We present a novel self-supervised approach for hierarchical representation learning and segmentation of perceptual inputs in a streaming fashion.

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We present a novel self-supervised approach for hierarchical representation learning and segmentation of perceptual inputs in a streaming fashion. Our research addresses how to semantically group streaming inputs into chunks at various levels of a hierarchy while simultaneously learning, for each chunk, robust global representations throughout the domain. To achieve this, we propose STREAMER, an architecture that is trained layer-by-layer, adapting to the complexity of the input domain. In our approach, each layer is trained with two primary objectives: making accurate predictions into the future and providing necessary information to other levels for achieving the same objective. The event hierarchy is constructed by detecting prediction error peaks at different levels, where a detected boundary triggers a bottom-up information flow. At an event boundary, the encoded representation of inputs at one layer becomes the input to a higher-level layer.

We thank all three reviewers for their comments and insightful suggestions. We outline some of these changes here. Our approach uses CFR instead of MCTS. We've added the following sentence: "Compared to Does the proposed method generalize to other games such as werewolf or saboteur? . . . DeepRole could be applied directly to Saboteur. We mention in the discussion: "In future Need ablation and analysis -- we all know trained agents are vulnerable to adversarial human players -- e.g. the Another interesting observation is the bot does not need conversation.

We present a novel self-supervised approach for hierarchical representation learning and segmentation of perceptual inputs in a streaming fashion.

Live streaming platforms have become a dominant form of online content consumption, offering dynamically evolving content, real-time interactions, and highly engaging user experiences. These unique characteristics introduce new challenges that differentiate live streaming recommendation from traditional recommendation settings and have garnered increasing attention from industry in recent years. However, research progress in academia has been hindered by the lack of publicly available datasets that accurately reflect the dynamic nature of live streaming environments. To address this gap, we introduce KuaiLive, the first real-time, interactive dataset collected from Kuaishou, a leading live streaming platform in China with over 400 million daily active users. The dataset records the interaction logs of 23,772 users and 452,621 streamers over a 21-day period. Compared to existing datasets, KuaiLive offers several advantages: it includes precise live room start and end timestamps, multiple types of real-time user interactions (click, comment, like, gift), and rich side information features for both users and streamers. These features enable more realistic simulation of dynamic candidate items and better modeling of user and streamer behaviors. We conduct a thorough analysis of KuaiLive from multiple perspectives and evaluate several representative recommendation methods on it, establishing a strong benchmark for future research. KuaiLive can support a wide range of tasks in the live streaming domain, such as top-K recommendation, click-through rate prediction, watch time prediction, and gift price prediction. Moreover, its fine-grained behavioral data also enables research on multi-behavior modeling, multi-task learning, and fairness-aware recommendation. The dataset and related resources are publicly available at https://imgkkk574.github.io/KuaiLive.

AI slop is flooding every single digital platform, and music streaming services are no exception--so much so, even someone who generally avoids AI might find themselves unknowingly listening to a robot hornily singing about butts. Take the sordid saga of "Make Love to My Shitter," an AI-generated track from an artist called BannedVinylCollection. Brace Belden, a host of the popular politics podcast TrueAnon, says that Spotify recently queued up the bawdy song after he'd finished listening to alt-country legend Lucinda Williams' 1992 album Sweet Old World. "I didn't realize the song was AI at first," he says. "I thought it might've been some obscene joke record from the 80s or 90s."

When Netflix first started adding video games to its huge catalogue of streaming TV shows and films, it did so quietly. In 2021, after releasing an impressive experiment with the idea of interactive film in Black Mirror: Bandersnatch in 2018 and a free Stranger Things game in 2019, Netflix began expanding more fully into interactive entertainment. The streamer's gaming offering, for a long time, was its best-kept secret. Whoever was running it really had an eye for quality: award-winningly brilliant and relatively little-known indie games comprised the majority of its catalogue, alongside decent licensed games based on everything from The Queen's Gambit to the reality dating show Too Hot to Handle. Subscribers could play games such as Before Your Eyes, a brief and touching story about a life cut short; Spiritfarer, about guiding lost souls to rest and Into the Breach, a superb sci-fi strategy game with robots v aliens.

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