The number of planning applications for new homes in England is at its highest level for four years, new data shared with BBC Verify suggests. Applications for 335,000 homes outside London were lodged in 2025, up by 60% on 2024, according to Planning Portal, the service people use to request permission. But there are warnings that more needs to be done to meet Labour's target of building 1.5 million homes by 2029, as separate government data released on Thursday suggests there has been a decrease in house building. The Ministry of Housing, Communities and Local Government said it had overhauled the planning system and removed long-standing barriers that have held back housebuilding. The increase in planning applications for new homes in England follows controversial reforms introduced by Labour, which allow development on some lower-quality green belt land, known as grey belt .

The home secretary has announced a blueprint for reforming what she called the broken policing model in England and Wales. Shabana Mahmood confirmed the shake-up will create a new National Police Service (NPS) to fight the most complex cross-border crime and could also see the number of local forces in England and Wales cut by around two-thirds. She told the House of Commons she also intends to make better use of technology - including the largest-ever rollout of facial recognition. This government's reforms will ensure we have the right policing in the right place, Mahmood said. I set out reforms that are long overdue and define a new model for policing in this country, with local policing that protects our communities and national policing that protects us all.

The model is built to detect when crimes are being "contemplated." A US telecom company trained an AI model on years of inmates' phone and video calls and is now piloting that model to scan their calls, texts, and emails in the hope of predicting and preventing crimes. Securus Technologies president Kevin Elder told that the company began building its AI tools in 2023, using its massive database of recorded calls to train AI models to detect criminal activity. It created one model, for example, using seven years of calls made by inmates in the Texas prison system, but it has been working on building other state-or county-specific models. Over the past year, Elder says, Securus has been piloting the AI tools to monitor inmate conversations in real time (the company declined to specify where this is taking place, but its customers include jails holding people awaiting trial, prisons for those serving sentences, and Immigrations and Customs Enforcement detention facilities). "We can point that large language model at an entire treasure trove [of data]," Elder says, "to detect and understand when crimes are being thought about or contemplated, so that you're catching it much earlier in the cycle."

The reforms, which amend the AI Act and several other privacy and tech-related laws, would also cut back on website pop-ups asking permission to use cookies and reduce documentation requirements for small and medium-sized businesses. EU tech chief Henna Virkkunen said the changes, which need to be approved by representatives of the 27 EU member states, would boost European competitiveness by simplifying rules about AI, cybersecurity and data protection. "We have talent, infrastructure, a large internal single market. But our companies, especially our start-ups and small businesses, are often held back by layers of rigid rules," Virkkunen said. Lobby groups for tech giants in the United States, where President Donald Trump's administration has been a vocal critic of Europe's regulatory approach, welcomed the move, while lamenting that the measures did not go far enough.

As large language models increasingly gain popularity in real-world applications, processing extremely long contexts, often exceeding the model's pre-trained context limits, has emerged as a critical challenge. While existing approaches to efficient long-context processing show promise, recurrent compression-based methods struggle with information preservation, whereas random access approaches require substantial memory resources. We introduce REFORM, a novel inference framework that efficiently handles long contexts through a two-phase approach. First, it incrementally processes input chunks while maintaining a compressed KV cache, constructs cross-layer context embeddings, and utilizes early exit strategy for improved efficiency. Second, it identifies and gathers essential tokens via similarity matching and selectively recomputes the KV cache. Compared to baselines, REFORM achieves over 52% and 34% performance gains on RULER and BABILong respectively at 1M context length. It also outperforms baselines on Infinite-Bench, RepoEval, and MM-NIAH, demonstrating flexibility across diverse tasks and domains. Additionally, REFORM reduces inference time by 30% and peak memory usage by 5%, achieving both efficiency and superior performance.

This paper examines the role of public interest litigation in promoting accountability for AI and automated decision-making (ADM) in Australia. Since ADM regulation faces geopolitical headwinds, effective governance will have to rely at least in part on the enforcement of existing laws. Drawing on interviews with Australian public interest litigators, technology policy activists, and technology law scholars, the paper positions public interest litigation as part of a larger ecosystem for transparency, accountability and justice with respect to ADM. It builds on one participant's characterisation of litigation about ADM as an exercise in legal retrofitting: adapting old laws to new circumstances. The paper's primary contribution is to aggregate, organise and present original insights on pragmatic strategies and tactics for effective public interest litigation about ADM. Naturally, it also contends with the limits of these strategies, and of the Australian legal system. Where limits are, however, capable of being overcome, the paper presents findings on urgent needs: the enabling institutional arrangements without which effective litigation and accountability will falter. The paper is relevant to law and technology scholars; individuals and groups harmed by ADM; public interest litigators and technology lawyers; civil society and advocacy organisations; and policymakers.

Children will be taught how to budget and how mortgages work as the government seeks to modernise the national curriculum in England's schools. They will also be taught how to spot fake news and disinformation, including AI-generated content, following the first review of what is taught in schools in over a decade. Education Secretary Bridget Phillipson said the government wanted to revitalise the curriculum but keep a firm foundation in basics like English, maths and reading. Head teachers said the review's recommendations were sensible but would require sufficient funding and teachers. The government commissioned a review of the national curriculum and assessments in England last year, in the hope of developing a cutting edge curriculum that would narrow attainment gaps between the most disadvantaged students and their classmates.

Discrete-choice life cycle models of labor supply can be used to estimate how social security reforms influence employment rate. In a life cycle model, optimal employment choices during the life course of an individual must be solved. Mostly, life cycle models have been solved with dynamic programming, which is not feasible when the state space is large, as often is the case in a realistic life cycle model. Solving a complex life cycle model requires the use of approximate methods, such as reinforced learning algorithms. We compare how well a deep reinforced learning algorithm ACKTR and dynamic programming solve a relatively simple life cycle model. To analyze results, we use a selection of statistics and also compare the resulting optimal employment choices at various states. The statistics demonstrate that ACKTR yields almost as good results as dynamic programming. Qualitatively, dynamic programming yields more spiked aggregate employment profiles than ACKTR. The results obtained with ACKTR provide a good, yet not perfect, approximation to the results of dynamic programming. In addition to the baseline case, we analyze two social security reforms: (1) an increase of retirement age, and (2) universal basic income. Our results suggest that reinforced learning algorithms can be of significant value in developing social security reforms.

The Republican Plan to Reform the Census Could Put Everyone's Privacy at Risk A little-known algorithmic process called "differential privacy" helps keep census data anonymous. President Donald Trump and the Republican Party have spent the better part of the president's second term radically reshaping the federal government. But in recent weeks, the GOP has set its sights on taking another run at an old target: the US census. Since the first Trump administration, the right has sought to add a question to the census that captures a respondent's immigration status and to exclude noncitizens from the tallies that determine how seats in Congress are distributed. In 2019, the Supreme Court struck down an attempt by the first Trump administration to add a citizenship question to the census. But now, a little-known algorithmic process called "differential privacy," created to keep census data from being used to identify individual respondents, has become the right's latest focus.

President Trump sent shockwaves through the tech industry over the weekend by announcing a $100,000 payment for new employer-filed H-1B visa applications submitted after September 21, 2025. Since 1990, hundreds of thousands of foreigners have come to work for U.S. tech companies via the visa system. But in a proclamation, Trump wrote that the system had been "deliberately exploited to replace, rather than supplement, American workers with lower-paid, lower-skilled labor." Many experts agree that the H-1B system is flawed and needs amending. But TIME spoke with three professors in economics or business who believe that Trump's new fee system could be counterproductive: that it might push talent overseas; render universities and nonprofits unable to recruit foreign experts; and harm American tech innovation, including in the rapidly emerging field of AI.