Unmanned Aerial Vehicles (UAVs) have made significant advancements in communication stability and security through techniques such as frequency hopping, signal spreading, and adaptive interference suppression. However, challenges remain in modeling spectrum competition, integrating expert knowledge, and predicting opponent behavior. To address these issues, we propose UAV-FPG (Unmanned Aerial Vehicle–Frequency Point Game), a game-theoretic environment model that simulates the dynamic interaction between interference and anti-interference strategies of opponent and ally UAVs in communication frequency bands. The model incorporates a prior expert knowledge base to optimize frequency selection and employs large language models for episode-level opponent trajectory generation and planning within UAV-FPG, serving as an operationally more challenging simulator adversary for stress-testing anti-jamming policies under our evaluation protocol. Experimental results highlight the effectiveness of integrating the expert knowledge base and the large language model: relative to fixed-path baselines, iterative feedback-conditioned LLM planning tends to generate more adaptive trajectories and achieve higher opponent rewards in UAV-FPG. These findings are confined to the proposed simulation environment and are not intended as general claims about real-world jamming capability or onboard planning performance. UAV-FPG provides a robust platform for advancing anti-jamming strategies and intelligent decision-making in UAV communication systems.

Large language models (LLMs) demonstrate outstanding capability in understanding natural language and show great potential in open-domain travel planning. However, when confronted with multi-constraint itineraries, personalized recommendations, and scenarios requiring rigorous external information validation, pure LLM-based approaches lack rigorous planning ability and fine-grained personalization. To address these gaps, we propose the Symbolic LoRA Travel Planner (SLTP) framework—an agent architecture that combines a two-stage symbol-rule LoRA fine-tuning pipeline with a user multi-option heuristic tree search (MHTS) planner. SLTP decomposes the entire process of transforming natural language into executable code into two specialized, sequential LoRA experts: the first maps natural-language queries to symbolic constraints with high fidelity; the second compiles symbolic constraints into executable Python planning code. After reflective verification, the generated code serves as constraints and heuristic rules for an MHTS planner that preserves diversified top-K candidate itineraries and uses pruning plus heuristic strategies to maintain search-time performance. To overcome the scarcity of high-quality intermediate symbolic data, we adopt a teacher–student distillation approach: a strong teacher model generates high-fidelity symbolic constraints and executable code, which we use as hard targets to distill knowledge into an 8B-parameter Qwen3-8B student model via two-stage LoRA. On the ChinaTravel benchmark, SLTP using an 8B student achieves performance comparable to or surpassing that of other methods built on DeepSeek-V3 or GPT-4o as a backbone.

Maintaining high-fidelity character personas and tracking trusted narrative facts remain significant challenges for LLM-based role-playing systems, particularly in long-context scenarios. Traditional Retrieval-Augmented Generation (RAG) approaches, which typically rely on static, stateless retrieval, often struggle to capture evolving plot dynamics, leading to character hallucinations and logical inconsistencies over prolonged interactions. To address these limitations, we present FictionRAG, a novel stateful retrieval-augmented framework designed to enhance long-narrative role-playing. FictionRAG introduces a hierarchical memory architecture that decouples narrative information into three distinct lanes: factual events, persona traits, and worldview constraints. Furthermore, it employs a failure-driven metacognitive regulatory loop that dynamically identifies and corrects retrieval deficiencies—such as persona drift or conflicting world rules—before response generation. By treating role-playing as a dynamic state tracking problem rather than simple question answering, FictionRAG ensures that generated responses are strictly grounded in both the narrative timeline and the character’s psychological profile. Extensive experiments on a dataset comprising twenty classic novels demonstrate that FictionRAG significantly outperforms existing baselines in factual accuracy, persona stability, and worldview consistency. Beyond literary role-playing, these results suggest that stateful, evidence-constrained retrieval can serve as a general mechanism for long-form controllable generation tasks that require persistent state tracking and multi-dimensional consistency.