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Researchers have developed a new way to speed up quantum measurements, a vital building block for the next generation of quantum technologies.

Accurate and fast will be crucial for , but are fragile and susceptible to disturbance which can cause errors. Previous work in this area presented a fundamental challenge—scientists were only able to increase the accuracy of measurements in quantum systems by sacrificing speed.

A team of quantum experts, led by the University of Bristol, have struck upon a novel way to overcome this problem, published in a Physical Review Letters journal paper.

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IN A NUTSHELL 🔧 The United States has delivered a colossal superconducting magnet to France’s ITER project, advancing nuclear fusion technology. 🤝 Collaboration among eight American companies was essential to construct the solenoid’s support structure for the reactor. 🔄 Four out of six solenoid modules have been installed, with completion expected by the year’s end.

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Scientists discovered a new Hall effect driven by spin currents in noncollinear antiferromagnets, offering a path to more efficient and resilient spintronic devices.

A research team led by Colorado State University graduate student Luke Wernert and Associate Professor Hua Chen has identified a previously unknown type of Hall effect that could lead to more energy-efficient electronic devices.

Their study, published in Physical Review Letters.

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This paper introduces an adaptive multi-agent framework to enhance collaborative reasoning in large language models (LLMs). The authors address the challenge of effectively scaling collaboration and reasoning in multi-agent systems (MAS), which is an open question despite recent advances in test-time scaling (TTS) for single-agent performance.

The core methodology revolves around three key contributions:

1. **Dataset Construction:** The authors create a high-quality dataset, M500, comprising 500 multi-agent collaborative reasoning traces. This dataset is generated automatically using an open-source MAS framework (AgentVerse) and a strong reasoning model (DeepSeek-R1). To ensure quality, questions are selected based on difficulty, diversity, and interdisciplinarity. The generation process involves multiple agents with different roles collaborating to solve challenging problems. Data filtering steps are applied to ensure consensus among agents, adherence to specified formats (e.g., using tags like “ and ‘boxed{}‘), and correctness of the final answer. The filtering criteria are based on Consensus Reached, Format Compliance, and Correctness. The data generation is described in Algorithm 1 in the Appendix.

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Could a tiny dose of gold restore sight? Researchers at Brown University have developed a groundbreaking retinal prosthesis using gold nanoparticles and infrared light to bypass damaged photoreceptors in retinal disorders like macular degeneration.

This minimally invasive method successfully activated the visual system in mice, offering promising early evidence for future clinical applications. Learn how this innovative fusion of nanotechnology and neuroscience could revolutionize treatment for millions suffering from vision loss.

#vision #visionloss #neuroscience #science

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