Neural scene reconstruction has become an important tool for building digital environments used in robotics, autonomous systems, and physical AI training. However, NeRF-based reconstruction often requires high computational cost and does not directly produce simulation-ready mesh assets. 3D Gaussian Splatting offers a faster alternative by representing a captured scene as an explicit cloud of Gaussian primitives that can be rendered in real time. This paper proposes a practical pipeline that converts 3D Gaussian Splatting outputs into mesh-based assets and exports them into NVIDIA Omniverse and Isaac Sim workflows via OpenUSD. The proposed method extracts geometry from the Gaussian primitive cloud, reconstructs a watertight or simulation-usable mesh, bakes appearance information into textures and materials, and exports the result as an OpenUSD-compatible asset with physics and semantic metadata. The goal is to lower the cost of building realistic training environments while preserving visual and geometric fidelity sufficient for simulation, synthetic data generation, and robot learning. The paper also outlines a future extension toward live conversion, where streamed captures are incrementally transformed into simulation-ready scene updates.

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The space-air-ground integrated network offers a forward-looking direction for alleviating ground computing power bottlenecks. Efficiently deploying large, distributed models, a key technology for 6G ubiquitous intelligence, in highly dynamic, resource-heterogeneous environments holds significant strategic and application value. This paper systematically reviews the current state of research, core challenges, and key technologies for constructing distributed large-scale models in space-air-ground integrated networks. It analyzes critical issues,, including network heterogeneity, on-board resource constraints, and the security-efficiency trade-off, and evaluates the strengths, limitations, and applicability of existing solutions. The study reveals that adaptive model partitioning, lightweight security protocols, and standardized frameworks remain major shortcomings. Future research should focus on lightweight model design and architecture standardization, deep integration of privacy computing with communication, and joint scheduling of communication, sensing, and computing. This paper clarifies current research limitations and future directions, offering references for the large-scale application of space-air-ground integrated computing power networks.

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Large language models (LLMs) suffer inherent factual hallucination defects, which block their deployment in high-risk fields such as medicine and finance. Retrieval-Augmented Generation (RAG) serves a mainstream hallucination mitigation solution by introducing traceable external knowledge evidence. Nevertheless, existing RAG variants are plagued by retrieval noise, poor domain generalization, lack of reasoning verification and inconsistent evaluation standards. This paper adopts classification and comparative analysis as core research methods, and systematically sorts out all hallucination suppression technical routes centered on mitigating LLM hallucinations. Four major categories of anti-hallucination RAG technologies are summarized and their applicable boundaries are compared; two mainstream evaluation benchmarks, CRAG and RAGEval, are thoroughly analyzed. Aggregated experimental results demonstrate that layered stacking of multiple technologies achieves optimal hallucination reduction performance. Finally, this paper summarizes existing research gaps, including lightweight deployment and multimodal expansion, and proposes future research directions for trustworthy RAG systems. This review provides systematic theoretical support for industrial RAG model selection and optimization.

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Stable cross-domain feature alignment is indispensable for earth observation classification, which is fundamentally hampered by radiometric gaps between generic pre-training images and aerial remote sensing data. Vision-language pre-trained models exhibit strong zero-shot capability on ordinary photos yet suffer severe accuracy loss on satellite and aerial imagery. While LoRA tuning cuts partial training costs, backbone parameter fine-tuning still brings considerable GPU memory overhead in training. Relying on frozen DeepSeek V4 MoE text LLM and static SigLIP vision encoder, this study designs a slim cross-modal projection subnet to eliminate feature distribution gaps between modalities. Stacked residual MLPs constitute the sole learnable part, containing roughly 20M parameters for visual-text latent space matching. The model is evaluated collectively on EuroSAT, PatternNet and RSSCN7, covering nearly 60,000 aerial images with 55 separate scene classes. Recorded aggregate classification precision reached 99.80% across the unified multi-source testing pool. Compared with LoRA-dependent VL-ZSDA-RS benchmark schemes, the adjustable parameter scale shrinks by over half, alongside a 46% cut in peak GPU memory usage. Layer-wise ablation trials reflect unstable matching performance under shallow projection layouts; five stacked transformation layers deliver the most balanced tradeoff between computation overhead and inter-modal alignment quality. External trainable mapping subnetworks, as the test records suggest, unlock visual discrimination capacity for unmodified text-only large language models, supplying a low-hardware threshold tuning route for earth observation research groups constrained by computing resources.

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