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Yuxin Lin

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10 papers

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AAAI Conference 2026 Conference Paper

Enhancing Pre-training Data Detection in LLMs Through Discriminative and Symmetric Prefix Selection

Kai Sun
Yuxin Lin
Bo Dong
Jingyao Zhang
Bin Shi

The rapid development of large language models (LLMs) has relied on access to high-quality, large-scale datasets, yet growing concerns around data privacy and security have spurred substantial research into pre-training data detection. While state-of-the-art (SOTA) methods such as RECALL and CON-RECALL leverage auxiliary prefixes to enhance detection performance, their dependence on individual prefixes introduces notable instability across varying prefix conditions. To address this, we first conduct a theoretical analysis to assess the impact of prefixes on existing prefix-based methods. Building on the analysis, we propose a novel prefix selection method to identify optimal prefixes. Specifically, our method derives two key criteria Discriminability and Symmetry. These criteria serve to quantify the effectiveness of prefixes in detecting pre-training data, enabling precise selection of high-performing candidate prefixes. Experiments on the WikiMIA dataset demonstrate that our method consistently improves the performance of RECALL and CON-RECALL, achieving gains of up to 21.1% in AUC scores while significantly enhancing robustness.

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AAAI Conference 2026 Conference Paper

Frequency-Aligned Cross-Modal Learning with Top-K Wavelet Fusion and Dynamic Expert Routing for Enhanced Retinal Disease Diagnosis

Yuxin Lin
Haoran Li
Haoyu Cao
Yongting Hu
Qihao Xu
Chengliang Liu
Xiaoling Luo
Zhihao Wu

Multimodal fusion of color fundus photography (CFP) and optical coherence tomography (OCT) B-scan images has demonstrated superior diagnostic potential for retinal diseases compared to single-modality approaches. However, existing fusion paradigms - whether through naive concatenation or attention mechanisms - treat cross-modal interactions indiscriminately, lacking adaptive modulation of modality-specific contributions under varying clinical scenarios. We propose an adaptive fusion framework that dynamically routes and refines multimodal signals for enhancing disease recognition. The framework comprises two key components: 1) Dynamic Cross-Modal Expert Routing (CMER), which selectively activates convolutional neural network (CNN) experts from one modality based on contextual guidance from the other, ensuring only the most relevant feature extractors contribute to fusion; and 2) Top-K Expert-Guided Wavelet Fusion (TEWF), which performs discrete wavelet transform (DWT) to decompose selected features into low- and high-frequency subbands. Cross-modal attention is then applied specifically to high-frequency components, where lesion-specific microstructures reside, enabling frequency-aware fusion. Finally, inverse DWT (IDWT) reconstructs the fused representation, weighted by CMER-derived importance scores to amplify informative modality cues while suppressing redundancy. Experimental validation on two multimodal retinal datasets demonstrates that our method achieves state-of-the-art performance, outperforming existing fusion strategies by significant margins in disease classification accuracy and robustness.

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AAAI Conference 2026 Conference Paper

Incomplete Multi-view Diabetic Retinopathy Grading via Self-Supervised Inter- and Intra-View Restoration

Zhihao Wu
Yuxin Lin
Jie Wen
Wuzhen Shi
Linlin Shen

Multi-view diabetic retinopathy (DR) grading has achieved remarkable performance by capturing more comprehensive pathological features than single-view methods. However, complete multi-view fundus images are often difficult to obtain in clinical practice, and the performance degrades significantly when fewer views are available. To overcome this limitation, we propose the first incomplete multi-view DR grading framework, aiming to provide accurate diagnosis regardless of the number of available views. It introduces two novel modules. First, cross-view spatial correlation attention (CSCA) captures region correlations across views, automatically identifying and fusing diagnostically relevant spatial features to improve feature representation. Second, self-supervised mask consistency learning (SMCL) formulates a novel pretext task of missing-view information reconstruction by strategically masking inter- and intra-view regions, enabling the model to infer complete features from incomplete views. Benefiting from CSCA and SMCL, our method enhances structural feature consistency across views and effectively compensates for missing information during DR grading. Extensive experiments demonstrate that our method achieves state-of-the-art grading performance, particularly under realistic conditions where some views are unavailable.

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AAAI Conference 2026 Conference Paper

Scope Delineation Before Localization: A Two-Stage Framework for Enhancing Failure Attribution in Multi-Agent Systems

Kai Sun
Wenqiang Li
Bo Dong
Yuxin Lin
Jingyao Zhang
Bin Shi

Large language models (LLMs) are seeing growing adoption in multi-agent systems. In these systems, efficient failure attribution is critical for ensuring robustness and interpretability. Current LLM-based attribution methods often face challenges with lengthy logs and lacking expert knowledge. Drawing inspiration from human debugging strategies, we propose an automated failure attribution framework, Scope Delineation Before Localization, which operates in two key stages: (1) identifying the failure scope and (2) pinpointing the failure step. By decoupling failure attribution into the two stages, our approach alleviates the reasoning workload of LLMs, enabling more precise failure attribution. To support scope delineation, we further introduce two strategies: Stepwise Scope Delineation and Expertise-Assisted Scope Delineation. Experiments on the Who&When dataset validate the efficacy of our two-stage framework, demonstrating substantial improvements over prior methods (up to 24.27% on step-level accuracy).

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AAAI Conference 2026 Conference Paper

Towards Zero-Shot Diabetic Retinopathy Grading: Learning Generalized Knowledge via Prompt-Driven Matching and Emulating

Huan Wang
Haoran Li
Yuxin Lin
Huaming Chen
Jun Yan
Lijuan Wang
Jiahua Shi
Qihao Xu

As one of the primary causes of visual impairment, Diabetic Retinopathy (DR) requires accurate and robust grading to facilitate timely diagnosis and intervention. Different from conventional DR grading methods that utilize single-view images, recent clinical studies have revealed that multi-view fundus images can significantly enhance DR grading performance by expanding the field of view (FOV). However, there is a long-tailed distribution problem in fundus image analysis, i.e., a high prevalence of mild DR grades and a low prevalence of rare ones (e.g., cases of high severity), which presents a significant challenge to developing a unified model capable of detecting rare or unseen DR grades not encountered during training. In this paper, we propose ProME-DR, a Prompt-driven zero-shot DR grading framework, which leverages prompt Matching and Emulating to recognize the unseen DR categories and views beyond the training set. ProME-DR disentangles the training process into two stages to learn generalized knowledge for novel DR disease grading. Initially, ProME-DR leverages two sets of prompt units to capture semantic and inter-view consistency knowledge via a split-and-mask manner, gathering instance-level DR visual clues. Subsequently, it constructs a concept-aware emulator to generate context prompt units, linking extensible knowledge learned from the previously seen DR attributes for zero-shot DR grading. Extensive experiments conducted on eight datasets and various scenarios confirm the superiority of ProME-DR.

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AAAI Conference 2026 Conference Paper

Vision-Language Models Guided Graph Concept Reasoning for Interpretable Diabetic Retinopathy Diagnosis

Qihao Xu
Xiaoling Luo
Yuxin Lin
Chengliang Liu
Yongting Hu
Jinkai Li
Xinheng Lyu
Yong Xu

Deep neural networks (DNNs) have significantly advanced diabetic retinopathy (DR) diagnosis, yet their black-box nature limits clinical acceptance due to a lack of interpretability. Concept bottleneck model (CBM) offers a promising solution by enabling concept-level reasoning and test-time intervention, with recent DR studies modeling lesions as concepts and grades as outcomes. However, current methods often ignore relationships between lesion concepts across different DR grades and struggle when fine-grained lesion concepts are unavailable, limiting their interpretability and real-world applicability. To bridge these gaps, we propose VLM-GCR, a vision-language model guided graph concept reasoning framework for interpretable DR diagnosis. VLM-GCR emulates the diagnostic process of ophthalmologists by constructing a grading-aware lesion concept graph that explicitly models the interactions among lesions and their relationships to disease grades. In concept-free clinical scenarios, our method introduces a vision-language guided dynamic concept pseudo-labeling mechanism to mitigate the challenges of existing concept-based models in fine-grained lesion recognition. Additionally, we introduce a multi-level intervention method that supports error correction, enabling transparent and robust human-AI collaboration. Experiments on two public DR benchmarks show that VLM-GCR achieves strong performance in both lesion and grading tasks, while delivering clear and clinically meaningful reasoning steps.

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AAAI Conference 2025 Conference Paper

Deep Hierarchies and Invariant Disease-Indicative Feature Learning for Computer Aided Diagnosis of Multiple Fundus Diseases

Yuxin Lin
Wei Wang
Xiaoling Luo
Zhihao Wu
Chengliang Liu
Jie Wen
Yong Xu

With the advancement of computer vision, numerous models have been proposed for screening of fundus diseases. However, the recognition of multiple fundus diseases is often hampered by the simultaneous presence of multiple disease types and the confluence of lesion types in fundus images. This paper addresses these challenges by conceptualizing them as multi-level feature fusion and self-supervised disease-indicative feature learning problems. We decode fundus images at various levels of granularity to delineate scenarios wherein multiple diseases and lesions co-occur. To effectively integrate these features, we introduce a hierarchical vision transformer (HVT) that adeptly captures both inter-level and intra-level dependencies. A novel forward-attention module is proposed to enhance the integration of lower-level semantic information into higher semantic layers, thereby enriching the representation of complex features. Additionally, we introduce a novel self-supervised mask-consistent feature learner (MCFL). Unlike traditional mask-autoencoders that reconstruct original images using encoder-decoder structures, MCFL utilizes a teacher-student framework to reconstruct mask-consistent feature maps. In this setup, exponential moving averaging is employed to derive classification-guided features, serving as labels for reconstruction rather than merely reconstructing the original images. This innovative approach facilitates the extraction of disease-indicative features. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art models.

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RLC Conference 2025 Conference Paper

Reinforcement Learning for Human-AI Collaboration via Probabilistic Intent Inference

Yuxin Lin
Seyede Fatemeh Ghoreishi
Tian Lan
Mahdi Imani

Effective collaboration between humans and AI agents is increasingly essential as autonomous systems take on critical roles in domains like disaster response, healthcare, and robotics. However, achieving robust human-AI collaboration remains challenging due to the uncertainty, complexity, and unpredictability of human behavior, which is often difficult to convey explicitly to AI agents. This paper presents a belief-space reinforcement learning framework that enables AI agents to implicitly and probabilistically infer latent human intentions from behavioral data and integrate this understanding into robust decision-making. Our approach models human behavior at both the action (low) and subtask (high) levels, combining these with human and agent state information to construct a comprehensive belief state for the AI agent. We demonstrate that this belief state follows the Markov property, enabling the derivation of an optimal Bayesian policy under human and task uncertainty. Deep reinforcement learning is used to train an offline Bayesian policy across a wide range of human and task uncertainties, allowing real-time deployment to support effective human-AI collaboration. Numerical experiments demonstrate the effectiveness of the proposed policy in terms of cooperation, adaptability, and robustness.

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RLJ Journal 2025 Journal Article

Reinforcement Learning for Human-AI Collaboration via Probabilistic Intent Inference

Yuxin Lin
Seyede Fatemeh Ghoreishi
Tian Lan
Mahdi Imani

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NeurIPS Conference 2024 Conference Paper

CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction

Shuqi Li
Yuebo Sun
Yuxin Lin
Xin Gao
Shuo Shang
Rui Yan

There are two issues in news-driven multi-stock movement prediction tasks that are not well solved in the existing works. On the one hand, "relation discovery" is a pivotal part when leveraging the price information of other stocks to achieve accurate stock movement prediction. Given that stock relations are often unidirectional, such as the "supplier-consumer" relationship, causal relations are more appropriate to capture the impact between stocks. On the other hand, there is substantial noise existing in the news data leading to extracting effective information with difficulty. With these two issues in mind, we propose a novel framework called CausalStock for news-driven multi-stock movement prediction, which discovers the temporal causal relations between stocks. We design a lag-dependent temporal causal discovery mechanism to model the temporal causal graph distribution. Then a Functional Causal Model is employed to encapsulate the discovered causal relations and predict the stock movements. Additionally, we propose a Denoised News Encoder by taking advantage of the excellent text evaluation ability of large language models (LLMs) to extract useful information from massive news data. The experiment results show that CausalStock outperforms the strong baselines for both news-driven multi-stock movement prediction and multi-stock movement prediction tasks on six real-world datasets collected from the US, China, Japan, and UK markets. Moreover, getting benefit from the causal relations, CausalStock could offer a clear prediction mechanism with good explainability.

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