Traditional approaches -- such as Win Probability Added (WPA)-based ranking or computer vision-driven event detection -- can identify scoring plays but often miss strategic depth, momentum shifts, and storyline progression. Manual curation remains the gold standard but is resource-intensive and not scalable. We introduce DIAMOND, an LLM-driven agent for context-aware baseball highlight summarization that integrates structured sports analytics with natural language reasoning. DIAMOND leverages sabermetric features -- Win Expectancy, WPA, and Leverage Index -- to quantify play importance, while an LLM module enhances selection based on contextual narrative value. This hybrid approach ensures both quantitative rigor and qualitative richness, surpassing the limitations of purely statistical or vision-based systems. Evaluated on five diverse Korean Baseball Organization League games, DIAMOND improves F1-score from 42.9% (WPA-only) to 84.8%, outperforming both commercial and statistical baselines. Though limited in scale, our results highlight the potential of modular, interpretable agent-based frameworks for event-level summarization in sports and beyond.

As part of its research, Naver has also published studies in the field of human-robot interaction. After a series of experiments, for example, Naver concluded that the optimal spot for a robot in a crowded elevator with humans was the corner next to the entrance on the side opposite of the elevator buttons. Putting the robot at the back of the elevator made humans uncomfortable, Naver's researchers found. The company's engineers also designed animated eyes that gaze in the direction that the robot is headed. They found that employees were better able to anticipate the robot's movement if they could see its gaze.

SEONGNAM, SOUTH KOREA--At the new headquarters of South Korea's largest internet company, a fleet of self-driving robots whirl around delivering coffee, lunchboxes and mailed packages. More than 100 of these autonomous robots, which look a bit like the droid R2-D2 from the "Star Wars" films and go by the name "Rookie," are operational at Naver high-rise office building. The tower is specifically designed with bump-free flooring and handle-free doors that open via sensors to help the robots move around more easily. Powering the robots is a critical technology: a private 5G network that gives them a stable connection to the cloud, or virtual server, where their computing takes place, and where each unit's learned intelligence is stored and shared. Having the hardware for this in the cloud keeps the robots small and less expensive to build, key requirements for broader adoption.

In this paper, we propose a natural language processing architecture that can handle tasks that previously required two models as one model. With a single model, we analyze the language patterns and conversational context of Alzheimer's patients and derive answers from two results: patient classification and chatbot. If the patient's language characteristics are identified by chatbots in daily life, doctors can plan more precise diagnosis and treatment for early diagnosis. The proposed model is used to develop chatbots that replace questionnaires that required experts. There are two natural language processing tasks performed by the model. The first is a 'natural language classification' that indicates with probability whether the patient has an illness, and the second is to generate the next 'answer' of the chatbot to the patient's answer. In the first half, a context vector, which is a characteristic of patient utterance, is extracted through a self-attention neural network. This context vector and chatbot (expert, moderator) questions are entered together into the encoder to obtain a matrix containing the characteristics of the interaction between the questioner and the patient. The vectorized matrix becomes a probability value for classification of patients. Enter the matrix into the decoder with the next answer from the chatbot (the moderator) to generate the next utterance. As a result of learning this structure with DmentiaBank's cookie theft description corpus, it was confirmed that the value of the loss function of the encoder and decoder was significantly reduced and converged. This shows that capturing the speech language pattern of Alzheimer's disease patients can contribute to early diagnosis and longitudinal studies of the disease in the future.

South Korean e-commerce company Naver Corp announced a $1.2 billion purchase of U.S. fashion resale platform Poshmark Inc but investors questioned the timing of its biggest acquisition amid a slowing economy and sent its shares tumbling. Naver, which is also South Korea's top search engine, will pay $17.90 cash for each Poshmark share and acquire all of its outstanding stock in a foray into the U.S. e-commerce market. Poshmark is the largest fashion consumer-to-consumer platform in North America, with 80 million registered users led by Millennial and Gen Z active users, Naver executives said in a conference call on Tuesday. The deal will combine Poshmark's shopping platform with Naver's technology, likely starting with live-streaming, a key driver of e-commerce in South Korea, followed by technologies such as image recognition and artificial intelligence, the two companies said in a statement. With Millennials and Gen Zers leaning toward value-driven consumption such as environmental protection, and with inflation squeezing wallets, Naver and Poshmark seek to lead "re-commerce" or consumer-driven resale - expected to be the next global trend after convenient e-commerce, or fast fashion, Naver CEO Choi Soo-Yeon told Reuters.

The greatest pattern that the enterprises are following is utilizing amazing and universal AI devices. There has been a huge expansion in the reception of man-made reasoning, for business or business purposes as well as for homegrown employments. Computer-based intelligence can give answers for nearly anything! It can assist with restoring malignant growth, controlling independent vehicles, and increasing human insight. Some accept that it will either change our expectations for everyday comforts or bring upon us an automated end of the world, prompting the destruction of mankind. Everything relies upon how we are using it.

Naver, the Seongnam, South Korean-based company that operates the eponymous search engine Naver, this week announced that it trained one of the largest AI language models of its kind, called HyperCLOVA. Naver claims that the system learned 6,500 times more Korean data than OpenAI's GPT-3 and contains 204 billion parameters, the parts of the machine learning model learned from historical training data. For the better part of a year, OpenAI's GPT-3 has remained among the largest AI language models ever created. Via an API, people have used it to automatically write emails and articles, summarize text, compose poetry and recipes, create website layouts, and generate code for deep learning in Python. But GPT-3 has key limitations, chief among them that it's only available in English.

Technological advances have brought seismic shifts to various industries in Asia's fourth-largest economy, but the livestock sector is considered to have relatively lagged behind others despite having huge future prospects. Daniel Kyeong, co-founder and CEO of South Korea-based tech startup Animal Industry Data Korea, or AID Korea, was quick to jump on an idea -- develop a health care solution for livestock by utilizing artificial intelligence (AI) technology. Founded in 2017, AID Korea provides each farm owner with a customized solution. Its AI-powered management platform called « Farmsplan » not only increases productivity but also enhances animal welfare, which will eventually lead to quality meat. The system employs overhead surveillance cameras installed at each farm to monitor each labeled animal and to track down any abnormal behavior or movement with its AI algorithm and big data analysis.

Naver Labs, the research subsidiary of South Korean search giant Naver, has opened the patent and design for its robotic cart, dubbed AIRCART, the company announced. Third-party developers will be able to use an open kit provided by the firm sometime within the first half of next year to create related robotic products, the company said. The kit will have the source code, circuit design board, and user guide for the patents. AIRCART is an electronic cart with physical human-robot interaction technology applied that augments strength. A strength sensor on the handle reads the user's intention and controls power and direction, and the user can then move heavy items with little exertion.

StarGAN can flexibly translate an input image to any desired target domain using only a single generator and a discriminator. The images are generated by StarGAN trained on the CelebA dataset. The images are generated by StarGAN trained on the RaFD dataset. The images are generated by StarGAN trained on both the CelebA and RaFD dataset. Overview of StarGAN, consisting of two modules, a discriminator D and a generator G. (a) D learns to distinguish between real and fake images and classify the real images to its corresponding domain.