Africa
From gas to groceries, has Trump kept his promise to tackle rising prices?
From gas to groceries, has Trump kept his promise to tackle rising prices? President Donald Trump was swept to power for a second time on the back of a central campaign promise to tackle inflation. The steep rise in the cost of living was top of voters' minds and Trump blamed President Joe Biden. He also made sweeping promises to bring down prices for Americans starting on day one. One year on from his victory, BBC Verify revisits some of the president's claims.
Will quantum be bigger than AI?
Will quantum be bigger than AI? There's an old adage among tech journalists like me - you can either explain quantum accurately, or in a way that people understand, but you can't do both. That's because quantum mechanics - a strange and partly theoretical branch of physics - is a fiendishly difficult concept to get your head around. It involves tiny particles behaving in weird ways. And this odd activity has opened up the potential of a whole new world of scientific super power. Its mind-boggling complexity is probably a factor in why quantum has ended up with a lower profile than tech's current rockstar - artificial intelligence (AI).
Distilling LLM Agent into Small Models with Retrieval and Code Tools
Kang, Minki, Jeong, Jongwon, Lee, Seanie, Cho, Jaewoong, Hwang, Sung Ju
Large language models (LLMs) excel at complex reasoning tasks but remain computationally expensive, limiting their practical deployment. To address this, recent works have focused on distilling reasoning capabilities into smaller language models (sLMs) using chain-of-thought (CoT) traces from teacher LLMs. However, this approach struggles in scenarios requiring rare factual knowledge or precise computation, where sLMs often hallucinate due to limited capability. In this work, we propose Agent Distillation, a framework for transferring not only reasoning capability but full task-solving behavior from LLM-based agents into sLMs with retrieval and code tools. We improve agent distillation along two complementary axes: (1) we introduce a prompting method called first-thought prefix to enhance the quality of teacher-generated trajectories; and (2) we propose a self-consistent action generation for improving test-time robustness of small agents. We evaluate our method on eight reasoning tasks across factual and mathematical domains, covering both in-domain and out-of-domain generalization. Our results show that sLMs as small as 0.5B, 1.5B, 3B parameters can achieve performance competitive with next-tier larger 1.5B, 3B, 7B models fine-tuned using CoT distillation, demonstrating the potential of agent distillation for building practical, tool-using small agents. Our code is available at https://github.com/Nardien/agent-distillation.
SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning
Adebayo, Samuel, Dessing, Joost C., McLoone, Seán
In this research, we present SLYKLatent, a novel approach for enhancing gaze estimation by addressing appearance instability challenges in datasets due to aleatoric uncertainties, covariant shifts, and test domain generalization. SLYKLatent utilizes Self-Supervised Learning for initial training with facial expression datasets, followed by refinement with a patch-based tri-branch network and an inverse explained variance-weighted training loss function. Our evaluation on benchmark datasets achieves a 10.9% improvement on Gaze360, supersedes top MPIIFaceGaze results with 3.8%, and leads on a subset of ETH-XGaze by 11.6%, surpassing existing methods by significant margins. Adaptability tests on RAF-DB and Affectnet show 86.4% and 60.9% accuracies, respectively. Ablation studies confirm the effectiveness of SLYKLatent's novel components.
ECGXtract: Deep Learning-based ECG Feature Extraction for Automated CVD Diagnosis
Abuzied, Youssif, AbdEltawab, Hassan, Gaber, Abdelrhman, ElBatt, Tamer
This paper presents ECGXtract, a deep learning-based approach for interpretable ECG feature extraction, addressing the limitations of traditional signal processing and black-box machine learning methods. In particular, we develop convolutional neural network models capable of extracting both temporal and morphological features with strong correlations to a clinically validated ground truth. Initially, each model is trained to extract a single feature, ensuring precise and interpretable outputs. A series of experiments is then carried out to evaluate the proposed method across multiple setups, including global versus lead-specific features, different sampling frequencies, and comparisons with other approaches such as ECGdeli. Our findings show that ECGXtract achieves robust performance across most features with a mean correlation score of 0.80 with the ground truth for global features, with lead II consistently providing the best results. For lead-specific features, ECGXtract achieves a mean correlation score of 0.822. Moreover, ECGXtract achieves superior results to the state-of-the-art open source ECGdeli as it got a higher correlation score with the ground truth in 90% of the features. Furthermore, we explore the feasibility of extracting multiple features simultaneously utilizing a single model. Semantic grouping is proved to be effective for global features, while large-scale grouping and lead-specific multi-output models show notable performance drops. These results highlight the potential of structured grouping strategies to balance the computational efficiency vs. model accuracy, paving the way for more scalable and clinically interpretable ECG feature extraction systems in limited resource settings.
HAFixAgent: History-Aware Automated Program Repair Agent
Shi, Yu, Li, Hao, Adams, Bram, Hassan, Ahmed E.
Automated program repair (APR) has recently shifted toward large language models and agent-based systems, yet most systems rely on local snapshot context, overlooking repository history. Prior work shows that repository history helps repair single-line bugs, since the last commit touching the buggy line is often the bug-introducing one. In this paper, we investigate whether repository history can also improve agentic APR systems at scale, especially for complex multi-hunk bugs. We present HAFixAgent, a History-Aware Bug-Fixing Agent that injects blame-derived repository heuristics into its repair loop. A preliminary study of all 854 real-world bugs from Defects4J motivates our design, showing that bug-relevant history is both widely available and highly concentrated. Empirical comparison of HAFixAgent with two state-of-the-art baselines shows: (1) Effectiveness: HAFixAgent significantly improves over the agent-based baseline (by 212.3%) and the multi-hunk baseline (by 29.9%). (2) Efficiency: history does not significantly increase agent steps and keeps token costs comparable, with notably lower median costs for complex multi-file-multi-hunk bugs. (3) Practicality: combining different historical heuristics repairs more bugs, offering a clear cost-benefit trade-off. HAFixAgent offers a practical recipe for history-aware agentic APR: ground the agent in version control history, prioritize diff-based historical context, and integrate complementary heuristics when needed.
King handed Nvidia boss a letter warning of AI dangers
Jensen Huang, the head of the world's most valuable company Nvidia, says King Charles III personally handed him a copy of a speech he delivered in 2023 that included a warning about the dangers of artificial intelligence. He said, there's something I want to talk to you about. And he handed me a letter, Huang told the BBC, speaking after receiving the 2025 Queen Elizabeth Prize for Engineering in a ceremony at St James's Palace. The letter was a copy of the speech delivered by the King in 2023 at the world's first AI Summit, held at Bletchley Park . In it the monarch said that the risks of AI needed to be tackled with a sense of urgency, unity and collective strength.
In 'watershed moment', Tesla board to vote on Musk's 1 trillion package
In'watershed moment', Tesla board to vote on Musk's $1 trillion package Tesla's board is set to vote on CEO Elon Musk's $1 trillion pay package as major proxy adviser firms urge shareholders to reject the deal. The vote is scheduled for Thursday and will determine whether Musk secures what is the largest compensation package in corporate history. These firms often influence large passive funds that hold significant stakes in the electric carmaker. Tesla has faced mounting challenges this year, with global sales declining and investor confidence wavering. In July, Tesla reported a 13.5 percent decline in sales in the United States. They jumped 7.4 percent in the third quarter ending in September compared with the same period the year before, as US consumers scrambled to take advantage of a $7,500 EV tax credit that was set to expire that month.
Astronauts stranded in space over fears return capsule suffered damage from an unknown object
Doctor's husband who left daughter, 2, to die in hot car while watching adult movies DIES day he was to be locked up I keep hearing the same mortifying whisper about Meghan and Harry... their American dream is about to come crumbling down: MEGYN KELLY Baby girl with horrifying side effects, mom couldn't breathe and dad seriously sick... after simple error turned dream home into a death trap Super hot seductresses entrap Silicon Valley nerds and steal vital secrets... so we tracked one down and found out how Bryan Kohberger's staggering prison slush fund is revealed as prosecutors demand he give money to Idaho victims' families Why screaming female migrant who shouted'Help me, I have papers!' was arrested by ICE at Salt Lake City airport Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look Somali-American who said protecting illegal migrants from Trump was top priority LOSES bid to become America's wokest mayor Influencer Barbara Jankavski who was known online as'Human Barbie' dies age 31 My girlfriend's new body modification is repulsive. She says she did it for me... This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. Famous American writer's son, 19, arrested over alleged plot to bomb Detroit gay bars in ISIS terror attack The murder that haunts the Kennedys: Martha Moxley's loved ones reveal their truth in the FREE The Crime Desk newsletter... as accused cousin cleared in killing breaks cover Taylor Momsen admits Gossip Girl role was'killing' her during'long battle' to quit hit series Inside Zohran Mamdani's woke, celebrity filled victory party after socialist won NYC mayoral election Ariana Grande looks worlds away from her'clean-girl look' as she rocks black curly hair and skinny jeans in a major throwback snap from 18 years ago Three astronauts already in space for six months are now stranded in orbit after their craft may have been damaged by dangerous debris floating around Earth . China's Manned Spaceflight Agency (CMSA) has revealed that crew from its Shenzhou 20 mission will need to stay on board the Chinese station Tiangong.
MP wants Elon Musk's chatbot shut down over claim he enabled grooming gangs
MP wants Elon Musk's chatbot shut down over claim he enabled grooming gangs An MP has called for Elon Musk's artificial intelligence (AI) chatbot to be shut down after it called him a rape enabler. The Grok chatbot made the post on X about SNP MP Pete Wishart, after a user asked it to comment on the member's opinion on whether there should be an inquiry into grooming gangs in Scotland. Mr Wishart said he was seeking legal advice over the deeply distressing accusation and called for Musk to recalibrate the bot to shut it down. The BBC has approached XAI, the parent company of X, for comment. I was genuinely shocked to be described in such an appalling and defamatory way, Mr Wishart said in a statement.