Large language models (LLMs) have demonstrated impressive capabilities and are receiving increasing attention to enhance their reasoning through scaling test-time compute. However, their application in open-ended, knowledge-intensive, complex reasoning scenarios is still limited. Reasoning-oriented methods struggle to generalize to open-ended scenarios due to implicit assumptions of complete world knowledge. Meanwhile, knowledge-augmented reasoning (KAR) methods fails to address two core challenges: 1) error propagation, where errors in early steps cascade through the chain, and 2) verification bottleneck, where the explore–exploit trade-off arises in multi-branch decision processes. To overcome these limitations, we introduce ARise, a novel framework that integrates risk assessment of intermediate reasoning states with dynamic retrieval-augmented generation (RAG) within a Monte Carlo tree search paradigm. This approach enables effective construction and optimization of reasoning plans across multiple maintained hypothesis branches. Experimental results show that ARise significantly outperforms the state-of-the-art KAR methods by up to 23.10%, and the latest RAG-equipped large reasoning models by up to 25.37%. Our project page is at https://opencausalab.github.io/ARise.

Ray-tracing through numerical weather models (NWMs) is one of the most accurate methods for determining slant tropospheric delays (STDs) in microwave remote sensing. However, the massive data volumes of high-resolution NWMs create substantial I/O operations, limiting large-scale ray-tracing on general hardware. This constraint has historically necessitated parameterized tropospheric delay models, which are disseminated as standardized products (e.g., zenith delays with mapping functions and horizontal gradients). Recently, the AI-driven VAE-former algorithm revolutionized NWM compression, achieving >470:1 ratios by compressing 37 pressure level, 0.25°×0.25° ERA5 data into files smaller than surface-only VMF3 products (1°×1° resolution). This breakthrough challenges the conventional reliance on parameterized models as the sole practical solution. We quantified discrepancies in tropospheric delay parameters between original ERA5 and VAEformer-compressed CRA5 data across 2022, evaluating compression fidelity on global grids and against in-situ zenith tropospheric delay (ZTD) estimates. Results show global average precision loss from compression is10 mm). Our findings demonstrate CRA5 as a reliable ERA5 substitute, with compression-induced inaccuracies being negligible for most microwave-based remote sensing applications. This work underscores that parameterized delay modeling is no longer the exclusive pathway, enabling efficient local computation of high-precision STDs without through mapping functions and gradients.

The pseudolite positioning system can enhance Global Navigation Satellite System (GNSS) by improving satellite geometry and providing independent services in environments where GNSS is unavailable. This paper investigates a GNSS/Pseudolite integrated positioning system and verifies its positioning service performance in urban canyon environments. The experimental results indicate that in challenging urban canyon environments, the number of visible satellites for the GNSS significantly decreases, leading to a substantial decline in positioning accuracy. The GNSS/Pseudolite integrated system demonstrates its advantages. Even in favorable experimental conditions, compared to using GNSS PPP alone, the GNSS/Pseudolite integrated PPP improves horizontal accuracy by 10% and 3D accuracy by 12%. When the obstruction is severe, such as when the cut-off elevation angle is 50°, the GNSS/Pseudolite integrated PPP improves horizontal accuracy by ${7 2. 5 \%}$ and 3D accuracy by 79.2% compared to GNSS PPP. In challenging urban canyon environments, the GNSS/Pseudolite integrated system can still provide high-precision positioning services.

Accurate tropospheric delay forecasts are imperative for microwave-based remote sensing techniques, playing a pivotal role in early warning and forecasting of natural disasters such as tsunamis, heavy rains, and hurricanes. Nevertheless, conventional methods for forecasting tropospheric delays entail substantial computational resources and high network transmission speeds, thereby restricting their real-time applicability in remote sensing operations. In this study, we introduce a novel approach to derive forecasted tropospheric delays using artificial intelligence (AI) weather forecast foundation models (FMs), exemplified by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. We assess the accuracy of these forecasts on a global scale employing fifth-generation ECMWF atmospheric re-analysis of the global climate (ERA5) (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5), ground-based Global Navigation Satellite System (GNSS), and in situ radiosonde (RS) measurements as reference data. Our results show that the FM-based scheme outperforms traditional methods in both forecast accuracy and length, with the ability to provide high-accuracy tropospheric delay parameters locally for 15-day forecasts at any location within minutes. Furthermore, the FM scheme still maintains accuracy better than empirical models when forecasting up to ten days in advance. This research demonstrates the potential of AI weather forecast FMs in delivering high-precision tropospheric delay medium-range forecasts and improvements for real-time remote sensing applications.

We consider DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) low-pass visible light communication (VLC) systems in which water-filling and uniform power loading strategies have almost the same rate performance. However, conventional bit loading based on either water-filling or uniform power loading cannot reach the maximum achievable rate because the number of bits loaded per subcarrier can only be integers. In this paper, we apply pairwise coding (PWC) to solve this problem by performing joint coding on pairs of subcarriers to balance their symbol error rate (SER) performance. We present a closed-form expression for the optimal parameter of PWC and an approximation of its theoretical performance, and propose an enhanced bit loading scheme with PWC to improve the system rate. Numerical results suggest that the enhanced scheme has a significant rate improvement and approaches the maximum achievable rate for moderate to high signal-to-noise ratio (SNR).