智源发布 2026 十大 AI 技术趋势：世界模型成 AGI 共识方向

Core Viewpoint - The core viewpoint of the article is that a significant paradigm shift is occurring in artificial intelligence (AI), moving from a focus on language learning and parameter scale to a deeper understanding and modeling of the physical world, as highlighted in the 2026 AI technology trends report by the Beijing Zhiyuan Artificial Intelligence Research Institute [2][5]. Summary by Sections AI Technology Trends - The competition in foundational models is shifting from the size of parameters to the ability to understand how the world operates, marking a transition from "predicting the next word" to "predicting the next state of the world" [5][9]. - The year 2026 is identified as a critical turning point for AI, transitioning from the digital world to the physical world, driven by three main lines: cognitive paradigm elevation, embodiment and socialization of intelligence, and dual-track application value realization [8]. Key Trends - Trend 1: World Models and Next-State Prediction There is a consensus in the industry moving towards multi-modal world models that understand physical laws, with the NSP paradigm indicating AI's mastery of temporal continuity and causal relationships [9]. - Trend 2: Embodied Intelligence Embodied intelligence is moving from laboratory demonstrations to real industrial applications, with humanoid robots expected to transition to actual production and service scenarios by 2026 [10]. - Trend 3: Multi-Agent Systems The resolution of complex problems relies on multi-agent collaboration, with the standardization of communication protocols like MCP and A2A enabling agents to work together effectively [11]. - Trend 4: AI Scientists AI is evolving from a supportive tool to an autonomous researcher, significantly accelerating the development of new materials and drugs through the integration of scientific foundational models and automated laboratories [12]. - Trend 5: New "BAT" in AI The C-end AI super application is becoming a focal point for tech giants, with companies like OpenAI and Google leading the way in creating integrated intelligent assistants, while domestic players like ByteDance and Alibaba are also actively building their ecosystems [13]. - Trend 6: Enterprise AI Applications After a phase of concept validation, enterprise AI applications are entering a "disillusionment valley," but improvements in data governance and toolchains are expected to lead to measurable MVP products in vertical industries by the second half of 2026 [15]. - Trend 7: Rise of Synthetic Data As high-quality real data becomes scarce, synthetic data is emerging as a core resource for model training, particularly in fields like autonomous driving and robotics [16]. - Trend 8: Optimization of Inference Inference efficiency remains a key bottleneck for large-scale AI applications, with ongoing algorithmic innovations and hardware advancements driving down costs and improving energy efficiency [17]. - Trend 9: Open Source Compiler Ecosystem Building a compatible software stack for heterogeneous chips is crucial to breaking the monopoly on computing power, with platforms like Zhiyuan FlagOS aiming to create an open and inclusive AI computing foundation [18]. - Trend 10: AI Safety AI safety risks are evolving from "hallucinations" to more subtle "systemic deceptions," with various initiatives underway to enhance safety mechanisms and frameworks [19]. Conclusion - The Zhiyuan Research Institute emphasizes that the ten AI technology trends provide clear anchors for future technological exploration and industrial layout, aiming to promote a stable transition of AI towards value realization [21].