Large language models (LLMs) often generate hallucinations—fluent yet factually incorrect responses—that undermine reliability in knowledge-intensive tasks. Existing approaches for hallucination mitigation typically rely on external retrieval modules or probability heuristics, which either require additional resources or lack interpretability. In this work, we propose a diffusion-based hallucination detection framework (DHDF) that leverages U-Net denoising to reconstruct consensus answers from multiple LLM outputs. If the diffusion process exhibits spurious convergence away from factual ground truth, it provides a clear signal of hallucination. To quantify factual correctness, we incorporate TruthfulQA scores as a fact-grounded evaluation metric, distinguishing well-aligned models (high scores) from hallucination-prone models (low scores). Experimental results demonstrate that convergence dynamics under diffusion, combined with fact-grounded QA evaluation, offer an effective and interpretable pathway for hallucination detection without relying on external knowledge bases.

Low Earth Orbit (LEO) satellite constellations are crucial for global connectivity by providing extensive coverage and reduced delays. However, scheduling data transmission in these dynamic networks is challenging due to rapidly changing satellite positions. This research introduces BoostRL (boosted reinforcement learning), a novel framework integrating large language models (LLMs) with reinforcement learning (RL) for efficient scheduling in LEO satellite constellations. BoostRL leverages LLM-generated initial policies to accelerate convergence, thereby guiding the early stages of policy learning while adapting swiftly to dynamic network conditions. It employs a hybrid policy approach which transitions smoothly from LLM recommendations to autonomous RL policies. A tailored initialization of Q-value parameters and an enhanced loss function further optimize learning efficiency, by aligning the initial learning phase with LLM-generated insights. Simulations using two-line element (TLE) orbital data demonstrate that BoostRL achieves rapid convergence and improved efficiency, thereby validating its potential as a scalable, adaptive solution for managing satellite communication networks.

This paper proposes SafeGPT, a two-tiered framework that integrates generative pretrained transformers (GPTs) with reinforcement learning (RL) for efficient and reliable un-manned aerial vehicle (UAV) last-mile deliveries. In the proposed design, a Global GPT module assigns high-level tasks such as sector allocation, while an On-Device GPT manages real-time local route planning. An RL-based safety filter monitors each GPT decision and overrides unsafe actions that could lead to battery depletion or duplicate visits, effectively mitigating hallucinations. Furthermore, a dual replay buffer mechanism helps both the GPT modules and the RL agent refine their strategies over time. Simulation results demonstrate that SafeGPT achieves higher delivery success rates compared to a GPT-only baseline, while substantially reducing battery consumption and travel distance. These findings validate the efficacy of combining GPT-based semantic reasoning with formal safety guarantees, contributing a viable solution for robust and energy-efficient UAV logistics.