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Jiawei Sun

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9 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

AGI-Elo: How Far Are We From Mastering A Task?

Shuo Sun
Yimin Zhao
Christina Lee
Jiawei Sun
Chengran Yuan
Zefan Huang
Dongen Li
Justin Yeoh

As the field progresses toward Artificial General Intelligence (AGI), there is a pressing need for more comprehensive and insightful evaluation frameworks that go beyond aggregate performance metrics. This paper introduces a unified rating system that jointly models the difficulty of individual test cases and the competency of AI models (or humans) across vision, language, and action domains. Unlike existing metrics that focus solely on models, our approach allows for fine-grained, difficulty-aware evaluations through competitive interactions between models and tasks, capturing both the long-tail distribution of real-world challenges and the competency gap between current models and full task mastery. We validate the generalizability and robustness of our system through extensive experiments on multiple established datasets and models across distinct AGI domains. The resulting rating distributions offer novel perspectives and interpretable insights into task difficulty, model progression, and the outstanding challenges that remain on the path to achieving full AGI task mastery. We have made our code and results publicly available at https: //ss47816. github. io/AGI-Elo/.

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ICRA Conference 2025 Conference Paper

AlignBot: Aligning VLM-Powered Customized Task Planning with User Reminders Through Fine-Tuning for Household Robots

Zhaxizhuoma
Pengan Chen
Ziniu Wu
Jiawei Sun
Dong Wang 0028
Peng Zhou 0018
Nieqing Cao
Yan Ding 0002

This paper presents AlignBot, a novel framework designed to optimize VLM-powered customized task planning for household robots by effectively aligning with user reminders. In domestic settings, aligning task planning with user reminders poses significant challenges due to the limited quantity, diversity, and multimodal nature of the reminders. To address these challenges, AlignBot employs a fine-tuned LLaVA-7B model, functioning as an adapter for GPT-40. This adapter model internalizes diverse forms of user reminders-such as personalized preferences, corrective guidance, and contextual assistance-into structured instruction-formatted cues that prompt GPT-40 in generating customized task plans. Additionally, AlignBot integrates a dynamic retrieval mechanism that selects task-relevant historical successes as prompts for GPT-40, further enhancing task planning accuracy. To validate the effectiveness of AlignBot, experiments are conducted in real-world household environments, which are constructed within the laboratory to replicate typical household settings. A multimodal dataset with over 1, 500 entries derived from volunteer reminders is used for training and evaluation. The results demonstrate that AlignBot significantly improves customized task planning, outperforming existing LLM- and VLM-powered planners by interpreting and aligning with user reminders, achieving 86. 8 % success rate compared to the vanilla GPT-40 baseline at 21. 6%, reflecting a 65% improvement and over four times greater effectiveness. Supplementary materials are available at: https://yding25.com/AlignBot/

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NeurIPS Conference 2025 Conference Paper

Contrastive Learning with Data Misalignment: Feature Purity, Training Dynamics and Theoretical Generalization Guarantees

Jiawei Sun
Shuai Zhang
Hongkang Li
Meng Wang

Contrastive learning is a powerful framework for learning discriminative representations from image-text pairs. Despite its success, its theoretical foundations, especially when the image-text pair exhibits misalignment, remain underexplored. This paper provides the first theoretical analysis of contrastive learning under data misalignment, proving how the ground-truth modality-paired features are amplified while spurious features are suppressed through the training dynamics analysis. Specifically, we study two nonlinear encoders trained jointly with a contrastive loss and demonstrate that noisy (or misaligned) data pairs result in mixed representations and degrade the model's generalization ability. In contrast, recaptioning and filtering improve the data alignment, which in turn purifies the features learned by neurons and subsequently enhances generalization. Our analysis identifies feature purity as a key factor in the success of contrastive learning and offers insights into how data quality and training procedures impact representation learning and downstream generalization. Theoretical insights are supported by experiments on standard benchmarks.

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NeurIPS Conference 2025 Conference Paper

GD$^2$: Robust Graph Learning under Label Noise via Dual-View Prediction Discrepancy

Kailai Li
Jiong Lou
Jiawei Sun
Honghong Zeng
Wen Li
Chentao Wu
Yuan Luo
Wei Zhao

Graph Neural Networks (GNNs) achieve strong performance in node classification tasks but exhibit substantial performance degradation under label noise. Despite recent advances in noise-robust learning, a principled approach that exploits the node-neighbor interdependencies inherent in graph data for label noise detection remains underexplored. To address this gap, we propose GD$^2$, a noise-aware \underline{G}raph learning framework that detects label noise by leveraging \underline{D}ual-view prediction \underline{D}iscrepancies. The framework contrasts the \textit{ego-view}, constructed from node-specific features, with the \textit{structure-view}, derived through the aggregation of neighboring representations. The resulting discrepancy captures disruptions in semantic coherence between individual node representations and the structural context, enabling effective identification of mislabeled nodes. Building upon this insight, we further introduce a view-specific training strategy that enhances noise detection by amplifying prediction divergence through differentiated view-specific supervision. Extensive experiments on multiple datasets and noise settings demonstrate that \name~achieves superior performance over state-of-the-art baselines.

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IJCAI Conference 2025 Conference Paper

Leveraging Peer-Informed Label Consistency for Robust Graph Neural Networks with Noisy Labels

Kailai Li
Jiawei Sun
Jiong Lou
Zhanbo Feng
Hefeng Zhou
Chentao Wu
Guangtao Xue
Wei Zhao

Graph Neural Networks (GNNs) excel in many applications but struggle when trained with noisy labels, especially as noise can propagate through the graph structure. Despite recent progress in developing robust GNNs, few methods exploit the intrinsic properties of graph data to filter out noise. In this paper, we introduce ProCon, a novel framework that identifies mislabeled nodes by measuring label consistency among semantically similar peers, which are determined by feature similarity and graph adjacency. Mislabeled nodes typically exhibit lower consistency with these peers, a signal we measure using pseudo-labels derived from representational prototypes. A Gaussian Mixture Model is fitted to the consistency distribution to identify clean samples, which refine prototype quality in an iterative feedback loop. Experiments on multiple datasets demonstrate that ProCon significantly outperforms state-of-the-art methods, effectively mitigating label noise and enhancing GNN robustness.

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TMLR Journal 2025 Journal Article

Theoretical Learning Performance of Graph Networks: the Impact of Jumping Connections and Layer-wise Sparsification

Jiawei Sun
Hongkang Li
Meng Wang

Jumping connections enable Graph Convolutional Networks (GCNs) to overcome over-smoothing, while graph sparsification reduces computational demands by selecting a submatrix of the graph adjacency matrix during neighborhood aggregation. Learning GCNs with graph sparsification has shown empirical success across various applications, but a theoretical understanding of the generalization guarantees remains limited, with existing analyses ignoring either graph sparsification or jumping connections. This paper presents the first learning dynamics and generalization analysis of GCNs with jumping connections using graph sparsification. Our analysis demonstrates that the generalization accuracy of the learned model closely approximates the highest achievable accuracy within a broad class of target functions dependent on the proposed sparse effective adjacency matrix $A^*$. Thus, graph sparsification maintains generalization performance when $A^*$ accurately models data correlations. We reveal that jumping connections lead to different sparsification requirements across layers. In a two-hidden-layer GCN, the generalization is more affected by the sparsified matrix deviations from $A^*$ of the first layer than the second layer. To the best of our knowledge, this marks the first theoretical characterization of jumping connections' role in sparsification requirements. We validate our theoretical results on benchmark datasets in deep GCNs.

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NeurIPS Conference 2024 Conference Paper

Empowering and Assessing the Utility of Large Language Models in Crop Science

Hang Zhang
Jiawei Sun
Renqi Chen
Wei Liu
Zhonghang Yuan
Xinzhe Zheng
Zhefan Wang
Zhiyuan Yang

Large language models (LLMs) have demonstrated remarkable efficacy across knowledge-intensive tasks. Nevertheless, their untapped potential in crop science presents an opportunity for advancement. To narrow this gap, we introduce CROP, which includes a novel instruction tuning dataset specifically designed to enhance LLMs’ professional capabilities in the crop science sector, along with a benchmark that serves as a comprehensive evaluation of LLMs’ understanding of the domain knowledge. The CROP dataset is curated through a task-oriented and LLM-human integrated pipeline, comprising 210, 038 single-turn and 1, 871 multi-turn dialogues related to crop science scenarios. The CROP benchmark includes 5, 045 multiple-choice questions covering three difficulty levels. Our experiments based on the CROP benchmark demonstrate notable enhancements in crop science-related tasks when LLMs are fine-tuned with the CROP dataset. To the best of our knowledge, CROP dataset is the first-ever instruction tuning dataset in the crop science domain. We anticipate that CROP will accelerate the adoption of LLMs in the domain of crop science, ultimately contributing to global food production.

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ICRA Conference 2024 Conference Paper

Energy Consumption Modelling of Coaxial-Rotor in Vortex Ring State for Controllable High-speed Descending

Jiawei Sun
Xiang Zhou
Taoze Ban
Jiannan Zhao
Feng Shuang 0002

The ability to fast climb and descend is crucial for Unmanned Aerial Vehicle (UAV) applications in the mountains. The slower descent speed will affect the UAV’s working efficiency in reaching the rescue area. However, during the fast descent of the rotorcraft, a chaotic flow field rampages as the rotorcraft falls into its wake flow. This is known as the vortex ring. Therefore, the safe descent velocity of consumer UAVs is usually limited to approximately 3m/s. This limitation reduces the potential of UAVs to execute tasks in mountainous and plateau regions. To broaden the task capability constrained by the maximum descending speed, it is necessary to jointly analyze the flow field and the energy consumption during descending. Existing research mainly focused on how to avoid entering the vortex ring instead of offering sufficient power to fly with it. In this paper, in order to achieve an efficient rotorcraft for rescuing in mountainous and plateaus, we break through the maximum-descending-speed of a coaxial rotors UAV. Hence, a power consumption managing pipeline is proposed to extend the power tolerance of the UAV. Specifically, a theoretic model for the coaxial rotors is proposed to analyze the induced velocity and energy consumption during vertical descending. Then, the theoretic model is verified to be consistent with the Computational Fluid Dynamics (CFD) and wind tunnel experiment results. Finally, we optimized the tolerance of the power and dynamic system according to the theoretic model. With this pipeline, our real-time flight achieved 8m/s controlled vertical-descent-speed (CVDS), which is a leading result in both quadrotors and coaxial UAVs.

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JBHI Journal 2024 Journal Article

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in $^{18}$F-FDG PET Images Based on Multi-Deep-Learning Models

Chunjun Qian
Chong Jiang
Kai Xie
Chongyang Ding
Yue Teng
Jiawei Sun
Liugang Gao
Zhengyang Zhou

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in $^{18}$ F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0. 770, 95% CI: 0. 705-0. 834, with feature adding fusion method and C-index: 0. 764, 95% CI: 0. 695-0. 832, with feature concatenate fusion method) and OS prediction (C-index: 0. 770 (0. 692-0. 848) and 0. 771 (0. 694-0. 849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

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