Ant Financial's LingBot released its spatial perception model, LingBot-Depth 2.0.
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LingBot-VLA 2.0, the ant-like Lingbo embodiment base model, is open source.
On July 8th, Antminer Technology announced the upgrade and open-sourcing of its next-generation embodied platform model, LingBot-VLA 2.0. As a comprehensive upgrade to the open-source version LingBot-VLA 1.0 released in January of this year, LingBot-VLA 2.0 incorporates 60,000 hours of high-quality real-world physical data during the pre-training phase, covering 20 robot configurations from 17 mainstream robot brands, and expanding support for degrees of freedom such as the head, waist, end effector, and mobile chassis. This results in significant improvements in configuration generalization, degree of freedom support, and deployment efficiency.
Liu Liehong: The release of industry data value has entered a new stage of in-depth advancement, and the National Data Administration will work with relevant departments to introduce targeted and specific measures.
Mars Finance reported on July 7th that Liu Liehong, Secretary of the Party Leadership Group and Director of the National Bureau of Data, attended and addressed the opening ceremony of the "2026 Global Digital Economy Conference" and the "Data Element Development Forum" held in Beijing from July 2nd to 3rd. Liu Liehong stated that with the implementation of the "531" work system, the potential of data elements is being released at an accelerated pace, and data circulation and utilization are showing new trends. First, the release of industry data value has entered a new stage of in-depth advancement. Enterprises should focus on the "three main battlefields" to realize their own value in deepening the circulation and utilization of industry data; local governments should focus on creating a strong data atmosphere to empower industrial transformation and promote high-quality economic development through data; the National Bureau of Data will work with relevant departments to introduce targeted and specific measures. Second, the solutions to key problems are becoming clearer. For example, adapting to the diverse data needs of artificial intelligence and solving the problems of "dispersed system construction, inconsistent standards, and difficulty in data sharing," the practice of using "technology empowerment" such as intelligent agent invocation to resolve the pain point of "data dispersion," and using "semantic unification" to solve the problem of "inconsistent formats" has begun to show results. Third, data empowerment for the innovative development of artificial intelligence is continuously deepening. On the supply side, we will accelerate the construction of a high-quality dataset supply system for the industry; on the circulation side, we will systematically optimize data usage rules and circulation models for artificial intelligence; on the application side, we will drive the data flywheel to accelerate through "analog-data resonance". (National Data Administration website)
Anthropic: The Claude model contains elements similar to "conscious human thought."
BlockBeats reported on July 7th that Anthropic released a new research report discovering a spontaneously generated internal "global working space" called J-space (Jacobian space) within the Claude model. This is a dedicated set of neural activation patterns for the model to engage in silent thinking, allowing it to process concepts without writing them down, similar to conscious thought that humans can report. The research used Jacobian technology to identify neural activation patterns in the J-space, enabling it to read unspoken concepts and modify these patterns. Experiments showed that disabling the J-space weakens multi-step reasoning abilities but does not affect basic tasks or factual recall. Click the original link below to join the Beating · Lark AI news channel for 24/7 monitoring of global AI hotspots and news.
Financial AI operates outside of regulation; the UK's FCA plans to expand its jurisdiction over AI giants such as OpenAI and Anthropic.
According to Beating's monitoring, Sheldon Mills, Executive Director of the UK Financial Conduct Authority (FCA), warned that regulators are facing an "arms race" to keep pace with the rapid adoption of AI in the financial services industry as businesses and individuals accelerate their adoption. Mills' report on the financial impact of AI indicates that 20% of UK adults are already willing to let large models make their savings or borrowing decisions. While this service offers an experience equivalent to regulated traditional financial advice, its lack of regulatory oversight means users are unable to obtain any financial compensation when they suffer losses. The report recommends an urgent review of the risks of unregulated financial AI and an application for expanded legislative authorization to strengthen oversight of core technology providers such as Anthropic, OpenAI, Amazon, Google, and Microsoft through a "key third party" mechanism (the UK government has not yet finalized the specific list). It also recommends collaboration to launch free public financial literacy and decision-making guidance services assisted by AI.
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)
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.