In the rapidly developing field of automatic text generation and understanding, the quality of input data has been shown to be a key factor affecting the efficiency and accuracy of large language model (LLM) output. With the advent of advanced tools such as ChatGPT, input refinement work has mainly focused on prompt engineering. However, existing methods are often too dependent on specific contexts and are easily affected by individual expert experience and potential biases, limiting their wide applicability in diverse real-world applications. To address this problem, this study develops an Reinforced Token-Level Input Refinement, called RTLIR. We choose to optimize the input data at the fine-grained level of tokens, cleverly preserving the original text structure. Operationally, each state is defined by the token set of the current text, and each action is a binary decision process to decide whether to retain a specific token information. The agent automatically calculates and determines the selection probability of each token based on the current state, thereby optimizing the entire decision process. Through continuous exploration and learning, the agent can autonomously learn to identify the key inputs that have the greatest impact on the generation results and achieve refinement of the input data. In addition, RTLIR is a plug-and-play, LLM-agnostic module that can be used for a wide range of tasks and models. Experimental results show that RTLIR improves the performance of LLM in various input scenarios and tasks, with an average accuracy increase of 6%.

We present UrbanXplain, a language-driven urban planning system that combines real-time 3D rendering with explainable reasoning. UrbanXplain enables an integrated planning workflow driven entirely by natural language input. This includes steps from functional zoning to land use implementation. The system uses a large language models (LLMs) to perform spatial inference. It converts high-level planning goals into structured zoning commands, assigns building functions such as residential, commercial, or cultural, and creates layout plans that respect constraints like height, material, and accessibility. A Unity3D-based simulator renders the output design in real time, allowing users to explore and interact with the results. A key feature of UrbanXplain is its support for reasoning traceability. For each decision, the system displays its semantic parsing, zoning logic, and siting justifications. This ensures transparency and supports iterative refinement. We evaluate UrbanX-plain in three scenario-based experiments: 15 minute city design, green infrastructure planning, and energy efficient mixed-use layouts. These cases show LLMs supporting interpretable, adaptive, and cognitively accessible urban planning.

Evaluation of Massive Open Online Course (MOOC) quality is crucial to enhance the educational resources, benefiting user services, and enhancing students’ learning efficiency. Despite achieving encouraging results, current efforts are hindered by complex relationships between entities and individual varies. To address the above problem, in this article, we frame the issue as a task of learning course representations and proceed to develop an U ser-Centric H ypergraph R epresentation L earning ( UHRL ) for online course quality evaluation. In particular, we initially construct a MOOC hypergraph to depict the interactions and connections between the entities and use cross-hyperedge alignment to reveal the semantics of courses. And then we incorporate an attention mechanism in the information transmission process to ensure semantic integrity. Furthermore, to tackle the bias of users’ preference, our framework exploits mutual information for preserving the fairness of representation learning. Finally, our comprehensive experiments on three real-world datasets confirm the effectiveness of our approach compared to cutting-edge methods in evaluating online course quality across various performance metrics.

In the realm of human mobility, the decision-making process for selecting the next-visit location is intricately influenced by a trade-off between spatial and temporal constraints, which are reflective of individual needs and preferences. This trade-off, however, varies across individuals, making the modeling of these spatial-temporal dynamics a formidable challenge. To address the problem, in this work, we introduce the "Spatial-temporal Induced Hierarchical Reinforcement Learning" (STI-HRL) framework, for capturing the interplay between spatial and temporal factors in human mobility decision-making. Specifically, STI-HRL employs a two-tiered decision-making process: the low-level focuses on disentangling spatial and temporal preferences using dedicated agents, while the high-level integrates these considerations to finalize the decision. To complement the hierarchical decision setting, we construct a hypergraph to organize historical data, encapsulating the multi-aspect semantics of human mobility. We propose a cross-channel hypergraph embedding module to learn the representations as the states to facilitate the decision-making cycle. Our extensive experiments on two real-world datasets validate the superiority of STI-HRL over state-of-the-art methods in predicting users' next visits across various performance metrics.

University Course Timetabling Problem (UCTP) is a significant resource allocation challenge with NP-hard characteristics. As problem sizes increase, finding an optimal solution becomes increasingly complex. To address this, we propose an automated planning method using Reinforcement Learning (RL), which treats UCTP as a series of dynamic decision-making tasks. The RL agent acts as an automated planner, operating in a simulated environment that reflects the complexity and constraints of a university course. It adapts to changes in the timetable by evaluating the outcomes of its actions, continuously refining its strategy to identify and implement the most effective actions. During the explore phase, the agent tests new strategies using a priority-driven reward system and an experience replay mechanism. This approach replays high-value actions, enhancing solution diversity and reducing the search space. In the exploit phase, the agent leverages its accumulated knowledge to apply proven effective strategies. It also incorporates unbiased external evaluations to avoid local optima and ensure global optimization of its strategy. Extensive testing on multiple real-world datasets shows that our automated planning framework consistently outperforms existing methods in various complex scheduling scenarios. This confirms its effectiveness and practicality in meeting diverse scheduling needs.

The cellular heterogeneous networks (CHNs) are of great interest for the great potential of improving the network capacity by employing low-power, easy- deployment short-range mini-base stations (BSs). To cope with the challenge of inter-layer/inner-layer interference environment in such heterogeneous architecture, we introduce cognitive Radio (CR) in CHNs, and named it as CR Enabled cellular heterogeneous networks (CCHNs). However, the transmission protocol deign is crucial for the implement of CCHSs. For one thing, the division of sensing and access phase is vital for the sensing accuracy and spectrum efficiency; For another, the power spend on the spectrum sensing cuts the transmission power allocation budget. In this paper, such coupled problem is solved by a Bi-level optimization method, by which the joint problem is decoupled to the upper level power allocation subproblem and lower level slot partition subproblem. Simulation results show that the proposed algorithm achieves the optimal jointing optimization of spectrum sensing and access, which alleviates the inter-layer interference between heterogeneous cells and improve the performance of entire network.