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Guizhou University

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Research library12linked papers
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Selected work

Representative Papers

Threshold Attention Network for Semantic Segmentation of Remote Sensing Images

Jan 14, 2025IEEE Transactions on Geoscience and Remote Sensing

To address the high computational complexity and redundancy inherent in self-attention mechanisms for remote sensing image semantic segmentation, this paper proposes a Threshold Attention Mechanism (TAM) and a lightweight, efficient network termed TANet. TANet comprises two key components: an Attention Feature Enhancement Module (AFEM) for shallow-layer global feature enhancement, and a Threshold Attention Pyramid Pooling module (TAPP) for deep-layer multi-scale contextual modeling. TAM dynamically filters salient attention regions via adaptive thresholding, preserving long-range dependency modeling while substantially reducing computational overhead. Evaluated on the ISPRS Vaihingen and Potsdam benchmarks, TANet achieves competitive accuracy against state-of-the-art methods, with ~22% faster inference speed and 40% lower GPU memory consumption—demonstrating a favorable balance among accuracy, efficiency, and deployment practicality.

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MAPPO-LCR: Multi-Agent Policy Optimization with Local Cooperation Reward in Spatial Public Goods Games

Dec 18, 2025

In spatial public goods games, strong payoff coupling, environmental non-stationarity, and population-level strategy dependencies pose significant challenges for existing approaches—such as evolutionary updating or independent reinforcement learning—which fail to capture inter-agent strategic interdependence. To address this, this paper introduces multi-agent proximal policy optimization (MAPPO) to the domain for the first time, proposing the MAPPO-LCR framework. Its key contributions are: (1) a centralized critic that explicitly models global policy coupling; (2) a local cooperation reward (LCR) mechanism, where reward signals are derived from neighborhood cooperation density to guide policy updates; and (3) unified decoupled execution with joint value estimation, preserving the original game structure. Experiments across diverse enhancement factors demonstrate that MAPPO-LCR consistently emergently fosters cooperation, substantially outperforming independent PPO in cooperation rate, convergence speed, and robustness.

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Recent publications

Latest Papers

MAPPO-LCR: Multi-Agent Policy Optimization with Local Cooperation Reward in Spatial Public Goods Games

Dec 18, 2025

In spatial public goods games, strong payoff coupling, environmental non-stationarity, and population-level strategy dependencies pose significant challenges for existing approaches—such as evolutionary updating or independent reinforcement learning—which fail to capture inter-agent strategic interdependence. To address this, this paper introduces multi-agent proximal policy optimization (MAPPO) to the domain for the first time, proposing the MAPPO-LCR framework. Its key contributions are: (1) a centralized critic that explicitly models global policy coupling; (2) a local cooperation reward (LCR) mechanism, where reward signals are derived from neighborhood cooperation density to guide policy updates; and (3) unified decoupled execution with joint value estimation, preserving the original game structure. Experiments across diverse enhancement factors demonstrate that MAPPO-LCR consistently emergently fosters cooperation, substantially outperforming independent PPO in cooperation rate, convergence speed, and robustness.

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A Reasoning Paradigm for Named Entity Recognition

Nov 14, 2025

To address the weak zero-shot/low-resource generalization and poor interpretability of generative large language models (LLMs) in named entity recognition (NER)—stemming from implicit reasoning—this paper proposes the first end-to-end explicit reasoning framework tailored for NER. Our method transforms conventional pattern matching into a verifiable chain-of-thought (CoT) reasoning process, integrating instruction tuning with a multi-dimensional reward-based reasoning optimization mechanism. The framework is trained in three stages to substantially enhance model cognitive transparency and robustness. Experiments demonstrate that our approach achieves an F1 score of 72.4% in zero-shot settings—outperforming GPT-4 by 12.3 percentage points—and establishes the new state of the art. The core contribution lies in introducing, for the first time in NER, a verifiable and optimizable explicit reasoning paradigm grounded in interpretable, stepwise inference.

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