Large Language Models (LLMs) have shown remarkable ability in solving complex tasks, making them a promising tool for enhancing tabular learning. However, existing LLM-based methods suffer from high resource requirements, suboptimal demonstration selection, and limited interpretability, which largely hinder their prediction performance and application in the real world. To overcome these problems, we propose a novel in-context learning framework for tabular prediction. The core idea is to leverage the explanations generated by LLMs to guide a smaller, locally deployable Surrogate Language Model (SLM) to make interpretable tabular predictions. Specifically, our framework mainly involves three stages: (i) Post Hoc Explanation Generation, where LLMs are utilized to generate explanations for question-answer pairs in candidate demonstrations, providing insights into the reasoning behind the answer. (ii) Post Hoc Explanation-Guided Demonstrations Selection, which utilizes explanations generated by LLMs to guide the process of demonstration selection from candidate demonstrations. (iii) Post Hoc Explanation-Guided Interpretable SLM Prediction, which utilizes the demonstrations obtained in step (ii) as in-context and merges corresponding explanations as rationales to improve the performance of SLM and guide the model to generate interpretable outputs. Experimental results highlight the framework's effectiveness, with an average accuracy improvement of 5.31% across various tabular datasets in diverse domains.

Molecular interactions often involve high-order relationships that cannot be fully captured by traditional graph-based models limited to pairwise connections. Hypergraphs naturally extend graphs by enabling multi-way interactions, making them well-suited for modeling complex molecular systems. In this work, we introduce EquiHGNN, an Equivariant HyperGraph Neural Network framework that integrates symmetry-aware representations to improve molecular modeling. By enforcing the equivariance under relevant transformation groups, our approach preserves geometric and topological properties, leading to more robust and physically meaningful representations. We examine a range of equivariant architectures and demonstrate that integrating symmetry constraints leads to notable performance gains on large-scale molecular datasets. Experiments on both small and large molecules show that high-order interactions offer limited benefits for small molecules but consistently outperform 2D graphs on larger ones. Adding geometric features to these high-order structures further improves the performance, emphasizing the value of spatial information in molecular learning. Our source code is available at https://github.com/HySonLab/EquiHGNN/

We present Bielik 11B v2, a state-of-the-art language model optimized for Polish text processing. Built on the Mistral 7B v0.2 architecture and scaled to 11B parameters using depth up-scaling, this model demonstrates exceptional performance across Polish language benchmarks while maintaining strong cross-lingual capabilities. We introduce two key technical innovations: Weighted Instruction Cross-Entropy Loss, which optimizes learning across diverse instruction types by assigning quality-based weights to training examples, and Adaptive Learning Rate, which dynamically adjusts based on context length. Comprehensive evaluation across multiple benchmarks demonstrates that Bielik 11B v2 outperforms many larger models, including those with 2-6 times more parameters, and significantly surpasses other specialized Polish language models on tasks ranging from linguistic understanding to complex reasoning. The model's parameter efficiency and extensive quantization options enable deployment across various hardware configurations, advancing Polish language AI capabilities and establishing new benchmarks for resource-efficient language modeling in less-represented languages.

Large Language Models (LLMs) have demonstrated strong capabilities in rewriting text across various styles. However, effectively leveraging this ability for example-based arbitrary style transfer, where an input text is rewritten to match the style of a given exemplar, remains an open challenge. A key question is how to describe the style of the exemplar to guide LLMs toward high-quality rewrites. In this work, we propose a prompting method based on register analysis to guide LLMs to perform this task. Empirical evaluations across multiple style transfer tasks show that our prompting approach enhances style transfer strength while preserving meaning more effectively than existing prompting strategies.

Medical vision-language models (VLMs) have shown promise as clinical assistants across various medical fields. However, specialized dermatology VLM capable of delivering professional and detailed diagnostic analysis remains underdeveloped, primarily due to less specialized text descriptions in current dermatology multimodal datasets. To address this issue, we propose MM-Skin, the first large-scale multimodal dermatology dataset that encompasses 3 imaging modalities, including clinical, dermoscopic, and pathological and nearly 10k high-quality image-text pairs collected from professional textbooks. In addition, we generate over 27k diverse, instruction-following vision question answering (VQA) samples (9 times the size of current largest dermatology VQA dataset). Leveraging public datasets and MM-Skin, we developed SkinVL, a dermatology-specific VLM designed for precise and nuanced skin disease interpretation. Comprehensive benchmark evaluations of SkinVL on VQA, supervised fine-tuning (SFT) and zero-shot classification tasks across 8 datasets, reveal its exceptional performance for skin diseases in comparison to both general and medical VLM models. The introduction of MM-Skin and SkinVL offers a meaningful contribution to advancing the development of clinical dermatology VLM assistants. MM-Skin is available at https://github.com/ZwQ803/MM-Skin

EfficientNet models are convolutional neural networks optimized for parameter allocation by jointly balancing network width, depth, and resolution. Renowned for their exceptional accuracy, these models have become a standard for image classification tasks across diverse computer vision benchmarks. While traditional neural networks learn correlations between feature channels during training, vector-valued neural networks inherently treat multidimensional data as coherent entities, taking for granted the inter-channel relationships. This paper introduces vector-valued EfficientNets (V-EfficientNets), a novel extension of EfficientNet designed to process arbitrary vector-valued data. The proposed models are evaluated on a medical image classification task, achieving an average accuracy of 99.46% on the ALL-IDB2 dataset for detecting acute lymphoblastic leukemia. V-EfficientNets demonstrate remarkable efficiency, significantly reducing parameters while outperforming state-of-the-art models, including the original EfficientNet. The source code is available at https://github.com/mevalle/v-nets.

Pope Leo XIV has held his first meeting with the world's cardinals since his election as the head of the Catholic Church, identifying artificial intelligence (AI) as one of the most crucial issues facing humanity. Leo, the first American pope, laid out a vision of his papacy at the Vatican on Saturday, telling the cardinals who elected him that AI poses challenges to defending "human dignity, justice and labour" – a view shared with his predecessor, the late Pope Francis. Explaining his choice of name, the pontiff said he identified with the late Leo XIII, who had defended workers' rights during his 1878-1903 papacy at the dawn of the industrial age, adding that "social teaching" was now needed in response to the modern-day revolution brought by AI. The late Pope Francis, who died last month, warned that AI risked turning human relations into mere algorithms and called for an international treaty to regulate it. Francis warned the Group of Seven industrialised nations last year that AI must remain human-centric, so that decisions about when to use weapons or even less-lethal tools would not fall to machines.

Russia and Ukraine have accused one another of violating a three-day ceasefire as Moscow marked Victory Day by welcoming allies to a grand military parade. Russia's President Vladimir Putin marked the 80th anniversary of victory over Nazi Germany on Friday alongside China's Xi Jinping, in an event clearly intended to bolster support for his three-year offensive against Ukraine, which he had unilaterally paused for 72 hours to mark the occasion. "Russia has been and will remain an indestructible barrier against Nazism, Russophobia and anti-Semitism," said Putin, seeking to draw parallels between World War II – or the Great Patriotic War as it is named in Russia and other parts of the former Soviet Union – and the Ukraine war. Russia maintains that its February 2022 invasion of its neighbour is a battle against a "Nazi" regime in Kyiv. Ukraine has dismissed that claim as "incomprehensible".

Chinese soldiers are seen marching in Moscow's Red Square on Friday, May 9. (Credit: CCTV) Chinese President Xi Jinping was photographed standing next to Vladimir Putin on Friday as thousands of Russian troops and military vehicles rumbled through Moscow's Red Square during the country's annual Victory Day parade. The event, marking Russia's 80th anniversary of the defeat of Nazi Germany in World War II, featured over 11,500 troops and more than 180 military vehicles, including tanks, armored infantry vehicles and artillery used on the battlefield in Ukraine. "We are proud of their courage and determination, their spiritual force that always has brought us victory," Putin said about the Russian troops fighting in the war. Russian flag carrier Aeroflot canceled more than 100 flights to and from Moscow and delayed over 140 others on Wednesday as the military were repelling repeated Ukrainian drone attacks on the capital. Russian President Vladimir Putin, center right, and Chinese President Xi Jinping, center, watch the Victory Day military parade in Moscow, Russia, on Friday, May 9. (Mikhail Korytov/Photo host agency RIA Novosti via AP) Ukrainian authorities also reported scores of Russian strikes on Friday that killed at least two people in the Kherson and Zaporizhzhia regions and damaged buildings.

In the days ahead of the proposed truce, Moscow and Kyiv exchanged a barrage of strikes. Flights at airports across Russia were cancelled and some 60,000 passengers left stranded in the wake of Ukrainian drone attacks. Heavy restrictions are in place in the centre of Moscow as Russia prepares to mark the Soviet Union's victory over Nazi Germany. Russia says 27 world leaders are attending the event, with thousands of troops marching on Red Square ahead of a parade of some of Russia's latest weaponry. Brazil's Luiz Inácio Lula da Silva and Venezuelan President Nicolas Maduro are among the assembled guests, along with Serbian President Aleksandar Vucic and Robert Fico, Slovakia's prime minister who is the only European Union leader to travel to Moscow. Ukraine's Volodymyr Zelensky had earlier warned that he could not guarantee the safety of anyone attending the event and has urged heads of state not to travel to Moscow.