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In the new study, however, these shapes appeared in calculations describing the energy radiated as gravitational waves when two black holes cruised past one another. This marks the first time they’ve appeared in a context that could, in principle, be tested through real-world experiments.

Mogull likens their emergence to switching from a magnifying glass to a microscope, revealing features and patterns previously undetectable. “The appearance of such structures sheds new light on the sorts of mathematical objects that nature is built from,” he said.

These findings are expected to significantly enhance future theoretical models that aim to predict gravitational wave signatures. Such improvements will be crucial as next-generation gravitational wave detectors — including the planned Laser Interferometer Space Antenna (LISA) and the Einstein Telescope in Europe — come online in the years ahead.

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A team of AI researchers at the Alibaba Group’s Tongyi Lab, has debuted a new approach to training LLMs; one that costs much less than those now currently in use. Their paper is posted on the arXiv preprint server.

As LLMs such as ChatGPT have become mainstream, the resources and associated of running them have skyrocketed, forcing AI makers to look for ways to get the same or better results using other techniques. To this end, the team working at the Tongyi Lab has found a way to train LLMs in a new way that uses far fewer resources.

The idea behind ZeroSearch is to no longer use API calls to search engines to amass search results as a way to train an LLM. Their method instead uses simulated AI-generated documents to mimic the output from traditional search engines, such as Google.

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White light-emitting diodes (LEDs), the semiconductor devices underpinning the functioning of countless lighting technologies on the market today, were first released to the public in 1996. Following their commercial debut, these devices have fueled significant advancements within the electronics and lighting industry, due to their remarkable energy efficiencies and extended lifespans.

Researchers at the University of Cambridge and ETH Zurich recently carried out a study aimed at re-tracing the development of white LEDs over the past three decades, as well as trends in their costs and innovations in other engineering fields that fueled their advancement. Their paper, published in Nature Energy, was part of a larger research project that investigated the factors driving innovation in the clean energy sector.

“As part of our research, we looked at three key technologies at the forefront of the ongoing energy transition: solar photovoltaics for , lithium-ion batteries for , and white LEDs for efficient energy use in lighting,” Michael P. Weinold, first author of the paper, told Tech Xplore.

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Tin-halide perovskites, a class of tin-based materials with a characteristic crystal structure that resembles that of the compound calcium titanate, could be promising alternatives to commonly used semiconductors. Past studies have explored the possibility of using these materials to fabricate p-channel thin-film transistors (TFTs), devices used to control and amplify the flow of charge carriers in electronics devices.

So far, however, the reliable fabrication and integration of thin-film perovskites into commercially available electronics has proved challenging. This is in part due to difficulties encountered when trying to produce uniform perovskite films with consistent electronic properties using scalable and industry-compatible methods.

Researchers at Pohang University of Science and Technology recently introduced a new promising strategy for the fabrication of highly performing TFTs based on tin-halide perovskites. Their approach, outlined in a paper published in Nature Electronics, relies on thermal evaporation and the use of lead chloride (PbCl2) as a reaction initiator.

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Researchers at the University of Sydney have successfully performed a quantum simulation of chemical dynamics with real molecules for the first time, marking a significant milestone in the application of quantum computing to chemistry and medicine.

Understanding in real time how atoms interact to form new compounds or interact with light has long been expected as a potential application of quantum technology. Now, quantum chemist Professor Ivan Kassal and Physics Horizon Fellow Dr. Tingrei Tan have shown it is possible using a quantum machine at the University of Sydney.

The innovative work leverages a novel, highly resource-efficient encoding scheme implemented on a trapped-ion quantum computer in the University of Sydney Nanoscience Hub, with implications that could help transform medicine, energy and materials science.

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In a study published in the Proceedings of the National Academy of Sciences (PNAS), the researchers detail their discoveries about why the brain tumor glioblastoma is so aggressive. Their findings center on ZIP4, a protein that transports zinc throughout the body and sets off a cascade of events that drive tumor growth.

About half of all malignant brain tumors are glioblastomas, the deadliest form of brain cancer with a median survival rate of 14 months.

“Surgery for glioblastoma is very challenging, and patients almost always experience a relapse,” said the study’s senior author. “By better understanding why these brain tumors are so aggressive, we hope to open up paths for new treatments.”

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Is Gemini 2.5 Pro the AI breakthrough that will redefine machine intelligence? Google’s latest innovation promises to solve one of AI’s biggest hurdles: true reasoning. Unlike chatbots that regurgitate data, Gemini 2.5 Pro mimics human-like logic, connecting concepts, spotting flaws, and making decisions with unprecedented depth. This isn’t an upgrade—it’s a revolution in how machines think.

What makes Gemini 2.5 Pro unique? Built on a hybrid neural-symbolic architecture, it merges brute-force data processing with structured reasoning frameworks. Early tests show it outperforms GPT-4 and Claude 3 in complex tasks like legal analysis, medical diagnostics, and ethical dilemma navigation. We’ll break down its secret sauce: adaptive learning loops, context-aware problem-solving, and self-correcting logic that learns from mistakes in real time.

How will this impact you? Developers can build AI that understands instead of just parroting, businesses can automate high-stakes decisions, and educators might finally have a tool to teach critical thinking. But there’s a catch: Gemini 2.5 Pro’s \.

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers.

Guide for using DeepSeek on Lambda:
https://docs.lambdalabs.com/education/large-language-models/…dium=video.

📝 AlphaEvolve: https://deepmind.google/discover/blog/alphaevolve-a-gemini-p…lgorithms/
📝 My genetic algorithm for the Mona Lisa: https://users.cg.tuwien.ac.at/zsolnai/gfx/mona_lisa_parallel_genetic_algorithm/

📝 My paper on simulations that look almost like reality is available for free here:
https://rdcu.be/cWPfD

Or this is the orig. Nature Physics link with clickable citations:
https://www.nature.com/articles/s41567-022-01788-5

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Continue reading “DeepMind’s AlphaEvolve AI: History In The Making!” | >