Feedback has spent some time sifting through 2025's key scientific achievements to come up with a range of weird and wonderful (and less wonderful) winners for our inaugural Backsies awards Being a New Scientist reader, you are probably savvy enough to realise that end-of-year roundups are written weeks ahead of time. This particular summation was drafted on 1 December, just as Feedback was preparing to spend 24 days avoiding hearing Wham's Last Christmas and trying to persuade Feedback Jr to make their mind up on what they want for their main present. Anything radically silly that may have happened after that date will have to wait until next year. Truly, 2025 has been rich in all the things Feedback is interested in. We learned about fascinating proposals like nuking the seabed to stop climate change, a notion that went straight into our Do Not Recommend pile.

This mostly includes tasks with multifaceted objectives, such as dialogue response generation, or tasks with hard-to-define goals, such as enhancing program readability.

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Some things are sadly inevitable: death, taxes, another Coldplay album. One such inevitability, long since proved beyond any reasonable doubt, is that if you try to suppress an embarrassing story, you will only draw more attention to it. This phenomenon is called the Streisand Effect, after an incident in 2003 when Barbra Streisand sued to have an aerial photograph taken off the internet.

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Feedback reads a lot of academic articles, and we are often distressed by their titles, which can be not so much meandering and unclear as digressive and circumlocutory. The only things worse are the ones that preface the academese with an allegedly humorous pop culture reference. However, sometimes we run across research whose title is brisk and to the point.

Feedback is New Scientist's popular sideways look at the latest science and technology news. You can submit items you believe may amuse readers to Feedback by emailing feedback@newscientist.com Feedback is as fond of true crime as the next morbidly curious ghoul, so we have occasionally dipped our toes into the never-ending well of speculation about the Whitechapel murders of 1888-91 and the near-mythical Jack the Ripper. Although frankly, we didn't get much further than Alan Moore and Eddie Campbell's From Hell, which (spoiler!) ties the killings to the British establishment and the Freemasons, who supposedly arranged the murders to create an evil psychic force that would perpetuate the patriarchy. But the field of "Ripperology" extends far beyond one eccentric graphic novel.

Vision-language models (VLMs) have achieved remarkable success in scene understanding and perception tasks, enabling robots to plan and execute actions adaptively in dynamic environments. However, most multimodal large language models lack robust 3D scene localization capabilities, limiting their effectiveness in fine-grained robotic operations. Additionally, challenges such as low recognition accuracy, inefficiency, poor transferability, and reliability hinder their use in precision tasks. To address these limitations, we propose a novel framework that integrates a 2D prompt synthesis module by mapping 2D images to point clouds, and incorporates a small language model (SLM) for supervising VLM outputs. The 2D prompt synthesis module enables VLMs, trained on 2D images and text, to autonomously extract precise 3D spatial information without manual intervention, significantly enhancing 3D scene understanding. Meanwhile, the SLM supervises VLM outputs, mitigating hallucinations and ensuring reliable, executable robotic control code generation. Our framework eliminates the need for retraining in new environments, thereby improving cost efficiency and operational robustness. Experimental results that the proposed framework achieved a 96.0\% Task Success Rate (TSR), outperforming other methods. Ablation studies demonstrated the critical role of both the 2D prompt synthesis module and the output supervision module (which, when removed, caused a 67\% TSR drop). These findings validate the framework's effectiveness in improving 3D recognition, task planning, and robotic task execution.

Materials discovery and design are essential for advancing technology across various industries by enabling the development of application-specific materials. Recent research has leveraged Large Language Models (LLMs) to accelerate this process. We explore the potential of LLMs to generate viable hypotheses that, once validated, can expedite materials discovery. Collaborating with materials science experts, we curated a novel dataset from recent journal publications, featuring real-world goals, constraints, and methods for designing real-world applications. Using this dataset, we test LLM-based agents that generate hypotheses for achieving given goals under specific constraints. To assess the relevance and quality of these hypotheses, we propose a novel scalable evaluation metric that emulates the process a materials scientist would use to evaluate a hypothesis critically. Our curated dataset, proposed method, and evaluation framework aim to advance future research in accelerating materials discovery and design with LLMs.