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Institute of Artificial Intelligence

Academic institution
Research library9linked papers
Opportunities0open roles
Selected work

Representative Papers

Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting

Sep 30, 2026

This study addresses the issue of object hallucination in large vision-language models during decoding, which arises from visual uncertainty and lacks principled image-level false positive control in existing methods. To this end, it proposes CORAL, a framework that models visual uncertainty through an uncertainty-aware visual data splitting strategy. Notably, CORAL introduces a false discovery rate (FDR) control mechanism that computes mirror statistics via symmetrically perturbed inputs and establishes data-driven thresholds, enabling training-free hallucination mitigation. The proposed approach is compatible with various mainstream architectures and significantly outperforms state-of-the-art methods across multiple benchmarks. By effectively suppressing hallucinations while preserving genuine object detection capabilities, this work substantially enhances the reliability and robustness of model outputs.

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Evolving Support Priorities in Empathetic Reinforcement Learning

Sep 28, 2026

This study addresses the misalignment in empathy-enhanced reinforcement learning, where support priorities evolve dynamically during conversations while existing reward specifications remain static. To overcome this limitation, we propose CARE, a framework that introduces a pioneering context-adaptive scoring rule evolution mechanism. This mechanism dynamically adjusts the weights and criteria across cognitive, affective, and proactive empathy dimensions to construct an adaptive reward interface. Furthermore, the rule generator is trained via supervised fine-tuning combined with preference-based reinforcement learning, while the RLVER and MICA algorithms are integrated to optimize multi-turn empathetic strategies. Experimental results demonstrate that CARE achieves state-of-the-art performance on benchmarks including SentientBench. Notably, it improves the EMPA metric by at least 13 points over the strongest baseline, elevating the maximum score from 28.11 to 83.54.

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DORA: Dynamic Online Reinforcement Agent for Token Pruning in Vision Transformers

Sep 28, 2026

This study addresses the high computational overhead of self-attention in Vision Transformers (ViTs) and the lack of online adaptability in existing pruning methods by proposing the DORA framework. This approach formulates token pruning as a finite-horizon Markov decision process, leveraging deep reinforcement learning to generate input-adaptive, layer-wise dynamic pruning policies for frozen ViTs. To facilitate efficient training, DORA introduces a hierarchical actor, a privileged critic, and a shadow evaluation credit assignment mechanism. Evaluated on ImageNet, DORA reduces FLOPs by 38.4% with less than 1% accuracy degradation while improving throughput by 32.4%. Furthermore, the proposed method demonstrates significant zero-shot transfer capabilities, highlighting its effectiveness and generalizability across diverse downstream tasks without requiring task-specific fine-tuning.

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Recent publications

Latest Papers

Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting

Sep 30, 2026

This study addresses the issue of object hallucination in large vision-language models during decoding, which arises from visual uncertainty and lacks principled image-level false positive control in existing methods. To this end, it proposes CORAL, a framework that models visual uncertainty through an uncertainty-aware visual data splitting strategy. Notably, CORAL introduces a false discovery rate (FDR) control mechanism that computes mirror statistics via symmetrically perturbed inputs and establishes data-driven thresholds, enabling training-free hallucination mitigation. The proposed approach is compatible with various mainstream architectures and significantly outperforms state-of-the-art methods across multiple benchmarks. By effectively suppressing hallucinations while preserving genuine object detection capabilities, this work substantially enhances the reliability and robustness of model outputs.

0 citationsRead paper

Evolving Support Priorities in Empathetic Reinforcement Learning

Sep 28, 2026

This study addresses the misalignment in empathy-enhanced reinforcement learning, where support priorities evolve dynamically during conversations while existing reward specifications remain static. To overcome this limitation, we propose CARE, a framework that introduces a pioneering context-adaptive scoring rule evolution mechanism. This mechanism dynamically adjusts the weights and criteria across cognitive, affective, and proactive empathy dimensions to construct an adaptive reward interface. Furthermore, the rule generator is trained via supervised fine-tuning combined with preference-based reinforcement learning, while the RLVER and MICA algorithms are integrated to optimize multi-turn empathetic strategies. Experimental results demonstrate that CARE achieves state-of-the-art performance on benchmarks including SentientBench. Notably, it improves the EMPA metric by at least 13 points over the strongest baseline, elevating the maximum score from 28.11 to 83.54.

0 citationsRead paper

DORA: Dynamic Online Reinforcement Agent for Token Pruning in Vision Transformers

Sep 28, 2026

This study addresses the high computational overhead of self-attention in Vision Transformers (ViTs) and the lack of online adaptability in existing pruning methods by proposing the DORA framework. This approach formulates token pruning as a finite-horizon Markov decision process, leveraging deep reinforcement learning to generate input-adaptive, layer-wise dynamic pruning policies for frozen ViTs. To facilitate efficient training, DORA introduces a hierarchical actor, a privileged critic, and a shadow evaluation credit assignment mechanism. Evaluated on ImageNet, DORA reduces FLOPs by 38.4% with less than 1% accuracy degradation while improving throughput by 32.4%. Furthermore, the proposed method demonstrates significant zero-shot transfer capabilities, highlighting its effectiveness and generalizability across diverse downstream tasks without requiring task-specific fine-tuning.

0 citationsRead paper