Short-video platforms have become a central medium in the modern Internet landscape, where efficient information delivery and strong interactivity are reshaping user engagement and cultural dissemination. Among the various forms of user interaction, comments play a vital role in fostering community participation and enabling content re-creation. However, generating comments that are both compliant with platform guidelines and capable of exhibiting stylistic diversity and contextual awareness remains a significant challenge. We introduce LOLGORITHM, a modular multi-agent system (MAS) designed for controllable short-video comment generation. The system integrates video segmentation, contextual and affective analysis, and style-aware prompt construction. It supports six distinct comment styles: puns (homophones), rhyming, meme application, sarcasm (irony), plain humor, and content extraction. Powered by a multimodal large language model (MLLM), LOLGORITHM directly processes video inputs and achieves fine-grained style control through explicit prompt markers and few-shot examples. To support development and evaluation, we construct a bilingual dataset using official APIs from Douyin (Chinese) and YouTube (English), covering five popular video genres: comedy skits, daily life jokes, funny animal clips, humorous commentary, and talk shows. Evaluation combines automated metrics originality, relevance, and style conformity with a large-scale human preference study involving 40 videos and 105 participants. Results show that LOLGORITHM significantly outperforms baseline models, achieving preference rates of over 90% on Douyin and 87.55% on YouTube. This work presents a scalable and culturally adaptive framework for stylized comment generation on short-video platforms, offering a promising path to enhance user engagement and creative interaction.

Open-domain dialogue systems have made promising progress in recent years. While the state-of-the-art dialogue agents are built upon large-scale text-based social media data and large pre-trained models, there is no guarantee these agents could also perform well in fast-growing scenarios, such as live streaming, due to the bounded transferability of pre-trained models and biased distributions of public datasets from Reddit and Weibo, etc. To improve the essential capability of responding and establish a benchmark in the live open-domain scenario, we introduce the LiveChat dataset, composed of 1.33 million real-life Chinese dialogues with almost 3800 average sessions across 351 personas and fine-grained profiles for each persona. LiveChat is automatically constructed by processing numerous live videos on the Internet and naturally falls within the scope of multi-party conversations, where the issues of Who says What to Whom should be considered. Therefore, we target two critical tasks of response modeling and addressee recognition and propose retrieval-based baselines grounded on advanced techniques. Experimental results have validated the positive effects of leveraging persona profiles and larger average sessions per persona. In addition, we also benchmark the transferability of advanced generation-based models on LiveChat and pose some future directions for current challenges.

LiveChat is a customer service software that helps companies of all sizes to deliver effective customer service. It comes with a set of features that help to engage website visitors, generate leads, gather feedback, and boost online sales. ChatBot comes with a native LiveChat integration meaning you can connect both tools without coding. LiveChat lets you incorporate human touch into your support services, whereas ChatBot adds AI reliability and scalability into your services. In effect, you can deliver effective and real-time support to every client visiting your website.

Last November, during the Web Summit in Lisbon, David Marcus, VP of Messaging Products at Facebook, asked the following question: Who wants to call a brand? He was referring to the ordeal of having to get in touch with a customer service apparatus. Very often, this process is time consuming, stressful and leaves the customer frustrated, an extremely negative outcome for any brand. The current development in AI and machine learning has the potential to solve this situation by bringing a pleasant, time-efficient experience for customers, and efficiency and precious data to the brand. Brands need to be more accessible.

Artificial Intelligence (or AI for short) has become quite the buzzword recently. Everyone in the tech world seems to want to use it. A ton of different applications cropped up in the last couple of weeks, every more amazing than the last. We've seen AI that can tell us what it sees on an image, one that can suggest an answer based on an email contents and one that could tell us the weather if we ask the right questions. We wanted to check if there is any way we could use artificial intelligence in LiveChat to improve it in any way.