Articles

The article details how ByteDance's DataMind project builds an "AI + Data" unified data engine based on Apache Doris. The DataMind project addresses the limitations of traditional solutions in handling unstructured data and integrating with AI models. It integrates hybrid search capabilities, including Faiss-based vector indexing and Tablet-level BM25 scoring functions. At the same time, it complements AI functions such as AI_QUERY and TEXT_EMBEDDING, and supports Python UDF to meet custom model requirements. The article also delves into the implementation of GraphRAG (Graph Retrieval-Augmented Generation) construction and query mechanisms on DataMind, enhancing complex knowledge retrieval capabilities. Finally, it shares the implementation of an enterprise AI-powered data query platform, effectively solving practical challenges such as massive data volume, security, and query latency by optimizing data agent routing and connecting data lake permission systems, providing a solid foundation for the application of AI in enterprise data consumption.

The article introduces LatentMAS, a multi-agent inference framework proposed by Princeton University and other institutions, aimed at addressing the issues of low efficiency and information loss in traditional text-based multi-agent systems. LatentMAS innovatively allows agents to collaborate directly in the latent space. This is achieved by exchanging hidden layer representations and KV-cache working memory, rather than relying on text communication. Experiments demonstrate that LatentMAS achieves the highest accuracy improvement of 14.6% compared to single models and traditional text-based MAS on nine benchmark tasks, including mathematics, scientific reasoning, and code generation, while reducing output token usage by 70.8–83.7% and accelerating end-to-end inference speed by 4–4.3 times. This framework requires no additional training, is versatile, compatible with any HuggingFace model, and reveals that latent thinking has richer and more expressive semantic capabilities than discrete tokens.

The article focuses on the implementation of H5 animations for Taobao Live's 2025 Double 11 flagship interactive game, 'Huahuale,' delving into its high-performance, high-availability, and maintainable practical application. The author begins by analyzing the business context and animation requirements, proposing a hybrid technical solution that combines React, Phaser, Spine, and Lottie. It particularly highlights the advantages of Spine animation and its application in complex deformations and interactions. The article comprehensively explains how red packet flight path animations achieve smooth effects by exporting frame-by-frame data via AE scripts and dynamic frame interpolation, as well as the layer management for break-through visual effects. More importantly, the author shares engineering assurance practices for animations in mega-sale scenarios, including material delivery standards, loading control, animation degradation strategies, exception scenario handling, and a comprehensive monitoring system, offering valuable experience for running complex H5 animations in large-scale production environments.

This article introduces Nexus, a novel AI Model architecture by Huawei Noah's Ark Lab, designed to overcome limitations of Transformers in complex reasoning. By introducing a Higher-Order Attention Mechanism, Nexus provides context-awareness and multi-hop reasoning via a recursive framework. A key innovation is the significant improvement in model performance on complex tasks like mathematics and science without increasing parameters. Results show Nexus provides stable gains whether training small models from scratch or modifying existing large models (e.g., Qwen2.5), highlighting the significance of architectural innovation over mere scaling of parameters.

This article centers on Tilly Norwood, the virtual actress developed by UK company Particle6 using AI technology. It details her generation process from initial concept to final image, including ChatGPT-assisted character design, the integrated application of multiple AI tools (such as Runway, Sora, and 11Labs), and substantial 'enhancement' investments made to achieve an 'inherent naturalness.' The article further examines how Tilly's emergence challenges the traditional entertainment industry, sparking controversies over actors' image rights, capital efficiency, and the evolving boundaries of artistic creation. Through historical comparison, the author highlights that technological progress subverting Hollywood is not unprecedented and envisions the new creative possibilities AI performers might bring.

This paper introduces the Self-Referenced Policy Optimization (SRPO) framework, proposed by teams from Fudan University, Tongji University, and Shanghai Institute for Intelligent Computing (SiiRL), aiming to address the high dependency on expert demonstration data and reward sparsity issues prevalent in VLA model reinforcement learning. SRPO incorporates an internally built self-reference evaluation mechanism that utilizes the model's own successfully generated trajectories to provide progressive rewards for failed attempts. This approach facilitates adaptive policy optimization without the need for external expert data or task-specific reward engineering. The framework achieved a State-of-the-Art (SOTA) success rate of 99.2% on the LIBERO benchmark and significantly boosted performance by 167% on the LIBERO-Plus generalization task. SRPO's core technical solutions encompass on-policy trajectory collection, trajectory clustering based on latent world representation, and the calculation of progressive rewards using trajectory distance, all integrated into a Proximal Policy Optimization (PPO)-style policy optimization objective. Experiments demonstrate that SRPO not only substantially improves training efficiency and generalization capabilities but also encourages robots to explore new strategies beyond pre-defined expert trajectories.

This article details how Figma Co-founder and CEO Dylan Field, after the IPO, led the company out of the shadow of the failed Adobe acquisition, evolving it from a single tool into a comprehensive platform encompassing Figma Design, FigJam, Slides, and the latest AI tool, Make. The article unfolds across six core dimensions: Firstly, Figma combats “corporate malaise” and maintains startup agility by squeezing out inefficiencies, guarding against path dependence, and establishing “Maker Week.” Secondly, through deep insight into user behavior and a “playful” product philosophy, it successfully expanded FigJam from a single tool into a multi-product matrix. Next, it delves into Figma's positioning for its AI tool Make and the lessons learned from its early failures, emphasizing quality and editability. Subsequently, it elaborates on Figma's “shorten time to value” product refinement philosophy, balancing foundational experience with product vision. Finally, Dylan Field shares his understanding of “taste” and predicts the rise of “product builders” in the AI-era workflow and the designer's new role transitioning from a visual specialist to an architect.

The article centers on the core concept of 'future industries,' incorporating valuable perspectives from numerous industry leaders and experts. It begins by defining future industries through their strategic, leading, disruptive, and uncertain characteristics. Subsequently, the text delves into cutting-edge insights across various fields, organized into eight main sections: an Overview of Future Industries, Future Energy, Future Materials, Future Health, Future Manufacturing, Future Space, Future Mobility, and Future Consumption. The 'Overview of Future Industries' section highlights AI as the core engine driving industrial transformation, discussing the distinctions between enterprise AI and personal AI, the challenges and opportunities in AI implementation, and the critical importance of computing power and data. Other sections explore specific advancements such as fourth-generation nuclear power, strategic electronic chemicals, biomaufacturing, AI-powered healthcare, embodied AI factories, the low-altitude economy, intelligent driving, and the application of AI recommendations in the consumer sector. Overall, the article aims to provide readers with a comprehensive and profound understanding of the future industrial landscape, illustrating how technological shifts are reshaping industry paradigms.

The article details the latest EyeReal glasses-free 3D display technology from a Chinese team appearing in Nature. By deeply integrating computational optics and artificial intelligence, this technology successfully overcomes the physical limitations of traditional glasses-free 3D displays in terms of screen size and viewing angle. EyeReal achieves a desktop monitor-sized 3D image without glasses, covering a wide viewing angle of over 100 degrees, and provides a continuous and smooth viewing experience. Its core innovation lies in the dynamic space-bandwidth product (SBP) utilization strategy, combined with real-time eye tracking, which precisely projects limited optical information to the observer's eyes. Technically, it transforms complex viewing angle changes into standardized planar images through physically accurate binocular geometric modeling and eye geometry encoding, and then uses a lightweight fully convolutional neural network (FCN) for reverse rendering to control the pixel phase of three common TFT-LCD panels, using Malus's Law to synthesize a precise light field. EyeReal achieves 'full parallax' and 'focus parallax', effectively solving the '3D sickness' problem that plagues users, and provides a low-cost, high-quality new solution for immersive experiences. The research was led by Ma Weijie, a doctoral student at Fudan University, and was jointly trained by institutions such as the Shanghai AI Laboratory.

The article explains the underlying mechanism of Vercel's Workflow Development Kit (WDK) framework integrations, emphasizing that WDK was designed to reflect Vercel's Open SDK Strategy, allowing developers to build with any framework. It highlights that despite diverse framework characteristics, all integrations follow a consistent two-phase pattern: build-time and runtime. During the build-time phase, WDK compiles workflow and step functions into executable handler files, encompassing bundling, output configuration, and compatibility patches. The runtime phase exposes these handlers as HTTP endpoints, with specific implementation varying across frameworks. A crucial component is WDK's SWC compiler plugin, which transforms code into client, step, and workflow modes. The article illustrates this with a SvelteKit integration example, showing how Vite plugins and esbuild handle transformations and handler generation. It also discusses how WDK adapts to frameworks with and without bundlers (using Nitro for the latter) and addresses challenges like converting framework-specific request objects. Additionally, it emphasizes the importance and implementation of hot module replacement. Finally, the article categorizes frameworks into file-based routing and HTTP server frameworks, demonstrating how a common pattern scales to provide durable workflows to a broad developer community without requiring framework migration.