The 81st United Nations General Assembly (UNGA) concluded on Monday with artificial intelligence (AI), the Israel-Palestine conflict and the US-Israel war on Iran dominating world leaders' addresses during the general debate. UN Secretary-General Antonio Guterres, in his final address before his term ends, warned that the future of the world would be shaped by four tests: AI, climate change, wars and inequality. That is why the UN Charter - and the work of the United Nations - matter more than ever," Guterres said. Speeches ranged from calls to reform the UN to appeals for upholding international law, as the global body has been undermined by the unilateral foreign policy pushed by United States President Donald Trump. Here are the biggest takeaways from this year's summit: The situation in Palestine was a key issue among members amid Israel's ongoing genocidal war on Gaza despite the so-called "ceasefire" and escalating settler pogroms in the occupied West Bank.

In this summary article, organisers of a tutorial and a workshop at IJCAI-ECAI 2026 pick their key takeaways from their respective sessions. This tutorial was a practical, hands-on tutorial on the theory and methods for handling missing data in tabular and imaging settings, from statistical baselines to autoencoders and generative adversarial networks. The missingness mechanism is more important than the choice of imputation method. MCAR, MAR, and MNAR settings call for different treatment, and MNAR, the most challenging mechanism, is the one that most methods do not handle well. Deep generative imputation is not always better.

Trump-Xi meeting: Who has the upper hand? Could Trump go for a third term? Is the US eyeing its next Latin American target? Why is Trump tearing down parts of the White House? United States President Donald Trump and his Chinese counterpart Xi Jinping have agreed to a trade truce under which the US will ease tariffs and Beijing will restart imports of US soya beans, delay the introduction of export restrictions on some of its rare earth metals and intensify efforts to curb illegal fentanyl trafficking.

Standard training metrics like loss fail to explain the emergence of complex capabilities in large language models. We take a spectral approach to investigate the geometry of learned representations across pretraining and post-training, measuring effective rank (RankMe) and eigenspectrum decay ($α$-ReQ). With OLMo (1B-7B) and Pythia (160M-12B) models, we uncover a consistent non-monotonic sequence of three geometric phases during autoregressive pretraining. The initial "warmup" phase exhibits rapid representational collapse. This is followed by an "entropy-seeking" phase, where the manifold's dimensionality expands substantially, coinciding with peak n-gram memorization. Subsequently, a "compression-seeking" phase imposes anisotropic consolidation, selectively preserving variance along dominant eigendirections while contracting others, a transition marked with significant improvement in downstream task performance. We show these phases can emerge from a fundamental interplay of cross-entropy optimization under skewed token frequencies and representational bottlenecks ($d \ll |V|$). Post-training further transforms geometry: SFT and DPO drive "entropy-seeking" dynamics to integrate specific instructional or preferential data, improving in-distribution performance while degrading out-of-distribution robustness. Conversely, RLVR induces "compression-seeking", enhancing reward alignment but reducing generation diversity.

A shifting graduate labour market is not unusual, said Kirsten Barnes, head of digital platform at Bright Network, which connects graduates and young professionals to employers. "Any shifts in the graduate job market this year – which typically fluctuates by 10-15% – appear to be driven by a combination of factors, including wider economic conditions and the usual fluctuations in business demand, rather than a direct impact from AI alone. We're not seeing a consistent trend across specific sectors," she said. Claire Tyler, head of insights at the Institute for Student Employers (ISE), which represents major graduate employers, said that among companies recruiting fewer graduates "none of them have said it's down to AI". Some recruitment specialists cited the recent increase in employer national insurance contributions as a factor in slowing down entry-level recruitment.

In this series of articles, we're publishing summaries with some of the key takeaways from a few of the workshops held at the 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025). Recent progress in Sphere Neural Networks demonstrates various possibilities for neural networks to achieve symbolic-level reasoning. This workshop aimed to reconsider various problems and discuss walk-round solutions in the two-way street commingling of neural networks and mathematics. This workshop brought together researchers from artificial intelligence and diverse scientific domains to address new challenges towards accelerating scientific discovery and engineering design. This was the fourth iteration of the workshop, with the theme of AI for biological sciences following previous three years' themes of AI for chemistry, earth sciences, and materials/manufacturing respectively.

One open question in the study of Large Language Models (LLMs) is whether they can emulate human ethical reasoning and act as believable proxies for human judgment. To investigate this, we introduce a benchmark dataset comprising 196 real-world ethical dilemmas and expert opinions, each segmented into five structured components: Introduction, Key Factors, Historical Theoretical Perspectives, Resolution Strategies, and Key Takeaways. We also collect non-expert human responses for comparison, limited to the Key Factors section due to their brevity. We evaluate multiple frontier LLMs (GPT-4o-mini, Claude-3.5-Sonnet, Deepseek-V3, Gemini-1.5-Flash) using a composite metric framework based on BLEU, Damerau-Levenshtein distance, TF-IDF cosine similarity, and Universal Sentence Encoder similarity. Metric weights are computed through an inversion-based ranking alignment and pairwise AHP analysis, enabling fine-grained comparison of model outputs to expert responses. Our results show that LLMs generally outperform non-expert humans in lexical and structural alignment, with GPT-4o-mini performing most consistently across all sections. However, all models struggle with historical grounding and proposing nuanced resolution strategies, which require contextual abstraction. Human responses, while less structured, occasionally achieve comparable semantic similarity, suggesting intuitive moral reasoning. These findings highlight both the strengths and current limitations of LLMs in ethical decision-making.

A speech by the US vice-president, JD Vance, symbolised a fracturing consensus on how to approach AI. He attended the summit with other global leaders, including the Indian prime minister, Narendra Modi, the Canadian PM, Justin Trudeau, and the head of the European Commission, Ursula von der Leyen. In his speech in the Grand Palais, Vance made it clear the US was not going to be held back from developing the tech by global regulation or an excessive focus on safety. "We need international regulatory regimes that foster the creation of AI technology rather than strangle it, and we need our European friends, in particular, to look to this new frontier with optimism rather than trepidation," he said. Speaking in front of the country's vice-premier, Zhang Guoqing, Vance warned his peers against cooperating with "authoritarian" regimes – in a clear reference to Beijing.

Sam Altman, the chief executive of ChatGPT's OpenAI, testified before members of a Senate subcommittee on Tuesday about the need to regulate the increasingly powerful artificial intelligence technology being created inside his company and others like Google and Microsoft. The three-hour-long hearing touched on several aspects of the risks that generative AI could pose to society, how it would affect the jobs market and why regulation by governments would be needed. Tuesday's hearing will be the first in a series of hearings to come as lawmakers grapple with drafting regulations around AI to address its ethical, legal and national security concerns. Senator Richard Blumenthal from Connecticut opened the proceedings with an AI-generated audio recording that sounded just like him. "Too often we have seen what happens when technology outpaces regulation. We have seen how algorithmic biases can perpetuate discrimination and prejudice and how the lack of transparency can undermine public trust. This is not the future we want," the voice said.

You've probably heard a lot about both Bing Chat and ChatGPT in recent weeks. Generative AI is here to stay and even if it's not something you can see yourself using in the long term, it's certainly worth trying these tools out and educating yourself a little more on them. Bing Chat is in some ways quite like ChatGPT. Microsoft is a big investor in OpenAI, the company behind ChatGPT, and uses the latest GPT-4 model in the backend of Bing Chat. So, there are definite similarities in the way the two operate.