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You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Trump makes extraordinary promise to give EVERY American adult $5,000 'dividend'... but only if the Republicans win the midterms Fox News host Bret Baier is left stunned as AI whistleblower sounds urgent alarm on'most dangerous technology humanity has ever created'... before offering sole hope for preventing the end of civilization Melania Trump's team breaks silence on her husband's devoted'human printer' aide Natalie Harp I can't get what Lindsay Clancy's lawyer said about her husband Patrick out of my head... reading between the lines, it's so clear what he meant: KENNEDY The King has slapped down Harry and Meghan... but this is the biggest revelation from his public letter. The King's letter slapping down Harry and Meghan exposes an unforeseen weakness... and one word in particular could tell us everything about what's coming next: ROBERT JOBSON NFL fans divided over Black National ...

Recent findings provide one of the most detailed pictures to date of the genetic architecture of schizophrenia, opening up new avenues for research into the disorder. One of the greatest challenges in modern genetics is schizophrenia . Unlike diseases caused by a single mutation, this disorder appears to arise from a combination of hundreds of genetic variants, each with small effects on different brain processes. Some influence neural development, while others alter communication between neurons or the organization of brain connections. Together, they contribute to the risk of developing the disease.

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Recent advances in multimodal models have raised questions about whether vision-and-language models (VLMs) integrate cross-modal information in ways that reflect human cognition. One well-studied test case in this domain is the bouba-kiki effect, where humans reliably associate pseudowords like'bouba' with round shapes and'kiki' with jagged ones. Given the mixed evidence found in prior studies for this effect in VLMs, we present a comprehensive re-evaluation focused on two variants of CLIP, ResNet and Vision Transformer (ViT), given their centrality in many state-of-the-art VLMs. We apply two complementary methods closely modelled after human experiments: a prompt-based evaluation that uses probabilities as a measure of model preference, and we use Grad-CAM as a novel approach to interpret visual attention in shape-word matching tasks. Our findings show that these model variants do not consistently exhibit the bouba-kiki effect. While ResNet shows a preference for round shapes, overall performance across both model variants lacks the expected associations. Moreover, direct comparison with prior human data on the same task shows that the models' responses fall markedly short of the robust, modality-integrated behaviour characteristic of human cognition. These results contribute to the ongoing debate about the extent to which VLMs truly understand cross-modal concepts, highlighting limitations in their internal representations and alignment with human intuitions.

OpenAI launches a limited preview of GPT-5.6 for a'small group of trusted partners' OpenAI launches a limited preview of GPT-5.6 for a'small group of trusted partners' The model's three variants will be available more broadly in the coming weeks. OpenAI has started previewing its GPT 5.6 series, which will be available in three versions, to a limited number of trusted partners. The company says the variant Sol is its strongest model yet, while Terra is for everyday use and has a similar performance to GPT 5.5 despite being twice as cheap. Luna, the last variant, is the company's lowest cost model. OpenAI plans to give them a broad release sometime in the coming weeks.

Large language model (LLM) routers improve the efficiency of multi-model systems by directing each query to the most appropriate model while leveraging the diverse strengths of heterogeneous LLMs. Most existing approaches frame routing as a classification problem based solely on the input query. While this reduces overhead by avoiding inference across all models, it overlooks valuable information that could be gleaned from potential outputs and fails to capture implicit intent or contextual nuances that often emerge only during response generation. These limitations can result in suboptimal routing decisions, particularly for complex or ambiguous queries that require deeper semantic understanding. To address this challenge, we propose Lookahead, a routing framework that "foresees" potential model outputs by predicting their latent representations and uses these predictions to guide model selection, thus enabling more informed routing without full inference. Within this framework, we implement two approaches based on causal and masked language models. Empirical evaluations across seven public benchmarks--spanning instruction following, mathematical reasoning, and code generation--show that Lookahead consistently outperforms existing routing baselines, achieving an average performance gain of 7.7% over the state-of-the-art.

Explicit noise-level conditioning is widely regarded as essential for the effective operation of Graph Diffusion Models (GDMs). In this work, we challenge this assumption by investigating whether denoisers can implicitly infer noise levels directly from corrupted graph structures, potentially eliminating the need for explicit noise conditioning. To this end, we develop a theoretical framework centered on Bernoulli edge-flip corruptions and extend it to encompass more complex scenarios involving coupled structure-attribute noise. Extensive empirical evaluations on both synthetic and real-world graph datasets, using models such as GDSS and DiGress, provide strong support for our theoretical findings. Notably, unconditional GDMs achieve performance comparable or superior to their conditioned counterparts, while also offering reductions in parameters (4 6%) and computation time (8 10%). Our results suggest that the high-dimensional nature of graph data itself often encodes sufficient information for the denoising process, opening avenues for simpler, more efficient GDM architectures.