Deliberative democracy depends on carefully designed institutional frameworks -- such as participant selection, facilitation methods, and decision - making mechanisms -- that shape how deliberation performs . However, identifying optimal institutional designs for specific contexts remains challenging when relying solely on real - world observations or laboratory experiments: they can be expensive, ethically and methodologically tricky, or too limited in scale to give us clear answers . Computational experiments offer a complementary approach, enabling researchers to conduct large - scale investigations while systematically analyzing complex dynamics, emergent and unexpected collective behavior, and risks or opportunities associated with novel democratic designs . Therefore, this paper explores Digital Twin (DT) technology as a computational testing ground for deliberative systems (with potential applicability to broader institutional analysis) . By constructing dynamic models that simulate real - world deliberation, DTs allow researchers and policymakers to rigorously test "what - if" scenarios across diverse institutional configurations in a controlled virtual environment. This approach facilitates evidence - based assessment of novel designs using synthetically generated data, bypassing the constraints of real - world or lab - based experimentation, and without societal disruption. The paper also discusses the limitations of this new methodological approach and suggest s where future research should focus .

Responsible AI (rAI) guidance increasingly promotes stakeholder involvement (SHI) during AI development. At the same time, SHI is already common in commercial software development, but with potentially different foci. This study clarifies the extent to which established SHI practices are able to contribute to rAI efforts as well as potential disconnects -- essential insights to inform and tailor future interventions that further shift industry practice towards rAI efforts. First, we analysed 56 rAI guidance documents to identify why SHI is recommended (i.e. its expected benefits for rAI) and uncovered goals such as redistributing power, improving socio-technical understandings, anticipating risks, and enhancing public oversight. To understand why and how SHI is currently practised in commercial settings, we then conducted an online survey (n=130) and semi-structured interviews (n=10) with AI practitioners. Our findings reveal that SHI in practice is primarily driven by commercial priorities (e.g. customer value, compliance) and several factors currently discourage more rAI-aligned SHI practices. This suggests that established SHI practices are largely not contributing to rAI efforts. To address this disconnect, we propose interventions and research opportunities to advance rAI development in practice.

Large Language Models (LLMs) have shown strong inductive reasoning ability across various domains, but their reliability is hindered by the outdated knowledge and hallucinations. Retrieval-Augmented Generation mitigates these issues by grounding LLMs with external knowledge; however, most existing RAG pipelines rely on unstructured text, limiting interpretability and structured reasoning. Knowledge graphs, which represent facts as relational triples, offer a more structured and compact alternative. Recent studies have explored integrating knowledge graphs with LLMs for knowledge graph question answering (KGQA), with a significant proportion adopting the retrieve-then-reasoning paradigm. In this framework, graph-based retrievers have demonstrated strong empirical performance, yet they still face challenges in generalization ability. In this work, we propose RAPL, a novel framework for efficient and effective graph retrieval in KGQA. RAPL addresses these limitations through three aspects: (1) a two-stage labeling strategy that combines heuristic signals with parametric models to provide causally grounded supervision; (2) a model-agnostic graph transformation approach to capture both intra- and inter-triple interactions, thereby enhancing representational capacity; and (3) a path-based reasoning strategy that facilitates learning from the injected rational knowledge, and supports downstream reasoner through structured inputs. Empirically, RAPL outperforms state-of-the-art methods by $2.66\%-20.34\%$, and significantly reduces the performance gap between smaller and more powerful LLM-based reasoners, as well as the gap under cross-dataset settings, highlighting its superior retrieval capability and generalizability. Codes are available at: https://github.com/tianyao-aka/RAPL.

Obtaining multiple meaningfully diverse, high quality samples from Large Language Models for a fixed prompt remains an open challenge. Current methods for increasing diversity often only operate at the token-level, paraphrasing the same response. This is problematic because it leads to poor exploration on reasoning problems and to unengaging, repetitive conversational agents. To address this we propose Intent Factored Generation (IFG), factorising the sampling process into two stages. First, we sample a semantically dense intent, e.g., a summary or keywords. Second, we sample the final response conditioning on both the original prompt and the intent from the first stage. This allows us to use a higher temperature during the intent step to promote conceptual diversity, and a lower temperature during the final generation to ensure the outputs are coherent and self-consistent. Additionally, we find that prompting the model to explicitly state its intent for each step of the chain-of-thought before generating the step is beneficial for reasoning tasks. We demonstrate our method's effectiveness across a diverse set of tasks. We show this method improves both pass@k and Reinforcement Learning from Verifier Feedback on maths and code tasks. For instruction-tuning, we combine IFG with Direct Preference Optimisation to increase conversational diversity without sacrificing reward. Finally, we achieve higher diversity while maintaining the quality of generations on a general language modelling task, using a new dataset of reader comments and news articles that we collect and open-source. In summary, we present a simple method of increasing the sample diversity of LLMs while maintaining performance. This method can be implemented by changing the prompt and varying the temperature during generation, making it easy to integrate into many algorithms for gains across various applications.

How the case gets resolved could have major implications for both AI and Hollywood going forward. "I really think the only thing that can stop AI companies doing what they're doing is the law," says Ed Newton-Rex, the CEO of nonprofit organization Fairly Trained, which provides certifications for AI models trained on licensed data. "If these lawsuits are successful, that is what will hopefully stop AI companies from exploiting people's life's work." AI companies train their models upon vast amounts of data scoured from across the web. Midjourney, which allows its millions of registered users to generate images from prompts, faces a class-action suit led by artists including Kelly McKernan, who found that users were inputting the artist's name as a keyword in Midjourney to spit out eerily similar artworks.

Midjourney's tool, which creates images from text prompts, has 20 million users on its Discord server, where users type their inputs. In the lawsuit, the two movie-making giants share examples in which Midjourney is able to create images that uncannily resemble characters each company owns the rights to, such as the Minions, controlled by Universal, or the Lion King, owned by Disney. They also say Midjourney "ignored" their attempts to remediate the issue prior to taking legal action. Midjourney did not immediately respond to New Scientist's request for comment. The lawsuit has been welcomed by Ed Newton-Rex at Fairly Trained, a non-profit organisation that promotes fairer training practices for AI companies.

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 Ophthalmologist Gus Gazzard writes in after taking a close look at a marketing email he received from WildBounds. It advertised a revolutionary new range of tents from Colorado-based company Big Agnes, which has created a new kind of waterproofing called HyperBead. Marketing is often detached from reality, but one sentence stood out: "Waterproof at the molecular level, this proprietary material shrugs off rain without relying on coatings or chemicals, meaning no reproofing and no PFAS."

Disney and Universal have filed a lawsuit against Midjourney, alleging that the San Francisco–based AI image generation startup is a "bottomless pit of plagiarism" that generates "endless unauthorized copies" of the studios' work. The complaint includes dozens of images that purportedly demonstrate how Midjourney can conjure images featuring the studios' intellectual property. One image depicts Yoda from Star Wars holding a light saber, which it says was made by inputting the prompt "Yoda with lightsaber, IMAX." Another shows that typing "The Boss Baby" as a prompt allegedly resulted in an image of an animated child in a tuxedo closely resembling the protagonist of Universal's The Boss Baby franchise. "This is an extremely significant development," says IP lawyer Chad Hummel, who sees the compilation of images in the complaint as compelling evidence that "the output is not sufficiently transformative."

Brian Wilson, the musical savant who scripted a defining Southern California soundtrack with a run of hit songs with the Beach Boys before being pulled down a rabbit hole of despair and depression when his highly anticipated masterwork was shelved unfinished, has died. Wilson's family announced his death Wednesday morning on Facebook. "We are at a loss for words right now," the post said. "Please respect our privacy at this time as our family is grieving. We realize we are sharing our grief with the world," said the statement, also shared on Instagram and the musician's website. The statement didn't reveal a cause of death. Wilson died more than a year after it was revealed he was diagnosed with dementia and placed under a conservatorship in May 2024.

In their lawsuit, the entertainment giants called Midjourney's popular AI-powered image generator a "bottomless pit of plagiarism" for its alleged reproductions of the studios' best-known characters. The suit, filed in federal court in Los Angeles, claims Midjourney pirated the libraries of the two Hollywood studios, making and distributing without permission "innumerable" copies of their marquee characters such as Darth Vader from Star Wars, Elsa from Frozen, and the Minions from Despicable Me. Midjourney did not immediately respond to a request for comment. Horacio Gutierrez, Disney's chief legal officer, said in a statement: "We are bullish on the promise of AI technology and optimistic about how it can be used responsibly as a tool to further human creativity, but piracy is piracy, and the fact that it's done by an AI company does not make it any less infringing." NBCUniversal's executive vice-president and general counsel, Kim Harris, said the company was suing to "protect the hard work of all the artists whose work entertains and inspires us and the significant investment we make in our content". Instead, the studios argue, Midjourney continued to release new versions of its AI image service that boast higher-quality infringing images.