OpenRaiser/Intern-Atlas huggingface dataset:
A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments
Authors: Yuanhao Liu, Shuo Liu, Yimeng Liu, Chanjin Zheng, Wei Zhang, Hong Qian
Venue: KDD
Year: 2025

Cognitive diagnosis model (CDM) is a fundamental component in intelligent education systems which aims to infer students' mastery levels based on historical response logs. However, existing CDMs usually follow the ID-based embedding paradigm, which could often diminish the effectiveness of CDMs in open student learning environments. This is mainly because most of them cannot directly infer new students' ability or utilize new exercises or knowledge concepts without retraining. Textual semantic information, due to its unified feature space and easy accessibility, can help alleviate this issue. However, directly incorporating textual semantic information may not benefit traditional CDMs due to the following challenges: the diversity and complexity of the original text corpus, lack of response-relevant features, and difficulty in integrating multi-source features. To this end, this paper proposes a Dual-Fusion Cognitive Diagnosis Framework (DFCD) to address the above challenges in open student learning environments. Specifically, to standardize the original text corpus and make it easier for CDMs to capture relevant textual semantic information, this paper first proposes the exercise-refiner and concept-refiner to make the exercises and knowledge concepts more coherent and reasonable in educational scenario via large language models. Then, DFCD encodes the refined features using text embedding models to obtain the textual semantic features. To construct response-relevant features, we propose a unified response-relevant feature construction to fully incorporate the information within the response logs. Finally, DFCD designs a dual-fusion module to merge the features from two sources, namely textual semantic features and response-relevant features. The ultimate representations possess the capability of inference in open student learning environments and can be also plugged in existing CDMs. Extensive experiments across three real-world datasets show that DFCD achieves superior performance and strong adaptability by improving the performance in three different scenarios of open student learning environments around 5% on average.
Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
Authors: Xiaoou Liu, Tiejin Chen, Longchao Da, Chacha Chen, Zhen Lin, Hua Wei
Venue: KDD
Year: 2025

Uncertainty quantification (UQ) enhances the reliability of Large Language Models (LLMs) by estimating confidence in outputs, enabling risk mitigation and selective prediction. However, traditional UQ methods struggle with LLMs due to computational constraints and decoding inconsistencies. Moreover, LLMs introduce unique uncertainty sources, such as input ambiguity, reasoning path divergence, and decoding stochasticity, that extend beyond classical aleatoric and epistemic uncertainty. To address this, we introduce a new taxonomy that categorizes UQ methods based on computational efficiency and uncertainty dimensions, including input, reasoning, parameter, and prediction uncertainty. We evaluate existing techniques, summarize existing benchmarks and metrics for UQ, assess their real-world applicability, and identify open challenges, emphasizing the need for scalable, interpretable, and robust UQ approaches to enhance LLM reliability.
SEPTQ: A Simple and Effective Post-Training Quantization Paradigm for Large Language Models
Authors: Han Liu, Haotian Gao, Xiaotong Zhang, Changya Li, Feng Zhang, Wei Wang, Fenglong Ma, Hong Yu
Venue: KDD
Year: 2025

Large language models (LLMs) have shown remarkable performance in various domains, but they are constrained by massive computational and storage costs. Quantization, an effective technique for compressing models to fit resource-limited devices while preserving generative quality, encompasses two primary methods: quantization aware training (QAT) and post-training quantization (PTQ). QAT involves additional retraining or fine-tuning, thus inevitably resulting in high training cost and making it unsuitable for LLMs. Consequently, PTQ has become the research hotspot in recent quantization methods. However, existing PTQ methods usually rely on various complex computation procedures and suffer from considerable performance degradation under low-bit quantization settings. To alleviate the above issues, we propose a simple and effective post-training quantization paradigm for LLMs, named SEPTQ. Specifically, SEPTQ first calculates the importance score for each element in the weight matrix and determines the quantization locations in a static global manner. Then it utilizes the mask matrix which represents the important locations to quantize and update the associated weights column-by-column until the appropriate quantized weight matrix is obtained. Compared with previous methods, SEPTQ simplifies the post-training quantization procedure into only two steps, and considers the effectiveness and efficiency simultaneously. Experimental results on various datasets across a suite of models ranging from millions to billions in different quantization bit-levels demonstrate that SEPTQ significantly outperforms other strong baselines, especially in low-bit quantization scenarios.
ZooplanktonBench: A Geo-Aware Zooplankton Recognition and Classification Dataset from Marine Observations
Authors: Fukun Liu, Adam T. Greer, Gengchen Mai, Jin Sun
Venue: KDD
Year: 2025

Plankton are small drifting organisms found throughout the world's oceans and can be indicators of ocean health. One component of this plankton community is the zooplankton, which includes gelatinous animals and crustaceans (e.g. shrimp), as well as the early life stages (i.e., eggs and larvae) of many commercially important fishes. Being able to monitor zooplankton abundances accurately and understand how populations change in relation to ocean conditions is invaluable to marine science research, with important implications for future marine seafood productivity. While new imaging technologies generate massive amounts of video data of zooplankton, analyzing them using general-purpose computer vision tools turns out to be highly challenging due to the high similarity in appearance between the zooplankton and its background (e.g., marine snow). In this work, we present the ZooplanktonBench, a benchmark dataset containing images and videos of zooplankton associated with rich geospatial metadata (e.g., geographic coordinates, depth, etc.) in various water ecosystems. ZooplanktonBench defines a collection of tasks to detect, classify, and track zooplankton in challenging settings, including highly cluttered environments, living vs non-living classification, objects with similar shapes, and relatively small objects. Our dataset presents unique challenges and opportunities for state-of-the-art computer vision systems to evolve and improve visual understanding in dynamic environments characterized by significant variation and the need for geo-awareness. The code and settings described in this paper can be found on our website: https://lfk118.github.io/ZooplanktonBench_Webpage.
Automatic Radiotherapy Treatment Planning with Deep Functional Reinforcement Learning
Authors: Bin Liu, Yu Liu, Zhiqian Li, Jianghong Xiao, Guosheng Yin, Huazhen Lin
Venue: KDD
Year: 2025

Intensity-modulated radiation therapy (IMRT) is a crucial radiotherapy technique, which is often formulated as an optimization problem. However, when the constraints are too restrictive to provide a feasible solution, human planners resort to relaxing the optimization parameters and re-evaluating the problem until an acceptable solution is obtained. However, this process is laborious and time-consuming, which has prompted attempts to automate radiotherapy through inverse planning studies using reinforcement learning. Unfortunately, these studies face two major limitations. First, a separate sub-network must be designed for each organ, rendering it difficult to apply to patients with an inconsistent number of structures. Second, the low signal-to-noise input and discrete action space result in low training efficiency. To address these issues, we propose an organ-sharing network that contains a functional embedding layer to extract curve features of the dose-volume histogram. It outputs continuous actions that can adjust the optimization parameters, thereby automating the radiotherapy planning process. The results from a cervical cancer dataset demonstrate the feasibility and efficiency of the proposed model in real-world radiotherapy. The code is available on https://github.com/Cissise/FatPIN.
MethaneS2CM: A Dataset for Multispectral Deep Methane Emission Detection
Authors: Hongxuan Liu, Juliana Y. Leung, Di Niu
Venue: KDD
Year: 2025

The detection of methane emissions has drawn significant attention from researchers, as methane is one of the main contributors to global warming. Due to their high accessibility and extensive coverage, satellite-based remote sensing data have become a widely used tool for studying methane emissions. The multispectral satellite Sentinel-2 is particularly prominent for studies related to methane, as it provides shortwave infrared (SWIR) spectral band data that align with the methane absorption line. Previous studies on methane emission detection using Sentinel-2 data rely mainly on synthetic data, which involves simulated methane emission plumes overlaid on real Sentinel-2 backgrounds. However, our analysis reveals that Sentinel-2 images with simulated plumes exhibit different data distributions and visual characteristics from those of real-world data. Moreover, these studies typically use only a single reference image as a clean background along with the plume image, which we find to be insufficient. Finally, the lack of publicly available benchmarks hinders research and development in this domain. To address these challenges, we introduce the MethaneS2CM dataset for methane emission detection, collected from Sentinel-2 multispectral data and the CarbonMapper platform. Our dataset consists of two main products (L1C and L2A) of Sentinel-2, including over 4000 plumes from 43 countries, ranging from 2016 to 2024. The dataset incorporates both long-term and short-term variations in Sentinel-2 images, with each sample consisting of images captured at different time periods. Furthermore, we conduct extensive experiments to evaluate a wide range of existing methane emission detection methods and models based on this dataset. We subsequently propose a deep methane emission classification model, MEECNet. Experimental results demonstrate that MEECNet outperforms all baseline methods. Our dataset is publicly available at https://huggingface.co/datasets/H1deaki/MethaneS2CM.
UQABench: Evaluating User Embedding for Prompting LLMs in Personalized Question Answering
Authors: Langming Liu, Shilei Liu, Yujin Yuan, Yizhen Zhang, Bencheng Yan, Zhiyuan Zeng, Zihao Wang, Jiaqi Liu, Di Wang, Wenbo Su, Pengjie Wang, Jian Xu, Bo Zheng
Venue: KDD
Year: 2025

Large language models (LLMs) achieve remarkable success in natural language processing (NLP). In practical scenarios like recommendations, as users increasingly seek personalized experiences, it becomes crucial to incorporate user interaction history into the context of LLMs to enhance personalization. However, from a practical utility perspective, user interactions' extensive length and noise present challenges when used directly as text prompts. A promising solution is to compress and distill interactions into compact embeddings, serving as soft prompts to assist LLMs in generating personalized responses. Although this approach brings efficiency, a critical concern emerges: Can user embeddings adequately capture valuable information and prompt LLMs? To address this concern, we propose UQABench, a benchmark designed to evaluate the effectiveness of user embeddings in prompting LLMs for personalization. We establish a fair and standardized evaluation process, encompassing pre-training, fine-tuning, and evaluation stages. To thoroughly evaluate user embeddings, we design three dimensions of tasks: sequence understanding, action prediction, and interest perception. These evaluation tasks cover the industry's demands in traditional recommendation tasks, such as improving prediction accuracy, and its aspirations for LLM-based methods, such as accurately understanding user interests and enhancing the user experience. We conduct extensive experiments on various state-of-the-art methods for modeling user embeddings. Additionally, we reveal the scaling laws of leveraging user embeddings to prompt LLMs. The benchmark is available online at https://github.com/OpenStellarTeam/UQABench.
Dynamic Learnware Filtering for Efficient Learnware Identification and System Slimming
Authors: Jian-Dong Liu, Zhi-Hao Tan, Zhi-Hua Zhou
Venue: KDD
Year: 2025

The learnware paradigm proposed by Zhou [34] aims to solve machine learning tasks by leveraging numerous existing high-performing models instead of training from scratch. These models are accommodated in a learnware dock system, where each learnware consists of a model and a specification that characterizes the model's utility, enabling it to be identified for future tasks. A critical challenge in this paradigm remains unresolved: determining what models can be or should be admitted to the system. Without well-established admission criteria, the uncontrolled growth of uploaded models could lead to significant redundancy and inefficiency, resulting in higher storage overhead, increased computational costs, and even potential system failure. To address this gap, this paper presents the first attempt to establish learnware admission criteria and dynamically filter redundant learnwares based on model capability coverage. Specifically, we organize task information from all learnwares into a tree-based structure to assess model capabilities across a continuously expanding task set. Using this structure and model capability representation, we develop an efficient and scalable method for detecting redundant learnwares dynamically without traversing the entire system. Theoretical analysis and extensive experiments involving over ten thousand simulated learnwares validate the efficacy and efficiency of our approach.
Enhanced Insurance Claim Prediction via Decoupled Graph Neural Networks with Pseudo Labeling
Authors: Hongrui Liu, Daixin Wang, Yifan Wu, Zhiqiang Zhang, Xiaobo Qin
Venue: KDD
Year: 2025

As the demand for insurance continues to skyrocket in our daily lives, accurately predicting claim amount has become a critical demand for insurance companies. This capacity enables to identify high-risk individuals for minimizing substantial claims and, conversely, help to lower premiums for other policyholders. Recently, graph neural networks (GNNs) have achieved remarkable performance on classification and recommendation for insurance, but have not been extended to this regression task. Will GNNs serve as a powerful tool for claim amount prediction? Surprisingly, our research reveals that GNNs perform well in this area, but encounter three challenges, i.e., the mixture of heterophilic and homophilous network pattern, label scarcity and label uncertainty. To address these challenges, we propose Decoupled Graph Neural Networks Enhanced by Pseudo Labels for Claim Prediction (GClaim) to empower existing GNNs for claim amount prediction. Specifically, GClaim automatically organizing nodes into distinct clusters, facilitating the independent learning of nodes within each latent network pattern. It further introduces trustworthy pseudo labels from unlabeled and uncertain nodes through a standard deviation-induced filtering strategy. Extensive experiments on three industrial datasets and five newly developed proxy public datasets with varying evaluation protocols demonstrate the effectiveness of GClaim, as well as each well-designed component in GClaim. In light of its outstanding performance, GClaim has been successfully deployed in the online auto-insurance platform of Alipay. With GClaim, the auto-insurance service has experienced an over 10% increase in the end-to-end conversion rate and over 25% rise in UV value.
Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
Authors: Langming Liu, Wanyu Wang, Chi Zhang, Bo Li, Hongzhi Yin, Xuetao Wei, Wenbo Su, Bo Zheng, Xiangyu Zhao
Venue: KDD
Year: 2025

Online advertising in recommendation platforms has gained significant attention, with a predominant focus on channel recommendation and budget allocation strategies. However, current offline reinforcement learning (RL) methods face substantial challenges when applied to sparse advertising scenarios, primarily due to severe overestimation, distributional shifts, and overlooking budget constraints. To address these issues, we propose MTORL, a novel multi-task offline RL model that targets two key objectives. First, we establish a Markov Decision Process (MDP) framework specific to the nuances of advertising. Then, we develop a causal state encoder to capture dynamic user interests and temporal dependencies, facilitating offline RL through conditional sequence modeling. Causal attention mechanisms are introduced to enhance user sequence representations by identifying correlations among causal states. We employ multi-task learning to decode actions and rewards, simultaneously addressing channel recommendation and budget allocation. Notably, our framework includes an automated system for integrating these tasks into online advertising. Extensive experiments on offline and online environments demonstrate MTORL's superiority over state-of-the-art methods. The code is available online at https://github.com/Applied-Machine-Learning-Lab/MTORL.
Stochastic Diffusion: A Diffusion Based Model for Stochastic Time Series Forecasting
Authors: Yuansan Liu, Sudanthi N. R. Wijewickrema, Dongting Hu, Christofer Bester, Stephen J. O&apos, Leary, James Bailey
Venue: KDD
Year: 2025

Recent successes in diffusion probabilistic models have demonstrated their strength in modeling and generating different types of data, paving the way for their application in generative time series forecasting. However, most existing diffusion based approaches rely on sequential models and unimodal latent variables to capture global dependencies and model entire observable data, resulting in difficulties when it comes to highly stochastic time series data. In this paper, we propose a novel Stochastic Diffusion (StochDiff) model that integrates the diffusion process into time series modeling stage and utilizes the representational power of the stochastic latent spaces to capture the variability of the stochastic time series data. Specifically, the model applies diffusion module at each time step within the sequential framework and learns a step-wise, data-driven prior for generative diffusion process. These features enable the model to effectively capture complex temporal dynamics and the multi-modal nature of the highly stochastic time series data. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of our proposed model for probabilistic time series forecasting, particularly in scenarios with high stochasticity. Additionally, with a real-world surgical use case, we highlight the model's potential in a medical application.
Fine-tuning Multimodal Large Language Models for Product Bundling
Authors: Xiaohao Liu, Jie Wu, Zhulin Tao, Yunshan Ma, Yinwei Wei, Tat-Seng Chua
Venue: KDD
Year: 2025

Recent advances in product bundling have leveraged multimodal information through sophisticated encoders, but remain constrained by limited semantic understanding and a narrow scope of knowledge. Therefore, some attempts employ In-context Learning (ICL) to explore the potential of large language models (LLMs) for their extensive knowledge and complex reasoning abilities. However, these efforts are inadequate in understanding mulitmodal data and exploiting LLMs' knowledge for product bundling. To bridge the gap, we introduce Bundle-MLLM, a novel framework that fine-tunes LLMs through a hybrid item tokenization approach within a well-designed optimization strategy. Specifically, we integrate textual, media, and relational data into a unified tokenization, introducing a soft separation token to distinguish between textual and non-textual tokens. Additionally, a streamlined yet powerful multimodal fusion module is employed to embed all non-textual features into a single, informative token, significantly boosting efficiency. To tailor product bundling tasks for LLMs, we reformulate the task as a multiple-choice question with candidate items as options. We further propose a progressive optimization strategy that fine-tunes LLMs for disentangled objectives: learning bundle patterns and enhancing multimodal semantic understanding specific to product bundling. Extensive experiments demonstrate that our approach outperforms a range of state-of-the-art (SOTA) methods. Codes are available at https://github.com/Xiaohao-Liu/Bundle-MLLM
Scenario Shared Instance Modeling for Click-through Rate Prediction
Authors: Dugang Liu, Chaohua Yang, Yuwen Fu, Xing Tang, Gongfu Li, Fuyuan Lyu, Xiuqiang He, Zhong Ming
Venue: KDD
Year: 2025

Multi-scenario recommendation (MSR) is a popular training paradigm in industrial platforms for uniformly integrating information from multiple scenarios and serving them simultaneously. A key challenge in MSR research is accurately identifying the commonalities and distinctive information between scenarios. Currently, most existing MSR methods focus on implicitly extracting this information from the architectural level. However, this continues to increase the complexity and training overhead of MSR. Furthermore, the custom components responsible for extracting implicit information in each MSR method are too dependent on the specific MSR architecture and are not easily reused in other methods. Given these challenges, we first show in a motivating experiment that it may be beneficial to explicitly select a reasonable set of shared instances that can affect parameter optimization in all scenarios during the training of MSR, i.e., to explicitly obtain the critical information required for MSR from the data level. Then, this paper proposes SSIM with an adaptive selection network. Specifically, SSIM can be integrated with existing MSR methods in a lightweight way to adaptively select an informative and shareable subset of instances from each scenario to improve recommendations. In particular, the selected multi-scenario shared subset has extraordinary reusability and can be easily saved to benefit model training of various future MSR models. Finally, we evaluate SSIM and demonstrate its effectiveness through experiments on two public multi-scenario benchmarks and an online A/B test.