The protection of cyber Intellectual Property (IP) such as web content is an increasingly critical concern. The rise of large language models (LLMs) with online retrieval capabilities enables convenient access to information but often undermines the rights of original content creators. As users increasingly rely on LLM-generated responses, they gradually diminish direct engagement with original information sources, which will significantly reduce the incentives for IP creators to contribute, and lead to a saturating cyberspace with more AI-generated content. In response, we propose a novel defense framework that empowers web content creators to safeguard their web-based IP from unauthorized LLM real-time extraction and redistribution by leveraging the semantic understanding capability of LLMs themselves. Our method follows principled motivations and effectively addresses an intractable black-box optimization problem. Real-world experiments demonstrated that our methods improve defense success rates from 2.5% to 88.6% on different LLMs, outperforming traditional defenses such as configuration-based restrictions.

This will not be ready before the next king's speech, and is likely to trigger concerns about delays to regulating the technology. The date for the next king's speech has not been set but several sources said it could take place in May 2026. Labour had originally planned to introduce a short, narrowly drafted AI bill within months of entering office that would have been focused on large language models, such as ChatGPT. The legislation would have required companies to hand over their models for testing by the UK's AI Security Institute. It was intended to address concerns that AI models could become so advanced that they posed a risk to humanity.

Three residents filed a lawsuit this week against Sonoma County seeking to block code enforcement from using drones to take aerial images of their homes in what the American Civil Liberties Union is calling a "runaway spying operation." The lawsuit, filed by the ACLU Wednesday on behalf of the three residents, alleges that the county began using drones with high-powered cameras and zoom lenses in 2019 to track illegal cannabis cultivation, but in the years since, officials have used the devices more than 700 times to find other code violations on private property without first seeking a warrant. "For too long, Sonoma County code enforcement has used high-powered drones to warrantlessly sift through people's private affairs and initiate charges that upend lives and livelihoods. All the while, the county has hidden these unlawful searches from the people they have spied on, the community, and the media," Matt Cagle, a senior staff attorney with the ACLU Foundation of Northern California, said in a statement. A spokesperson for Sonoma County said the county is reviewing the complaint and takes "the allegations very seriously."

They rumble down the highway between Beijing and Tianjin port: big lorries, loaded up and fully able to navigate themselves. Sure, there is a safety driver in the seat, as per government regulations, but these lorries don't require them, and many analysts say it won't take long before they are gone. When "safety driver" Huo Kangtian, 32, first takes his hands off the wheel, and lets the lorry drive itself, it is somehow impressive and disconcerting in equal measures. For the initial stages of the journey, he is in full control. Then - at a certain point - he hits a few buttons, and the powerful, heavy machine is driving itself, moving at speed along a public road to Tianjin.

The high court has told senior lawyers to take urgent action to prevent the misuse of artificial intelligence after dozens of fake case-law citations were put before the courts that were either completely fictitious or contained made-up passages. Lawyers are increasingly using AI systems to help them build legal arguments, but two cases this year were blighted by made-up case-law citations that were either definitely or suspected to have been generated by AI. In a 89m damages case against the Qatar National Bank, the claimants made 45 case-law citations, 18 of which turned out to be fictitious, with quotes in many of the others also bogus. The claimant admitted using publicly available AI tools and his solicitor accepted he cited the sham authorities. When Haringey Law Centre challenged the London borough of Haringey over its alleged failure to provide its client with temporary accommodation, its lawyer cited phantom case law five times.

Scientists have uncovered hidden patterns in the Bible that challenge ancient beliefs about its origins. Using artificial intelligence, they discovered'fingerprints' in text throughout the Old Testament, suggesting multiple people wrote the stories. The traditional Jewish and Christian understanding is that Moses wrote the first five books of the Old Testament, including stories about creation, Noah's flood and the Ark of the Covenant. The new study found three distinct writing styles with distinct vocabulary, tone and focus areas, suggesting multiple authors and sources contributed to the books over time. Researchers used AI analyzed for 50 chapters across five books, uncovering inconsistencies in language and content, repeated stories, shifts in tone and internal contradictions.

Just as AI begins to upend American society, Congress is considering a move that would sideline states from enforcing commonsense safeguards. Tucked into the recently passed House reconciliation package is Section 43201, a provision that would pre-empt nearly all state and local laws governing "artificial intelligence models," "artificial intelligence systems," and "automated decision systems" for the next 10 years. Last night, the Senate released its own version of the moratorium that would restrict states from receiving federal funding for broadband infrastructure if they don't fall in line. Supporters argue that a moratorium is needed to avoid a patchwork of state rules that could jeopardize U.S. AI competitiveness. AI'S DEVELOPMENT IS CRITICALLY IMPORTANT FOR AMERICA – AND IT ALL HINGES ON THESE FREEDOMS But this sweeping approach threatens to override legitimate state efforts to curb Big Tech's worst abuses--with no federal safeguards to replace them. It also risks undermining the constitutional role of state legislatures to protect the interests and rights of American children and working families amid AI's far-reaching social and economic disruptions.

Existing studies of innovation emphasize the power of social structures to shape innovation capacity. Emerging machine learning approaches, however, enable us to model innovators' personal perspectives and interpersonal innovation opportunities as a function of their prior trajectories of experience. We theorize then quantify subjective perspectives and innovation opportunities based on innovator positions within the geometric space of concepts inscribed by dynamic language representations. Using data on millions of scientists, inventors, writers, entrepreneurs, and Wikipedia contributors across the creative domains of science, technology, film, entrepreneurship, and Wikipedia, here we show that measured subjective perspectives anticipate what ideas individuals and groups creatively attend to and successfully combine in future. When perspective and background diversity are decomposed as the angular difference between collaborators' perspectives on their creation and between their experiences, the former consistently anticipates creative achievement while the latter portends its opposite, across all cases and time periods examined. We analyze a natural experiment and simulate creative collaborations between AI (large language model) agents designed with various perspective and background diversity, which are consistent with our observational findings. We explore mechanisms underlying these findings and identify how successful collaborators leverage common language to weave together diverse experience obtained through trajectories of prior work that converge to provoke one another and innovate. We explore the importance of these findings for team assembly and research policy.

This paper introduces Energentic Intelligence, a class of autonomous systems defined not by task performance, but by their capacity to sustain themselves through internal energy regulation. Departing from conventional reward-driven paradigms, these agents treat survival-maintaining functional operation under fluctuating energetic and thermal conditions-as the central objective. We formalize this principle through an energy-based utility function and a viability-constrained survival horizon, and propose a modular architecture that integrates energy harvesting, thermal regulation, and adaptive computation into a closed-loop control system. A simulated environment demonstrates the emergence of stable, resource-aware behavior without external supervision. Together, these contributions provide a theoretical and architectural foundation for deploying autonomous agents in resource-volatile settings where persistence must be self-regulated and infrastructure cannot be assumed.

Large language models (LLMs) hold promise for sustainable manufacturing, but often hallucinate industrial codes and emission factors, undermining regulatory and investment decisions. We introduce CircuGraphRAG, a retrieval-augmented generation (RAG) framework that grounds LLMs outputs in a domain-specific knowledge graph for the circular economy. This graph connects 117,380 industrial and waste entities with classification codes and GWP100 emission data, enabling structured multi-hop reasoning. Natural language queries are translated into SPARQL and verified subgraphs are retrieved to ensure accuracy and traceability. Compared with Standalone LLMs and Naive RAG, CircuGraphRAG achieves superior performance in single-hop and multi-hop question answering, with ROUGE-L F1 scores up to 1.0, while baseline scores below 0.08. It also improves efficiency, halving the response time and reducing token usage by 16% in representative tasks. CircuGraphRAG provides fact-checked, regulatory-ready support for circular economy planning, advancing reliable, low-carbon resource decision making.