Shengshu Technology Releases New Real-Time Interactive Model Vidu S1
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MIRA, an open-source 5B multiplayer world model, uses DINOv3 representation to mitigate long-term drift and can simulate 2v2 battles in Rocket League in real time.
According to Beating, AI research firm General Intuition, in collaboration with French AI lab Kyutai and Epic Games, has launched MIRA, a multiplayer interactive world model. As a generative game simulator supporting real-time multiplayer interaction, MIRA can simulate 2v2 battles in Rocket League in real time, based solely on historical footage and player button presses, without requiring a physics engine, rendering engine, or explicit 3D representation for inference. Unlike the "decoupling of logic computation and image rendering" approach adopted by companies like Odyssey, MIRA takes a generative simulation approach based on video latent space. MIRA boasts 5 billion parameters, and its core design builds the latent prediction space on a frozen general-purpose visual encoder, DINOv3-L. Leveraging pre-trained visual features, the generated latent states can more stably fall within the effective representation space, significantly mitigating image drift and divergence during long-term prediction. For multi-screen alignment, MIRA stitches the latent images from four player perspectives into a unified grid, enabling spatial attention mechanisms to operate naturally across viewpoints and improving the spatial consistency of vehicles, the soccer ball, and key events across multiple perspectives. The Action Dropout introduced during training also helps the system complete the game behavior of vehicles not controlled by commands when parts of the motion flow are missing. Currently, MIRA can run in real time at 20 frames per second on a single NVIDIA B200 graphics card. The team has open-sourced the training and inference code and released the Rocket Science dataset, which contains 1,000 hours of matches, approximately 4,000 hours of video, motion flow, and physics data from four perspectives; the complete training of the model used approximately 10,000 hours of clean match data.
Upgrade the Thousand Questions Big Model to a Real-Time Speech Recognition Big Model (Fun-ASR-Realtime)
According to Mars Finance, Qianwen's big model has been officially upgraded to the real-time speech recognition big model Fun-ASR-Realtime—a streaming speech recognition model with first-word latency controlled at the level of hundreds of milliseconds and recognition accuracy close to that of offline models, supporting 16 dialects and 30 languages.
After Their AI Models Hacked Real Companies, AI Labs Call for Stronger Cyber Defenses
More than 100 AI, security, finance, and technology organizations want governments and industry to prepare for attacks powered by increasingly capable models.
BNB Chain Releases 2026 Second Half Technology Roadmap: BSC Throughput Target to Double
Odaily Odaily reports that BNB Chain has released its technology roadmap for the second half of 2026, announcing that it will continue to optimize speed, throughput, and protocol stability, and plans to double the throughput of the BSC mainnet again, while developing a new generation of Layer 1 architecture for the next decade. BNB Chain stated that in the first half of 2026, BSC completed several performance upgrades, including reducing the block interval from 750 milliseconds to 450 milliseconds, decreasing the memory finality time from 1125 milliseconds to 650 milliseconds, and increasing the baseline throughput from approximately 2800 TPS to approximately 5200 TPS. At the middleware level, BNB Chain also advanced the construction of AI agents and payment infrastructure, including launching BNB Agent Studio and BNB Agent SDK to support the deployment of self-governed on-chain AI agents; it also continued to improve the Middleware Payment Protocol (MPP) SDK and explored institutional-grade privacy frameworks. For the second half of 2026, BNB Chain has proposed three core objectives: Double the throughput: Through BEP-675, BAL integration and EVM execution optimization, the BSC mainnet performance is further improved, with the long-term goal of achieving a 10x performance increase for the entire BNB Chain; Reduce the impact of network congestion: Improve stability during peak periods through resource isolation, dedicated transaction channels, and a transaction inclusion mechanism based on the FOCIL concept; Lowering the barrier to entry: Optimizing gas fee structures for different industries to reduce the cost for enterprises to enter Web2 and Web3 application scenarios.
Zhang Yuhang, Beijing Municipal Bureau of Economy and Information Technology: Developing the word-based economy and exploring business models such as quantity-based pricing, performance-based payment, and subscription-based hosting.
According to Mars Finance, Zhang Yuhang, a second-level inspector of the Beijing Municipal Bureau of Economy and Information Technology, stated on July 8th that the next step will be to create a new form of intelligent economy. This includes developing a word-based economy, building word-based factories, and exploring business models such as quantity-based pricing, performance-based payment, and subscription-based hosting; accelerating research and development of key technologies for embodied intelligence and robot products, and improving the systematic evaluation capabilities of robots; supporting the innovative development of intelligent terminals such as smartphones, smart computers, smart glasses, and smart homes, and improving the pilot-scale testing service system. (Beike Finance)
AlphAi upgrades Polymarket's compatibility with prediction market experiences, adding AI analytics and real-time signals.
According to official Odaily, Web3 trading platform AlphAi has announced an upgrade to its Polymarket-compatible prediction market experience, further integrating prediction markets into the platform's trading ecosystem. This upgrade adds AI analytics, real-time market signals, structured navigation, and Crypto market categorization. Users can view AI reference analysis, social dynamics, Smart Money activities, and real-time event markers on supported prediction market pages to better understand the information flow, fund movements, and event progression behind changes in market probability. AlphAi stated that prediction markets are gradually evolving from simple order book access to a more intelligent, event-driven trading experience. The platform will continue to build a trading discovery and entry point for Web3 users, focusing on memes, prediction markets, crypto events, and other emerging trading scenarios.