What could possibly go wrong? The 4-foot-tall humanoid robot that's in front of me seems, quite honestly, a bit drunk. After 30 seconds or so it abruptly stops, then strides toward me with an arm outstretched. The little robot is at the World Artificial Intelligence Conference, on the banks of the Huangpu river in Shanghai. The convention center is teeming with humanoids --dancing ones, box-toting ones, robot dog-walking ones doing circuits around trade show booths. A few lie slumped in a corner as their batteries recharge. A Unitree humanoid robot modified for experimental purposes at the BAAI.

LLMscape is an interactive installation that investigates how humans and AI construct meaning under shared conditions of uncertainty. Within a mutable, projection-mapped landscape, human participants reshape the world and engage with multiple AI agents, each developing incomplete and provisional accounts of their environment. Exhibited in Shanghai and continually evolving, the work positions AI not as deterministic tools but as embodied co-witnesses to an unstable world, examining the parallels between human and artificial meaning-making and inviting reflection on our shared epistemic limits.

The words in red are the data processing steps. The overall procedure of UDL data processing is shown in Figure 1 .

However, several challenges are posed.

Accurate and robust weather forecasting remains a fundamental challenge due to the inherent spatio-temporal complexity of atmospheric systems. In this paper, we propose a novel self-supervised learning framework that leverages spatio-temporal structures to improve multi-variable weather prediction. The model integrates a graph neural network (GNN) for spatial reasoning, a self-supervised pretraining scheme for representation learning, and a spatio-temporal adaptation mechanism to enhance generalization across varying forecasting horizons. Extensive experiments on both ERA5 and MERRA-2 reanalysis datasets demonstrate that our approach achieves superior performance compared to traditional numerical weather prediction (NWP) models and recent deep learning methods. Quantitative evaluations and visual analyses in Beijing and Shanghai confirm the model's capability to capture fine-grained meteorological patterns. The proposed framework provides a scalable and label-efficient solution for future data-driven weather forecasting systems.

Chain-of-thought (CoT) rationales, which provide step-by-step reasoning to derive final answers, benefit LLMs in both inference and training. Incorporating rationales, either by generating them before answering during inference, or by placing them before or after the original answers during training - significantly improves model performance on mathematical, symbolic and commonsense reasoning tasks. However, most work focuses on the role of rationales in these reasoning tasks, overlooking their potential impact on other important tasks like natural language understanding (NLU) tasks. In this work, we raise the question: Can rationales similarly benefit NLU tasks? To conduct a systematic exploration, we construct NLURC, a comprehensive and high-quality NLU dataset collection with rationales, and develop various rationale-augmented methods. Through exploring the applicability of these methods on NLU tasks using the dataset, we uncover several potentially surprising findings: (1) CoT inference shifts from hindering NLU performance to surpassing direct label prediction as model size grows, indicating a positive correlation. (2) Most rationale-augmented training methods perform worse than label-only training, with one specially designed method consistently achieving improvements. (3) LLMs trained with rationales achieve significant performance gains on unseen NLU tasks, rivaling models ten times their size, while delivering interpretability on par with commercial LLMs.

Climate change is amplifying extreme weather and climate events worldwide [1]. Anthropogenic greenhouse gas emissions have disrupted the Earth's climate system, driving more frequent and severe heatwaves [2], cold spells [3], heavy precipitation [4], agricultural droughts [5], and tropical cyclones (TCs) [6]. Between 2016 and 2024, daily land temperature records show that extreme heat events occurred over four times more often than expected, while cold records declined by half [7]. These unprecedented shifts threaten human health [8, 9], infrastructure [10, 11], food security [12], biodiversity [13], and global economies [14, 15]. Therefore, reliable climate projections are essential for effective mitigation and adaptation strategies [16-18]. The Coupled Model Intercomparison Project (CMIP) [19] provides a foundation for global climate projections. Since its launch in 1995, CMIP has coordinated systematic evaluation of coupled general circulation models (GCMs). CMIP5 introduced Representative Concentration Pathways (RCPs), while CMIP6 extended this framework by incorporating Shared Socioeconomic Pathways (SSPs) through ScenarioMIP, enabling consistent simulations of emissions and socioeconomic trajectories to 2100 and facilitating integrated assessment of climate risks [20]. These advances have greatly enhanced the scientific and policy relevance of climate projections.

Solar energy adoption is critical to achieving net-zero emissions. However, it remains difficult for many industrial and commercial actors to decide on whether they should adopt distributed solar-battery systems, which is largely due to the unavailability of fast, low-cost, and high-resolution irradiance forecasts. Here, we present SunCastNet, a lightweight data-driven forecasting system that provides 0.05$^\circ$, 10-minute resolution predictions of surface solar radiation downwards (SSRD) up to 7 days ahead. SunCastNet, coupled with reinforcement learning (RL) for battery scheduling, reduces operational regret by 76--93\% compared to robust decision making (RDM). In 25-year investment backtests, it enables up to five of ten high-emitting industrial sectors per region to cross the commercial viability threshold of 12\% Internal Rate of Return (IRR). These results show that high-resolution, long-horizon solar forecasts can directly translate into measurable economic gains, supporting near-optimal energy operations and accelerating renewable deployment.