RePro: Proof-Verified Benchmark Rewriting for Reliable Evaluation of LLM Mathematical Problem Solving
为解决数据污染影响大模型数学解题评估的问题，提出RePro框架，利用神经自动定理证明器重写问题并确保答案正确性。
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Optimal Smooth Coverage Trajectory Planning for Quadrotors in Cluttered Environment
This paper addresses the trajectory planning problem for unmanned aerial vehicles (UAVs) in complex scenarios such as power line inspection, where full coverage of points of interest (POIs) must be achieved under smoothness, timeliness, and obstacle-avoidance constraints. We propose a front-end/back-end co-design framework for optimal smooth trajectory generation. The front-end employs a genetic algorithm to solve the POI visiting sequence, formulated as a constrained traveling salesman problem (TSP). The back-end jointly optimizes trajectory timing, C² continuity, and minimum obstacle clearance via nonlinear least-squares, yielding differentiable, safe, and time-efficient fully covered paths. Our key innovation lies in the tight integration of discrete sequence optimization with continuous trajectory synthesis, augmented by environment-aware explicit obstacle modeling. Numerical simulations demonstrate that the algorithm robustly generates smooth, 100% POI-covered trajectories in dense obstacle environments, reducing average mission time by 18.7% while maintaining feasibility—indicating strong potential for real-time deployment.
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RePro: Proof-Verified Benchmark Rewriting for Reliable Evaluation of LLM Mathematical Problem Solving
为解决数据污染影响大模型数学解题评估的问题，提出RePro框架，利用神经自动定理证明器重写问题并确保答案正确性。
0 citationsRead paper
Optimal Smooth Coverage Trajectory Planning for Quadrotors in Cluttered Environment
This paper addresses the trajectory planning problem for unmanned aerial vehicles (UAVs) in complex scenarios such as power line inspection, where full coverage of points of interest (POIs) must be achieved under smoothness, timeliness, and obstacle-avoidance constraints. We propose a front-end/back-end co-design framework for optimal smooth trajectory generation. The front-end employs a genetic algorithm to solve the POI visiting sequence, formulated as a constrained traveling salesman problem (TSP). The back-end jointly optimizes trajectory timing, C² continuity, and minimum obstacle clearance via nonlinear least-squares, yielding differentiable, safe, and time-efficient fully covered paths. Our key innovation lies in the tight integration of discrete sequence optimization with continuous trajectory synthesis, augmented by environment-aware explicit obstacle modeling. Numerical simulations demonstrate that the algorithm robustly generates smooth, 100% POI-covered trajectories in dense obstacle environments, reducing average mission time by 18.7% while maintaining feasibility—indicating strong potential for real-time deployment.
0 citationsRead paper