UK-based supermarket chain Sainsbury’s posted stronger third-quarter grocery sales and held its full-year profit outlook, despite weaker performance in general merchandise and Argos. In the 16 weeks to 3 January 2026, total retail sales excluding fuel increased 3.9% year-on-year to £10.02bn ($13.43bn) Total retail sales, including fuel, climbed to £11.12bn, up from £10.76bn in the same quarter last year. Grocery sales advanced 5.4% over the period while general merchandise and clothing declined 1.1%. ...

Core Insights - The article discusses the concept of world models in embodied AI, emphasizing their role as internal simulators that help agents perceive environments, take actions, and predict future states [1][2]. Group 1: World Models Overview - The research on world models has seen unprecedented growth due to the explosion of generative models, leading to a complex array of architectures and techniques lacking a unified framework [2]. - A novel three-axis classification method is proposed to categorize existing world models based on their functionality, temporal modeling, and spatial representation [6]. Group 2: Mathematical Principles - World models are typically modeled as partially observable Markov decision processes (POMDPs), focusing on learning compact latent states from partial observations and the transition dynamics between states [4]. - The training paradigm for world models often employs a "reconstruction-regularization" approach, which encourages the model to reconstruct observations from latent states while aligning posterior inference with prior predictions [9]. Group 3: Functional Positioning - World models can be categorized into decision-coupled and general-purpose types, with the former optimized for specific decision tasks and the latter serving as task-agnostic simulators [6][15][16]. - Decision-coupled models, like the Dreamer series, excel in task performance but may struggle with generalization due to their task-specific representations [15]. - General-purpose models aim for broader predictive capabilities and transferability across tasks, though they face challenges in computational complexity and real-time inference [16]. Group 4: Temporal Modeling - Temporal modeling can be divided into sequential reasoning and global prediction, with the former focusing on step-by-step simulation and the latter predicting entire future sequences in parallel [20][23]. - Sequential reasoning is beneficial for closed-loop control but may suffer from error accumulation over long predictions [20]. - Global prediction enhances computational efficiency and reduces error accumulation but may lack detailed local dynamics [23]. Group 5: Spatial Representation - Various strategies for spatial representation include global latent vectors, token feature sequences, spatial latent grids, and decomposed rendering representations [25][28][34][35]. - Global latent vectors compress scene states into low-dimensional variables, facilitating real-time control but potentially losing fine-grained spatial information [28]. - Token feature sequences allow for detailed representation of complex scenes but require extensive data and computational resources [29]. - Spatial latent grids maintain local topology and are prevalent in autonomous driving, while decomposed rendering supports high-fidelity image generation but struggles with dynamic scenes [34][35]. Group 6: Data Resources and Evaluation Metrics - Data resources for embodied AI can be categorized into simulation platforms, interactive benchmarks, offline datasets, and real robot platforms, each serving distinct purposes in training and evaluating world models [37]. - Evaluation metrics focus on pixel-level generation quality, state/semantic consistency, and task performance, with recent trends emphasizing physical compliance and causal consistency [40].

Summary of VERSES (VRSS.D) Update / Briefing July 29, 2025 Company Overview - **Company**: VERSES (VRSS.D) - **Focus**: Development of AI and robotics technology, particularly in real-world applications and enterprise solutions Key Points Uplist Plans - The company is actively pursuing an uplist to a major tier one US exchange, such as Nasdaq or the New York Stock Exchange, to access larger pools of capital and greater liquidity [3][4][6] - A reverse split is required to meet minimum share price requirements for listing on these exchanges [3] - Transitioned from a foreign private issuer to a domestic SEC reporting company, which comes with different public disclosure requirements [4] Corporate Strategy and Technology Development - The company is focused on creating real-world intelligence through AI, addressing the limitations of current AI technologies that struggle with practical applications [14][15][18] - Emphasis on developing enterprise-ready solutions that can handle mission-critical applications, such as real-time sensing and modeling of environments [19][22] - The company has made significant advancements in its technology, including the development of Genius, a platform designed for adaptive real-time learning [32][35] Product Innovations - **Acxiom**: A cognitive architecture that demonstrates superior performance in spatial reasoning and real-time learning, outperforming competitors like Google's models [35][37] - **VBGS**: A new machine vision technology that allows for real-time mapping and sensing of environments, significantly improving accuracy and efficiency [40][41] - **Genius Act**: A robotic simulation that showcases the ability of robots to learn and adapt in real-time, achieving tasks without extensive pre-training [44][46] Market Position and Recognition - The company has gained attention from various media outlets and industry experts, indicating a growing recognition of its technology and potential [75] - Partnerships with notable organizations, including NASA and JPL, highlight the credibility and applicability of its technology in real-world scenarios [74] Customer Adoption and Revenue Potential - Strong adoption of the Genius platform across diverse industries, including CRM, banking, and media, with customers acting as both users and potential resellers [67][70] - The company is optimistic about future revenue growth as it transitions from research to revenue-generating phases [72][75] Future Outlook - The company believes it is at a pivotal moment, with the potential to significantly impact the AI and robotics market by solving complex real-world problems [89][90] - Continued focus on developing systems that can operate intelligently in dynamic environments, aiming to lead in the enterprise and industrial sectors [66][87] Additional Important Insights - The company acknowledges the challenges faced during the transition period but emphasizes the strength and resilience of its team and technology [5][6] - The strategic focus on real-world applications of AI and robotics positions the company uniquely in a market that is increasingly recognizing the limitations of traditional AI approaches [14][18][63]