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Huanjun Wang

Publications and source records attributed to Huanjun Wang.

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Facility Location Games for Multi-Location Agents with Satisfaction

In this paper, we study mechanism design for single-facility location games where each agent has multiple private locations in [0, 1]. The individual objective is a satisfaction function that measures the discrepancy between the optimal facility location for an agent and the location provided by the mechanism. Based on different distance functions from agents to the facility, we consider two types of individual objectives: the sum-variant satisfaction and the max-variant satisfaction. Our goal is to design mechanisms that locate one facility to maximize the sum (or the minimum) of all agents' satisfactions, while incentivizing agents to truthfully report their locations. In this paper, we mainly focus on desirable and obnoxious facility location games. For desirable facility location games, we propose two group strategy-proof mechanisms with approximation ratios of 2 and 5/4 for maximizing the sum of the sum-variant and max-variant satisfaction, respectively. Moreover, another mechanism achieves an approximation ratio of 2 for simultaneously maximizing the minimum of the sum-variant satisfaction and the minimum of the max-variant satisfaction. For obnoxious facility location games, we establish that two group strategy-proof mechanisms are the best possible, providing an approximation ratio of 2 for maximizing the sum of the sum-variant satisfaction and the sum of the max-variant satisfaction, respectively. Additionally, we devise two 4/3-approximation randomized group strategy-proof mechanisms, and provide two lower bounds of 1.0625 and 1.0448 of randomized strategy-proof mechanisms for maximizing the sum of the sum-variant satisfaction and the sum of the max-variant satisfaction, respectively.

cs.GT

Adaptive Overclocking: Dynamic Control of Thinking Path Length via Real-Time Reasoning Signals

Large Reasoning Models (LRMs) often suffer from computational inefficiency due to overthinking, where a fixed reasoning budget fails to match the varying complexity of tasks. To address this issue, we propose Adaptive Overclocking, a method that makes the overclocking hyperparameter $\alpha$ dynamic and context-aware. Our method adjusts reasoning speed in real time through two complementary signals: (1) token-level model uncertainty for fine-grained step-wise control, and (2) input complexity estimation for informed initialization. We implement this approach with three strategies: Uncertainty-Aware Alpha Scheduling (UA-$\alpha$S), Complexity-Guided Alpha Initialization (CG-$\alpha$I), and a Hybrid Adaptive Control (HAC) that combines both. Experiments on GSM8K, MATH, and SVAMP show that HAC achieves superior accuracy-latency trade-offs, reducing unnecessary computation on simple problems while allocating more resources to challenging ones. By mitigating overthinking, Adaptive Overclocking enhances both efficiency and overall reasoning performance.

cs.LG