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Gang Chen

7 papers indexed

openalexAgriculture2026-07-23

Integrating GIS-MCDA and Machine Learning Approach to Identify Marginal and Underutilized Lands in Leon County, Florida

Tewodros A. Simret, Sewunet A. Natae, Gang Chen, Victor Ibeanusi, Hubert Hirwa

This study developed an integrated Geographical Information System (GIS) based Multi-Criteria Decision Analysis (GIS-MCDA) framework based on hydrological marginality, soil suitability, land cover marginality, slope, social marginality, and contaminated land indicators to identif…

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arxivcs.AI2026-07-20

Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG

Jiaming Tian, Liyao Li, Wentao Ye, Haobo Wang, Lihua Yu, Zujie Ren, et al.

Tables are a critical knowledge source in retrieval-augmented generation (RAG), but a retrieved table may lack sufficient evidence to answer a query, a property we call answerability. While answerability broadly concerns whether a source or collection of sources contains sufficie…

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arxivcs.RO2026-07-16

Risk-Aware Belief Control Barrier Functions over Random Finite Sets

Shaohang Han, Gang Chen, Yixi Cai, Ignacio Torroba, Ivan Stenius, Patric Jensfelt, et al.

Ensuring robot safety in unknown, dynamic environments is a fundamental requirement. It involves inferring the states of an unknown and time-varying number of moving objects from noisy, incomplete measurements. We address safe control under the induced multi-object state uncertai…

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arxivcs.AIcs.LG2026-07-02

Purified OPSD: On-Policy Self-Distillation Without Losing How to Think

Zhanming Shen, Jintao Tong, Shaotian Yan, Chen Shen, Hao Chen, Wentao Ye, et al.

On-policy self-distillation (OPSD) has emerged as a promising paradigm for improving LLM reasoning, where a privileged teacher with access to reference solutions provides token-level supervision on the student's own generated trajectories. However, we find that OPSD consistently…

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arxivcs.RO2026-06-30

Verification-Gated Agentic Mission-State Governance for Intelligent Industrial Multi-Robot Systems

Guoqin Tang, Qingxuan Jia, Yichen Tan, Zeyuan Huang, Ning Ji, Gang Chen

Agentic artificial intelligence is increasingly used to decompose industrial tasks, propose robot actions, and adapt execution plans in dynamic cyber-physical environments. However, autonomous proposal generation alone does not guarantee that multi-robot industrial systems preser…

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arxivcs.LGcs.AI2026-06-27

HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression

Yuxuan Yang, Feiyang Ren, Bowen Zeng, Dalin Zhang, Jinpeng Chen, Gang Chen, et al.

Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.g., Top-$p$ nucleus sampling) offer superior accuracy by dynamically fluctuating memory budgets, yet modern inference engines (e.g., vLLM) demand rigid, static memory patterns to leve…

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crossrefACM Computing Surveys2026-06-09Cited by 10

Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI

Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, Gang Chen

Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality tabular data for model training remains a significant obstacle. Numerous works have focused on tabular data augmentation (TDA) to enhance the original table with additional data, thereby impr…

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