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Minzu University of China

Academic institutionasia · cn
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Research library15linked papers
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Selected work

Representative Papers

Beyond Peak Backlog: Conditional Energy and Temporal Geometry in Capacity-Constrained Delayed Bandit Optimization

Aug 17, 2026

This study addresses the dependence of regret bounds on peak backlog in capacity-constrained multi-armed bandits with delayed feedback. We propose a scheduler-side conditional energy interface that decouples rate adaptation from disturbance filtering to optimize delay complexity. By revealing how temporal geometry influences regret, we demonstrate that minimax regret can differ polynomially under identical delay statistics, thereby overcoming the limitations of aggregated metrics. Leveraging semi-transparent oracles and strong convexity analysis, we derive a parameter-free regret bound with a delay term of o(√(e_c d_tot)) and establish a lower bound for capacity-scarce regimes. These contributions significantly refine the theoretical granularity of regret analysis in delayed feedback settings beyond conventional aggregate measures.

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Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptation

Jan 27, 2026

This study addresses the challenge of stance detection in real-world social media, where targets are often undefined and dynamically evolving, rendering traditional methods ineffective. The work introduces, for the first time, an open-domain zero-shot stance detection task that leverages large language models (LLMs) to dynamically generate stance targets and adapt to multiple targets without requiring prior target knowledge. Key contributions include the construction of the first Chinese social media stance dataset with multidimensional evaluation metrics and the design of both integrated and two-stage fine-tuning frameworks. Experimental results demonstrate that the two-stage fine-tuned Qwen2.5-7B achieves a composite score of 66.99% in target identification, while the integrated fine-tuned DeepSeek-R1-Distill-Qwen-7B attains an F1 score of 79.26% in stance detection.

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Recent publications

Latest Papers

Beyond Peak Backlog: Conditional Energy and Temporal Geometry in Capacity-Constrained Delayed Bandit Optimization

Aug 17, 2026

This study addresses the dependence of regret bounds on peak backlog in capacity-constrained multi-armed bandits with delayed feedback. We propose a scheduler-side conditional energy interface that decouples rate adaptation from disturbance filtering to optimize delay complexity. By revealing how temporal geometry influences regret, we demonstrate that minimax regret can differ polynomially under identical delay statistics, thereby overcoming the limitations of aggregated metrics. Leveraging semi-transparent oracles and strong convexity analysis, we derive a parameter-free regret bound with a delay term of o(√(e_c d_tot)) and establish a lower bound for capacity-scarce regimes. These contributions significantly refine the theoretical granularity of regret analysis in delayed feedback settings beyond conventional aggregate measures.

0 citationsRead paper

Zero-Shot Stance Detection in the Wild: Dynamic Target Generation and Multi-Target Adaptation

Jan 27, 2026

This study addresses the challenge of stance detection in real-world social media, where targets are often undefined and dynamically evolving, rendering traditional methods ineffective. The work introduces, for the first time, an open-domain zero-shot stance detection task that leverages large language models (LLMs) to dynamically generate stance targets and adapt to multiple targets without requiring prior target knowledge. Key contributions include the construction of the first Chinese social media stance dataset with multidimensional evaluation metrics and the design of both integrated and two-stage fine-tuning frameworks. Experimental results demonstrate that the two-stage fine-tuned Qwen2.5-7B achieves a composite score of 66.99% in target identification, while the integrated fine-tuned DeepSeek-R1-Distill-Qwen-7B attains an F1 score of 79.26% in stance detection.

0 citationsRead paper