Hyperbolic representations are effective in modeling knowledge graph data which is prevalently used to facilitate multi-hop reasoning. However, a rigorous and detailed comparison of the two spaces for this task is lacking. In this paper, through a simple integration of hyperbolic representations with an encoder-decoder model, we perform a controlled and comprehensive set of experiments to compare the capacity of hyperbolic space versus Euclidean space in multi-hop reasoning. Our results show that the former consistently outperforms the latter across a diverse set of datasets. In addition, through an ablation study, we show that a learnable curvature initialized with the delta hyperbolicity of the utilized data yields superior results to random initializations. Furthermore, our findings suggest that hyperbolic representations can be significantly more advantageous when the datasets exhibit a more hierarchical structure.

Sony's anime-focused streaming service Crunchyroll has come under fire after users pointed out substandard AI-generated subtitles in several of its series. For example, viewers reportedly saw the phrase "ChatGPT said" in the German subtitles for Necronomico and the Cosmic Horror Show. Both the English and German captions have been criticized for being sloppy and difficult to understand. Engadget reports that Crunchyroll confirmed a third-party provider violated its agreement by using AI, and the company is now investigating the incident. The company's CEO, Rahul Purini, previously said in an interview with The Verge that Crunchyroll has been testing AI subtitles to release episodes more quickly.

If only more of these dumb ones come along," boasts a woman in a new video game that has fuelled a debate on sexism in China. The players in the live-action Revenge on Gold Diggers are male protagonists lured into relationships by manipulative women who are after their money - how the man responds shapes the rest of the story. It topped the gaming platform Steam's sales list within hours of its release in June but controversy quickly followed. Some slammed it for reinforcing insulting gender stereotypes, while supporters say the game cautions people about love scams. So heated was the criticism that the game's creators quietly renamed it Emotional Anti-Fraud Simulator the next day. But that wasn't enough to undo the damage. The game's lead director, Hong Kong filmmaker Mark Hu, has now been banned on several Chinese social media platforms. The game's creators insist they never intended to "target women" - rather they wanted to facilitate "open dialogue about emotional boundaries and the grey zones in modern dating". Xu Yikun, an artist who tried the game and found it deeply offensive, rejects that rationale. She accuses them of "a classic business model that thrives on generating content that sparks debate and divisions". Critics like her say the very term "gold digger" reeks of misogyny. "It's a label that's used, all too often, on women," Ms Xu says. "Sexist jokes and derogatory terms like these have found their way into our everyday language." "If you have a rich boyfriend, you are called a gold digger.

Industries can rethink how work gets done, raising the bar for productivity and workplace safety. Across industries, companies are feeling the squeeze from labor shortages, rising costs and nonstop pressure to boost efficiency. Robots are quickly becoming real-life solutions, and their promise has never felt more relevant. With factories and warehouses scrambling to fill essential roles, the search for fresh ideas is heating up. That's where AEON comes in.

A growing number of fire departments across the country are turning to artificial intelligence to help detect and respond to wildfires more quickly. The rise of artificial intelligence in education is forcing schools and universities to rethink everything from homework policies to how final exams are administered. With tools like ChatGPT now widespread, students can generate essays, solve complex math problems or draft lab reports in seconds, raising urgent questions about what authentic learning looks like in 2025. To fight back, some schools are turning to an unlikely solution: pen and paper. The old-school "blue book," a lined booklet used for handwritten test answers, is staging a comeback, according to reporting from The Wall Street Journal.

The traffic receded as Chicago withdrew into the distance behind me on Interstate 90. The speakers in my rental car, playing Spotify from my smartphone, put out the opening riff of a laid-back psychedelic-rock song. When the lyrics came, delivered in a folksy vibrato, they matched my mood: "Smoke in the sky / No peace found," the band's vocalist sang. Except perhaps he didn't really sing, because he doesn't exist. By all appearances, neither does the band, called the Velvet Sundown.

Perhaps unsurprisingly, the combo delivers record-setting performance. The launch of new Nvidia RTX mobile graphics--including the top-tier RTX 5090 with 24GB of VRAM--has the potential for chart-topping performance. Now it's joined by AMD's Ryzen 9 9955HX3D, a 16-core CPU with the company's vaunted 3D V-Cache, an extra stack of L3 cache that can prove useful in games. The MSI Raider A18 HX A9W brings both new chips into one chassis. That's incredible hardware, but the laptop retails for an equally incredible MSRP of 5,099.99. Each is an undisputed heavyweight in its category and should deliver a killer one-two punch of CPU and GPU performance. With that said, however, this MSI Raider A18 HX A9W still must deal with the power and thermal constraints faced by every laptop--and it will be interesting to see the results. The MSI Raider A18 HX A9W delivers additional technical highlights, too, like the PCIe 5.0 solid state drive and the 4K Mini-LED display.

Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation remains the ultimate challenge in long-text processing. We directly optimize for long-text tasks in an end-to-end fashion and introduce a novel agent workflow, MemAgent, which reads text in segments and updates the memory using an overwrite strategy. We extend the DAPO algorithm to facilitate training via independent-context multi-conversation generation. MemAgent has demonstrated superb long-context capabilities, being able to extrapolate from an 8K context trained on 32K text to a 3.5M QA task with performance loss < 5% and achieves 95%+ in 512K RULER test.

Amid a tidal wave of misinformation flooding social media during elections and crises, extensive research has been conducted on misinformation detection, primarily focusing on text-based or image-based approaches. However, only a few studies have explored multimodal feature combinations, such as integrating text and images for building a classification model to detect misinformation. This study investigates the effectiveness of different multimodal feature combinations, incorporating text, images, and social features using an early fusion approach for the classification model. This study analyzed 1,529 tweets containing both text and images during the COVID-19 pandemic and election periods collected from Twitter (now X). A data enrichment process was applied to extract additional social features, as well as visual features, through techniques such as object detection and optical character recognition (OCR). The results show that combining unsupervised and supervised machine learning models improves classification performance by 15% compared to unimodal models and by 5% compared to bimodal models. Additionally, the study analyzes the propagation patterns of misinformation based on the characteristics of misinformation tweets and the users who disseminate them.

We extend BeamAttack, an adversarial attack algorithm designed to evaluate the robustness of text classification systems through word-level modifications guided by beam search. Our extensions include support for word deletions and the option to skip substitutions, enabling the discovery of minimal modifications that alter model predictions. We also integrate LIME to better prioritize word replacements. Evaluated across multiple datasets and victim models (BiLSTM, BERT, and adversarially trained RoBERTa) within the BODEGA framework, our approach achieves over a 99\% attack success rate while preserving the semantic and lexical similarity of the original texts. Through both quantitative and qualitative analysis, we highlight BeamAttack's effectiveness and its limitations. Our implementation is available at https://github.com/LucK1Y/BeamAttack