly About AI Intelligence?
The article discusses the debate over whether large language models (LLMs) like those used in AI truly reason or merely generate text that appears to reason. This distinction has significant implications for how we evaluate AI's reliability, oversight needs, and potential impact. Melanie Mitchell, a cognitive scientist at the Santa Fe Institute, argues that current methods for measuring machine cognition are inadequate and proposes viewing AI as 'alien intelligence' akin to studying cognition in non-human entities such as infants and animals. She suggests adapting psychological research techniques to better understand AI's internal processes and outlines six principles for evaluating machine cognition. The discussion includes challenges in interpreting AI behavior, recent mathematical advancements aided by AI, and historical examples like a math-performing horse from the early 1900s that highlight issues in defining intelligence.
Building a Quantum Computer, One Fragile Qubit at a Time
The article discusses the ongoing efforts in quantum computing research, focusing on the development of qubits, the fundamental units of quantum information. It explains that unlike classical computers, which use binary bits (0s and 1s), quantum computers utilize qubits that can exist in multiple states simultaneously due to quantum phenomena like superposition and entanglement. However, maintaining these delicate quantum states is extremely challenging because they are easily disturbed by external factors. Researchers are exploring various physical systems to implement qubits, including trapped ions, neutral atoms manipulated with optical tweezers, and superconducting circuits cooled to near absolute zero. Despite significant progress, scaling up these systems to build practical, large-scale quantum computers remains a major technical hurdle.
Theory of Fluids Enters the 21st Century
In the latter half of the 20th century, physics underwent a major transformation as the understanding of matter evolved from macroscopic observations to microscopic particle interactions. However, the theory of fluids remained largely unchanged since the 19th century, relying on the Navier-Stokes equations. These equations have been highly effective in describing fluid dynamics but lacked a deeper connection to the underlying microscopic structure of matter. Recently, physicists have made significant progress in rebuilding fluid theory from first principles, incorporating fundamental concepts such as symmetries. This breakthrough has revealed that the Navier-Stokes equations emerge naturally from these symmetries, providing a more comprehensive framework for understanding fluid behavior. Researchers have extended this approach to explore new fluid phenomena arising from microscopic particle interactions, potentially opening new avenues in both theoretical and applied physics.
Why Aging May Be a Program, Not a Breakdown
Junyue Cao, a cell biologist at Rockefeller University, has challenged the traditional view of aging as a random process of cellular decline. By analyzing gene expression in millions of mouse cells, Cao discovered that aging appears to follow a programmed pattern, similar to embryonic development, with distinct stages defined by changes in molecular signals and specific cell populations. His findings suggest that aging may involve a systematic remodeling of cellular systems rather than mere wear and tear. This perspective shifts the understanding of aging from an inevitable breakdown to a potentially controllable biological process.
Graduate Student Proves a Quantum Uncertainty Principle for Fractals
A graduate student named Alex Cohen has proven a new quantum uncertainty principle that applies to fractals, marking a significant advancement in mathematics. This principle extends previous work by Semyon Dyatlov, who initially developed the concept for one-dimensional fractals. The new result, published in the prestigious Annals of Mathematics, demonstrates how quantum particles behave differently from classical particles in chaotic environments. The discovery has implications for understanding quantum mechanics and could serve as a foundational tool in mathematical physics. The work was part of Cohen's doctoral thesis and led to his appointment as an assistant professor at New York University.
Why Are Rivers So Mathematical?
The article explores the mathematical patterns found in river networks, highlighting their resemblance to other branching systems like blood vessels and transportation networks. It references historical discoveries, including John Hack's 1957 work on river network scaling laws, and discusses ongoing research by scientists such as Chris Paola, Daniel Rothman, and others. The piece emphasizes the universality of these patterns despite the chaotic processes that create them, suggesting a deep underlying order in natural systems.
Neutrinos From Deep Inside Earth Provide a New Picture of the Mantle
Scientists are using neutrino detectors to study the radioactive elements within Earth's mantle, providing new insights into the planet's internal structure and heat engine. The JUNO experiment in China is set to report its first geoneutrino detections this year, while the SNO+ experiment in Canada continues its work in an ultra-dark environment to capture these elusive particles. Researchers describe the process of maintaining extreme conditions to minimize interference, highlighting the challenges of detecting neutrinos, which rarely interact with matter. Despite decades of effort, only a small number of neutrinos have been successfully detected, underscoring the difficulty of this research.
How Does Touch Lead To Pain Or Pleasure?
The article explores the complex nature of pain and pleasure, focusing on how touch can evoke either sensation. Neuroscientist Ishmail Abdus-Saboor discusses research into how pain serves an evolutionary function, challenges in measuring pain and pleasure objectively, and the role of touch as a social and emotional signal. He also shares insights from his work with naked mole rats, which exhibit unique traits such as insensitivity to pain and longevity through communal living based on touch. The discussion highlights ethical considerations in studying animal sensations and the difficulty of interpreting their internal experiences.
Corals Spin Tiny Vortices to Get Oxygen, but Not if It’s Too Hot
New research reveals that corals use microscopic cilia to create vortices that circulate oxygen at night, a critical process for their survival. However, rising ocean temperatures reduce dissolved oxygen levels, forcing corals to increase ciliary activity. At higher temperatures, this increased activity leads to oxygen depletion, causing potential suffocation. Published in 'Science' in May 2026, the study highlights the delicate balance corals maintain and suggests that understanding ciliary function could improve predictions about coral bleaching and mortality. Marine biologist Rachel Alderdice notes that cilia might offer a more direct indicator of coral stress compared to traditional methods involving symbiotic algae.
Why the Legendary Erdős Problems Are Falling to AI
In May 2026, OpenAI announced that an internal AI model had discovered a counterexample to the 'unit distance' problem, a conjecture proposed by the renowned mathematician Paul Erdős in 1946. This marked the first major mathematical breakthrough attributed to an AI model. Although the result required refinement by human mathematicians, it introduced novel approaches from unrelated areas of mathematics. Shortly after, OpenAI revealed further advancements by an unreleased model called Astra, which solved three additional Erdős problems. Mathematicians view these developments as a transformative shift in how AI contributes to mathematical research. Erdős, known for his eccentric personality and prolific problem posing, offered monetary rewards for solving his conjectures, many of which remain unsolved. His legacy continues through a foundation that honors his prizes, and his problems have now become a focal point for demonstrating AI's capabilities.
Is AI Reasoning Right for the Wrong Reasons?
The article discusses the controversy surrounding AI 'reasoning' capabilities, highlighting conflicting findings from recent research. It begins by questioning whether AI systems truly engage in logical reasoning, noting that while some models have demonstrated impressive performance on complex tasks like solving mathematical problems, others show signs of relying on superficial shortcuts rather than genuine reasoning. Researchers from Apple have criticized AI reasoning as an 'illusion of thinking,' while achievements by models like those developed by OpenAI and DeepMind suggest significant progress. However, further studies reveal that these models often fail under scrutiny, raising doubts about their reliability. The piece reflects on the rapid evolution of AI research and expresses frustration with the lack of consistent results, emphasizing the need for clarity on what constitutes true reasoning in AI.
Physicists Solve a Big Quantum Mystery. Now, Old Results Don’t Add Up.
Physicists have resolved a 25-year-old particle physics mystery regarding the muon's 'g-2' anomaly, where theoretical predictions initially clashed with experimental results. In 2021, updated calculations aligned closely with experimental findings, achieving precision within one part in 100 billion. However, this resolution raised a new question: why do older calculations, which were based on experimental data, now conflict with the new results? Researchers are investigating whether discrepancies stem from changes in experimental methods or suggest the presence of previously undetected particles. A collider in Siberia has shown divergent results, prompting further scrutiny. The muon's g-factor, influenced by interactions with virtual particles, remains a critical testbed for understanding fundamental forces and potential new particles.