Core Viewpoint - The release of Kimi K2 marks a significant turning point for the company, indicating a shift from a reliance on scaling laws to a more innovative approach in AI model development and strategy [2][4][22]. Group 1: Kimi K2 Release and Its Impact - Kimi K2 achieved a global fifth ranking in the LMArena leaderboard and first among open-source models, surpassing competitors like Claude 4 and DeepSeek-R1-0528 [2]. - The release is seen as more than just a temporary success; it represents a deeper strategic shift for the company and the industry [4][22]. - Kimi K2 introduces two major advancements: an expansion of model parameters to over 1 trillion and the concept of "model as agent," allowing for tool utilization [23][35]. Group 2: Challenges Faced by Kimi - Kimi's previous strategy relied heavily on scaling laws, believing that larger models and more data would lead to better performance, but this approach faced challenges as high-quality data became scarce [8][13][14]. - The company's user growth strategy was questioned after competitors like DeepSeek demonstrated significant user acquisition without marketing spend, highlighting the need for a more effective product [18][54]. - Kimi's marketing budget reached approximately 900 million RMB in 2024, yet user engagement declined, indicating a disconnect between spending and user retention [17]. Group 3: Strategic Transformation - The company has shifted its focus from aggressive marketing to enhancing model performance and embracing open-source collaboration, reflecting a significant cultural change [55]. - Kimi's team has decided to halt all marketing activities and concentrate resources on foundational algorithms and the K2 model, emphasizing the importance of product quality over quantity [55]. - The strategic pivot is seen as a response to the success of DeepSeek, which has prompted Kimi to adopt more effective architectural choices and prioritize technical research [55][56].

Core Viewpoint - Kimi K2 has emerged as a leading open-source model, showcasing significant advancements in capabilities, particularly in code, agent tasks, and mathematical reasoning [4][5]. Group 1: Technical Highlights - Kimi K2 features a total parameter count of 1 trillion and 32 billion active parameters, demonstrating its advanced capabilities [4]. - The model has achieved state-of-the-art (SOTA) performance in various benchmark tests, including SWE Bench Verified, Tau2, and AceBench [12]. - The Kimi team emphasizes a shift from static imitation learning to Agentic Intelligence, requiring models to autonomously perceive, plan, reason, and act in complex environments [9][10]. Group 2: Core Innovations - Three core innovations are implemented in Kimi K2: 1. MuonClip optimizer, which replaces traditional Adam optimizer, allowing for lossless spike pre-training on 15.5 trillion tokens [11]. 2. Large-scale Agentic Tool Use data synthesis, enabling the generation of multi-turn tool usage scenarios across hundreds of domains and thousands of tools [12]. 3. A universal reinforcement learning framework that extends alignment from static to open domains [12]. Group 3: Pre-training and Post-training Phases - During the pre-training phase, Kimi K2 optimizes both the optimizer and data, utilizing the MuonClip optimizer to enhance training stability and efficiency [21][22]. - The training data covers four main areas: web content, code, mathematics, and knowledge, all subjected to strict quality screening [24]. - The post-training phase involves supervised fine-tuning and reinforcement learning, with a focus on generating high-quality training data through a rejection sampling mechanism [30][31]. Group 4: Reinforcement Learning Process - The reinforcement learning process includes creating verifiable reward environments for objective evaluation of model performance [33]. - A self-critique reward mechanism is introduced, allowing the model to evaluate its outputs based on predefined standards [34]. - The model generates diverse agentic tasks and tool combinations, ensuring a comprehensive training approach [35]. Group 5: Infrastructure and Performance - Kimi K2's training relies on a large-scale high-bandwidth GPU cluster composed of NVIDIA H800, ensuring efficient training across various resource scales [38]. - Each node is equipped with 2TB of memory, facilitating high-speed interconnectivity among GPUs [39].

Core Insights - VERSES AI Inc. and Analog are expanding their collaboration on Smart City projects, focusing on energy management, urban robotics, and edge-AI applications following a successful pilot program [1][4] - The Genius platform demonstrated a 32% increase in completed rides during the pilot, indicating significant potential for revenue growth for fleet operators [2][3] - The collaboration aims to integrate Genius into Analog's secure edge infrastructure, enabling real-time decision-making and data control for Smart City applications [4][5] Company Overview - VERSES AI Inc. specializes in cognitive computing and next-generation agentic software systems, with its flagship product, Genius, designed for reliable predictions and decisions under uncertainty [6] - Analog, founded in 2024, focuses on adaptive intelligence and edge computing, aiming to create intelligent systems that enhance human connection and experience [7] Future Prospects - The partnership will explore higher-value Smart City projects, including logistics, autonomous inspection robots, and city-scale sensor fusion, leveraging Genius' capabilities [4][5] - Significant investments are being made in Smart City initiatives, particularly in the Middle East, with expectations for growth in autonomous vehicles, sensors, and energy management systems [5]

Launch to Drive Enterprise Customer Acquisition, Product Adoption and Revenue GrowthVANCOUVER, British Columbia, April 30, 2025 (GLOBE NEWSWIRE) -- VERSES AI Inc. (CBOE:VERS) (OTCQB:VRSSF) ("VERSES'' or the "Company”) a cognitive computing company specializing in next-generation intelligent software systems is pleased to announce that the Company has officially launched its flagship product Genius enabling Agentic Intelligence for Enterprise. The Company will offer Genius as a paid service with consumption- ...