Accurate and feasible target assignment in an urban environment without road networks remains challenging. Existing methods exhibit critical limitations: computational inefficiency preventing real-time decision-making requirements and poor cross-scenario generalization, yielding task-specific policies that lack adaptability. To achieve efficient target assignment in urban adversarial scenarios, we propose an efficient traversable path generation method requiring only binarized images, along with four key constraint models serving as optimization objectives. Moreover, we model this optimization problem as a Markov decision process (MDP) and introduce the generalization sequential proximal policy optimization (GSPPO) algorithm within the reinforcement learning (RL) framework. Specifically, GSPPO integrates an exploration history representation module (EHR) and a neuron-specific plasticity enhancement module (NPE). EHR incorporates exploration history into the policy learning loop, which significantly improves learning efficiency. To mitigate the plasticity loss in neural networks, we propose an NPE module, which boosts the model’s representational capability and generalization across diverse tasks. Experiments demonstrate that our approach reduces planning time by four orders of magnitude compared to the online planning method. Against the benchmark algorithm, it achieves 94.16% higher convergence performance, 33.54% shorter assignment path length, 51.96% lower threat value, and 40.71% faster total time. Our approach supports real-time military reconnaissance and will also facilitate rescue operations in complex cities.

Schema linking, the task of identifying relevant database schema elements (tables and columns) for natural language queries, is a critical component in database-driven natural language interfaces. While existing approaches rely on question decomposition to handle complex queries, they often suffer from error propagation and low precision. In this paper, we propose a novel schema linking framework enhanced by self-verification (SV) and value hints (VHs) that significantly improves both precision and recall. Our approach introduces two key components: (1) self-verification (SV), an iterative refinement mechanism that validates and corrects initial predictions through explicit verification prompts, and (2) value hints (VHs), which explicitly guide the model to recognize database values mentioned in queries. We conduct comprehensive experiments on two benchmark datasets, Spider and BIRD, using two language models of 4B and 80B parameters. Our results demonstrate that SV + VH consistently improves performance across datasets, models, and method configurations, outperforming both decomposition-based approaches and compute-matched alternatives such as self-consistency under equivalent inference budgets.