Articles

This article details ByteDance's Doubao video generation model, Seedance 1.5 Pro, whose core innovation lies in achieving "native audio-video co-generation" rather than the traditional method of generating video and then dubbing it. This resolves common lip-sync mismatches and audio-visual asynchrony. The article delves into the model's dual-branch Diffusion Transformer architecture, its robust data processing strategy (including filtering, professional labeling, and curriculum learning scheduling), and a three-step training process (pre-training, SFT, and RLHF). Additionally, it highlights how inference speed is improved by over 10 times through distillation, quantization, and parallel computing techniques. The article concludes by comparing Seedance 1.5 Pro's video and audio capabilities against other models, specifically its strong performance in the Chinese context, and previews the upcoming Draft Preview feature designed to enhance creative efficiency and reduce costs. The article uses accessible language to ensure it is easily understood by a broad audience.

The article delves into Don Norman's foundational principles of interaction design, emphasizing how products should be intuitively understandable without needing instructions. It introduces six key concepts: Affordances, which communicate possible actions; Signifiers, providing clues on how and where to act; Constraints, preventing incorrect use; Mappings, ensuring logical control-to-result relationships; Feedback, indicating action success; and Conceptual Models, forming a user's mental understanding of how a product works. By applying these principles, the article contends that product design can make technology more user-friendly, reduce errors, and enhance user confidence, ultimately making everyday life smoother for everyone.

This article provides an in-depth analysis of OpenAI CEO Sam Altman's core perspectives from a recent interview. He firmly believes OpenAI has not been surpassed by competitors like Google; instead, it is forging a 'system-wide dominance' through a robust closed-loop system comprising models, interfaces, and infrastructure. Altman exhibits disdain for OpenAI's IPO prospects, asserting that maintaining private status is crucial for pursuing 'superintelligence.' He criticizes Google's strategy of awkwardly integrating AI into existing products, predicting a future dominated by new 'AI-first' product paradigms. He discloses OpenAI's strategic pivot towards enterprise-level services, citing the GDP-val benchmark which indicates AI has reached expert proficiency in 74.1% of knowledge work tasks. He further forecasts that humans will transition into 'AI managers,' exploring the profound implications for employment and future societal models. Altman also stresses that trillions in computing power investment will catalyze scientific discoveries and redefines the ambiguous AGI concept as a more formidable 'superintelligence,' signifying AI's enhanced capability to handle complex management roles.

This article provides an in-depth comparative review of OpenAI's GPT Image 1.5 and Google's Banana Pro, two major image generation models. The author conducts detailed tests across four dimensions: information accuracy (especially regarding text and Chinese processing), realistic texture, precise editing capability, and world knowledge understanding, accompanied by numerous comparison images for intuitive display. The test results indicate that GPT Image 1.5 performs poorly in Chinese text generation and significantly lags behind Banana Pro in information accuracy. In terms of realistic texture, images generated by GPT Image 1.5 appear slightly 'AI-like,' exhibiting an unnatural sheen, while Banana Pro is more natural. For precise editing capability, Banana Pro demonstrates more stable performance in light and shadow, proportion, and overall art style consistency, whereas GPT Image 1.5 often results in incongruous effects. In world knowledge understanding, both have their own merits, but GPT Image 1.5 occasionally shows significant knowledge errors. The article ultimately concludes that GPT Image 1.5 still fails to surpass Banana Pro in many aspects, affirming Google's rapid iteration and leading position in the AI image generation domain.

The article is a compilation of several news items and an editorial on the state of LLMs. The editorial by Andrew Ng argues that while LLMs are more general than previous AI, they still lack human-level generality and require laborious data-centric AI approaches for specific tasks. It highlights the need for continued hard work to build more intelligent models. Following this, the article introduces Runway's GWM-1 family of general world models for real-time, coherent video generation across different applications like simulated worlds, robotics, and conversational avatars. It then covers OpenAI's launch of GPT-5.2 models (Pro, Thinking, Instant) to address competitive pressures, showcasing their improved performance in reasoning benchmarks and significantly lower computational costs. Finally, it details SEMI, a sample-efficient method using a projector and LoRA adapters to adapt LLMs to new data types and specialized domains with minimal examples, greatly reducing the data burden for multimodal integration.

The article centers on GUI Agent, a cutting-edge technology, highlighting its core capability: enabling AI to operate digital interfaces through visual understanding, mimicking human interaction. With the release of ByteDance's Doubao Mobile Assistant, StepFun's Step-GUI Edge, and Zhipu AutoGLM, GUI Agent has become a crucial path to break down application barriers. The article delves into the necessity of its "Edge-Cloud Collaboration" paradigm, where small parameter models handle screen operations and privacy protection on the edge, while large models on the cloud are responsible for complex logical reasoning. This model ensures performance while balancing data security and complex task processing. Subsequently, through detailed real-world case studies (WeChat's 'politely declining loan requests', Xiaohongshu likes and comments, Kuaishou Express Edition earning rewards and cashing out), it vividly demonstrates Step-GUI's powerful capabilities and judgment in complex real-world scenarios, such as recognizing loading states, handling pop-ups, and understanding business logic. The article also specifically argues for GUI Agent's universality as an 'off-road vehicle' for non-standard operation interfaces without APIs, and emphasizes that Step-GUI, combined with the MCP Protocol, can transform AI from an independent tool into a bridge connecting LLMs with mobile applications, significantly enhancing AI's application potential and deployment convenience in mobile automation.

Zhidx.com's article offers a detailed analysis of the AI glasses market's current state and future trajectory. Through interviews with numerous industry leaders and experts, it uncovers diverse perspectives on major companies entering the AI glasses arena. While some startups perceive it as a 'dead end', analysts suggest it fosters market prosperity. The article highlights that despite the entry of big players like Xiaomi and Alibaba, startups such as Rokid, RayNeo, and INMO are also performing robustly, with sales simultaneously soaring, collectively propelling explosive market growth. Currently, the industry generally operates at a loss for market penetration, facing key challenges like an immature supply chain, a lack of specialized chip solutions, and high R&D costs. To counter potential fierce future competition, startups are largely pursuing differentiation strategies, building competitive moats through technological innovation, deep cultivation of vertical scenarios, and refined channel development, aiming for differentiated competition against larger corporations. The article concludes by deliberating when AI glasses might experience their 'iPhone moment,' emphasizing the need to resolve technical pain points, expand core scenarios, and enhance user education.

This article details Xiaomi's latest open-source large model, MiMo-V2-Flash, with 309B total parameters (15B active), positioned as an efficient inference, code, and Agent foundation model. Its core innovations include the adoption of Hybrid Attention architecture (mixing Sliding Window Attention and Global Attention) and Multi-Token Prediction (MTP) inference acceleration technology, significantly boosting inference efficiency and throughput while reducing costs. In multiple Agent benchmarks, MiMo-V2-Flash ranks among the global top 2 open-source models, and its code capabilities surpass all other open-source models, comparable to the closed-source model Claude 4.5 Sonnet, yet with only 2.5% of Claude 4.5 Sonnet's inference price and 2x faster speed. The article also introduces the new post-training paradigm, Multi-Teacher On-Policy Distillation (MOPD), which can achieve teacher model performance with extremely low resources. Model weights and inference code have been comprehensively open-sourced, with a limited-time free API and Web Demo experience available.

The article provides a detailed analysis of MiniMax's HILUO Video Team's first open-source project, VTP (Visual Tokenizer Pre-training), focusing on addressing a counter-intuitive phenomenon in visual generation: over-training the visual tokenizer leads to degraded generation performance, a problem termed the “pre-training scaling problem.” The article points out that traditional tokenizer training emphasizes pixel reconstruction, causing models to overemphasize low-level information rather than the high-level semantics required for generation. VTP jointly optimizes the tokenizer by combining three training objectives: image-text contrastive learning (leveraging CLIP for image semantic understanding), perceptual spatial structure (leveraging DINOv2 to learn intrinsic image features and structural relationships), and low-weight pixel reconstruction. This enables the tokenizer to simultaneously understand image content, perceive spatial structure, and retain pixel details. Furthermore, VTP adopts the Vision Transformer (ViT) architecture to improve efficiency and effectiveness. Experimental results show that VTP breaks through the performance ceiling of traditional methods, continuously improving generation performance with increasing data, computational power, and parameter scaling. It surpasses existing SOTA methods in understanding, reconstruction, and generation capabilities, while also significantly accelerating convergence. This provides new optimization directions and ideas for future large model visual generation.

The article thoroughly reviews the development of the AI industry in 2025, highlighting that the global large model landscape presents a China-US "dual-core driven" dynamic, with both the rise of open-source models (represented by DeepSeek) and competition with closed-source models (OpenAI, Anthropic, Google) in terms of technical paths. Regarding technological evolution, native multi-modal integration, reasoning capability, long context and memory mechanisms, and agent capabilities have become key trends. The article also emphasizes the importance of Physical AI and World Models as a new paradigm of intelligence, and analyzes the slow shift of the industrial chain from a "single winner" to "multi-polar coexistence", and the "software-hardware dual-core" ecosystem built by NVIDIA and OpenAI. Finally, the article discusses the "Generative AI chasm", commercialization challenges, and "software-hardware integration" innovation paths faced by AI applications, and offers an outlook for continuous AI learning, the productivity paradox, and changes in investment logic in 2026.