most citedEvolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons

14 citations · 21 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

When Model Priors Conflict with Visual Evidence: Mitigating Commonsense-Driven Hallucinations by Selective Prior Calibration

Kesheng Chen, Yamin Hu, Wenjian Luo

In vision--language models, commonsense-driven hallucination (CDH) occurs when a model's commonsense prior overrides clear visual evidence of an atypical state. For example, a mode…

cs.LG2026

AP-BMM: Approximating Capability-Cost Pareto Sets of LLMs via Asynchronous Prior-Guided Bayesian Model Merging

Kesheng Chen, Yamin Hu, Zhenqian Zhu +2

LLM services need to offer a family of models spanning different capability--cost trade-offs to accommodate diverse user preferences. Model merging offers a practical way to constr…

cs.CV2026

CDH-Bench: A Commonsense-Driven Hallucination Benchmark for Evaluating Visual Fidelity in Vision-Language Models

Kesheng Chen, Yamin Hu, Qi Zhou +2

Vision-language models (VLMs) achieve strong performance on many benchmarks, yet a basic reliability question remains underexplored: when visual evidence conflicts with commonsense…

cs.NE20267 cited

A Novel Immune Algorithm for Multiparty Multiobjective Optimization

Kesheng Chen, Wenjian Luo, Qi Zhou +3

Traditional multiobjective optimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical ap…

cs.NE202614 cited

Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons

Kesheng Chen, Wenjian Luo, Xin Lin +2

Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-…

cs.AI2025

MIR: Efficient Exploration in Episodic Multi-Agent Reinforcement Learning via Mutual Intrinsic Reward

Kesheng Chen, Wenjian Luo, Bang Zhang +2

Episodic rewards present a significant challenge in reinforcement learning. While intrinsic reward methods have demonstrated effectiveness in single-agent rein-forcement learning s…