Hallucinations remain a significant challenge in current Generative AI models, undermining trust in AI systems and their reliability. This study investigates how orchestrating multiple specialized Artificial Intelligent Agents can help mitigate such hallucinations, with a focus on systems leveraging Natural Language Processing (NLP) to facilitate seamless agent interactions. To achieve this, we design a pipeline that introduces over three hundred prompts, purposefully crafted to induce hallucinations, into a front-end agent. The outputs are then systematically reviewed and refined by second- and third-level agents, each employing distinct large language models and tailored strategies to detect unverified claims, incorporate explicit disclaimers, and clarify speculative content. Additionally, we introduce a set of novel Key Performance Indicators (KPIs) specifically designed to evaluate hallucination score levels. A dedicated fourth-level AI agent is employed to evaluate these KPIs, providing detailed assessments and ensuring accurate quantification of shifts in hallucination-related behaviors. A core component of this investigation is the use of the OVON (Open Voice Network) framework, which relies on universal NLP-based interfaces to transfer contextual information among agents. Through structured JSON messages, each agent communicates its assessment of the hallucination likelihood and the reasons underlying questionable content, thereby enabling the subsequent stage to refine the text without losing context. The results demonstrate that employing multiple specialized agents capable of interoperating with each other through NLP-based agentic frameworks can yield promising outcomes in hallucination mitigation, ultimately bolstering trust within the AI community.

Kherson, Ukraine – In late November, Maria, a 22-year-old from Ponyativka in southern Ukraine, gave birth to a boy. She named her second child Ivan, after his father who had been dreaming about a son since he joined the army in 2023. Baby Ivan was the only child born that day in the district maternity hospital in Kherson, a city where more people die than are born and more decide to leave than stay. According to the local administration, just 15 babies were born in December while 256 people died and 311 fled. As Kherson dies out, its 83,000 residents – down from a population of more than 320,000 before the war – are focusing on how to survive relentless shelling by Russia and what locals have nicknamed "human safaris".

We present a method for augmenting a Large Language Model (LLM) with a combination of text and visual data to enable accurate question answering in visualization of scientific data, making conversational visualization possible. LLMs struggle with tasks like visual data interaction, as they lack contextual visual information. We address this problem by merging a text description of a visualization and dataset with snapshots of the visualization. We extract their essential features into a structured text file, highly compact, yet descriptive enough to appropriately augment the LLM with contextual information, without any fine-tuning. This approach can be applied to any visualization that is already finally rendered, as long as it is associated with some textual description.

There have been several instances of unidentified drones flying over military bases recently. At least 10 such drones had been seen flying above Manching Air Base near the city of Ingolstadt on Sunday evening, German police said. Last month, there were sightings at Manching and nearby Neuburg an der Donau. Drones were also spotted at the US air base at Ramstein and at an industrial zone near it in the North Sea. In her statement, Interior Minister Faeser said "espionage or sabotage are regularly considered as a possible reason".

Generative artificial intelligence (GenAI) holds great promise as a tool to support personalized learning. Teachers need tools to efficiently and effectively enhance content readability of educational texts so that they are matched to individual students reading levels, while retaining key details. Large Language Models (LLMs) show potential to fill this need, but previous research notes multiple shortcomings in current approaches. In this study, we introduced a generalized approach and metrics for the systematic evaluation of the accuracy and consistency in which LLMs, prompting techniques, and a novel multi-agent architecture to simplify sixty informational reading passages, reducing each from the twelfth grade level down to the eighth, sixth, and fourth grade levels. We calculated the degree to which each LLM and prompting technique accurately achieved the targeted grade level for each passage, percentage change in word count, and consistency in maintaining keywords and key phrases (semantic similarity). One-sample t-tests and multiple regression models revealed significant differences in the best performing LLM and prompt technique for each of the four metrics. Both LLMs and prompting techniques demonstrated variable utility in grade level accuracy and consistency of keywords and key phrases when attempting to level content down to the fourth grade reading level. These results demonstrate the promise of the application of LLMs for efficient and precise automated text simplification, the shortcomings of current models and prompting methods in attaining an ideal balance across various evaluation criteria, and a generalizable method to evaluate future systems.

Attacks on underwater cables running through strategically significant bodies of water in both the Baltic Sea and the South China Sea by Russia and China, respectively, in recent months has top officials concerned they are not "mere coincidence." Maritime sabotage efforts in both regions of the world appear to have been on the rise over the last several years, with a notable spike in recent months after at least three separate attacks occurred in as many months, beginning in November, and the top suspects are Russia and China. "The Kremlin has been running a hybrid campaign against Europe for years, ranging from spreading disinformation and cyberattacks to weaponizing energy supplies. Since Russia's full-scale invasion of Ukraine, these efforts have intensified dramatically," EU High Representative Kaja Kallas told Fox News Digital. "However, Russia is not the only challenge we face."

Russia claims it has captured two villages in eastern Ukraine where its forces have been steadily advancing for months, as Ukraine's president urged allies to deliver all the weapons they have promised to send to Kyiv. The Russian Defence Ministry said on Sunday that soldiers have captured the village of Yantarne in the eastern Donetsk region, about 10km (six miles) southwest of Kurakhove, a key logistics hub that Moscow claimed to have seized last week – a day after Russia's army said it had also taken new territory northwest of Kurakhove. The Defence Ministry added that soldiers had also captured the village of Kalinove in the northeastern Kharkiv region. The village is on the western bank of the Oskil River, which for a long time formed the front line between the two armies in the region. A Ukrainian official, quoted by the AFP news agency, said on Thursday that Russian forces had managed to establish a bridgehead on the western bank after crossing the river.

The challenge focused on the data-driven predictability of global sea surface temperatures (SSTs), a key factor in climate forecasting, ecosystem management, fisheries management, and climate change monitoring. The challenge involved forecasting SST anomalies (SSTAs) three months in advance using historical data and included a special task of predicting SSTAs nine months ahead for the Baltic Sea. Participants utilized various machine learning approaches to tackle the task, leveraging data from ERA5. This paper discusses the methodologies employed, the results obtained, and the lessons learned, offering insights into the future of climate-related predictive modeling.

Data-driven models are promising tools for predicting ocean conditions and enhancing the details of these predictions. In this study, we applied advanced machine learning methods to model sea surface velocity and height in the Gulf of Mexico. To forecast broad ocean conditions, we used a method called Fourier Neural Operators (FNO), designed to balance computational efficiency with accuracy through a specialized loss function that combines grid and spectral space information. For creating high-resolution details from low-resolution data -- a process called downscaling -- we explored two different neural network architectures and compared their performance against simpler linear interpolation. This combination of forecasting and downscaling methods greatly improves the efficiency of ocean forecast and downscaling compared to numerical simulation with limited input variables. Our results highlight that these data-driven techniques can provide reliable, physics-aware predictions that can be useful for quick, localized analyses and in generating statistical predictions.

Automatic Ship Identification Systems (AIS) play a key role in monitoring maritime traffic, providing the data necessary for analysis and decision-making. The integrity of this data is fundamental to the correctness of infer-ence and decision-making in the context of maritime safety, traffic manage-ment and environmental protection. This paper analyzes the impact of data integrity in large AIS datasets, on classification accuracy. It also presents er-ror detection and correction methods and data verification techniques that can improve the reliability of AIS systems. The results show that improving the integrity of AIS data significantly improves the quality of inference, which has a direct impact on operational efficiency and safety at sea.