Global large language model capabilities are projected to advance significantly in 2025, progressively conquering productivity scenarios with notable improvements in reasoning, programming, agentic functions, and multimodal capacities, though shortcomings persist in stability and hallucination rates for general-purpose applications. Looking ahead to 2026, further breakthroughs are anticipated in reinforcement learning, model memory, and context engineering, transitioning from short-context generation to extended chain-of-thought tasks and from text-based interaction to native multimodal capabilities, bringing the field closer to achieving the long-term objective of Artificial General Intelligence.

The institution anticipates a resurgence of pre-training scaling laws in 2026, with flagship models reaching new parameter count milestones. Architecturally, Transformer-based frameworks will continue to dominate while Mixture-of-Experts approaches balancing performance and efficiency gain consensus, with various attention mechanisms undergoing ongoing optimization and transitions. The paradigm shift will involve pre-training phases where scaling laws, high-quality data, and reinforcement learning collectively enhance model capabilities, with expectations that Nvidia's GB-series chip maturation will enable superior performance through 10,000-card clusters during pre-training, thereby elevating both parameter scales and intelligence ceilings.

Reinforcement learning is gaining prominence as the critical enabler for unlocking advanced model capacities, elevating intelligence thresholds by enabling more logical, human-preference-aligned reasoning through self-generated data and multi-iteration processes that fundamentally depend on massive computing power and premium data quality. Leading international developers including OpenAI and Gemini prioritize reinforcement learning, with domestic counterparts like DeepSeek and Alibaba's Tongyi Qianwen following suit, projecting increased reinforcement learning integration among global model developers throughout 2026.

Emerging pathways including continual learning, model memory, and world models are poised for fundamental breakthroughs addressing catastrophic forgetting through selective memory mechanisms. Algorithms and architectures such as Google's Titans, MIRAS, and Nested Learning enable dynamic adjustment of learning and memory based on task duration and significance, facilitating continual and potentially lifelong learning. Additionally, world models focusing on physical world causality understanding present breakthrough opportunities through explorations like Genie 3 and Marble across different modeling approaches.

Potential risks include slower-than-expected technological iteration and potential disruption of existing model architectures and training paradigms.