OpenRaiser/Intern-Atlas huggingface dataset:
Equivariant and invariant message passing for global subseasonal-to-seasonal forecasting
Authors: Yang Liu, Zinan Zheng, Yu Rong, Deli Zhao, Hong Cheng, Jia Li
Venue: Conference KDD 2025
Year: 2025

Accurate weather forecasting on Subseasonal-to-Seasonal (S2S) timescale is critical to human society such as agriculture planning and extreme weather preparation. Although data-driven models have become alternatives to computationally intensive Numerical Weather Prediction (NWP) systems, existing Transformer-based approaches suffer from biases due to planar projections distorting the spherical geometry and inadequate handling of vector-scalar variable interactions (e.g., wind velocity vs. temperature). To address these limitations, we propose a graph-based Equivariant and Invariant Message Passing (EIMP) framework that directly processes spherical grid data. It maintains SO(3) equivariant embeddings for vector data and SO(3) invariant embeddings for scalar data, which are interacted by a shared invariant message embedding. Guaranteed equivariant and invariant message aggregation functions are proposed to update embeddings under strict symmetry constraints. Extensive experiments on the Earth Reanalysis 5 (ERA5) reanalysis dataset of 41 years demonstrate the proposed model achieves significant improvement over advanced data-driven models and skillful numerical ECMWF systems. Additionally, we empirically show that EIMP demonstrates geometrically superior predictions and conduct ablation studies to validate the efficacy of its design.
Scihorizon: Benchmarking ai-for-science readiness from scientific data to large language models
Authors: Chuan Qin, Xin Chen, Chengrui Wang, Pengmin Wu, Xi Chen, Yihang Cheng, Jingyi Zhao, Meng Xiao, Xiangchao Dong, Qingqing Long, Boya Pan, Han Wu, Chengzan Li, Yuanchun Zhou, Hui Xiong, Hengshu Zhu
Venue: Conference KDD 2025
Year: 2025

In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discovery, establishing AI-for-Science (AI4Science) as a dynamic and evolving field. However, there is still a lack of an effective framework for the overall assessment of AI4Science, particularly from a holistic perspective on data quality and model capability. Therefore, in this study, we propose SciHorizon, a comprehensive assessment framework designed to benchmark the readiness of AI4Science from both scientific data and LLM perspectives. First, we introduce a generalizable framework for assessing AI-ready scientific data, encompassing four key dimensions: Quality, FAIRness, Explainability, and Compliance-which are subdivided into 15 sub-dimensions. Drawing on data resource papers published between 2018 and 2023 in peer-reviewed journals, we present recommendation lists of AI-ready datasets for Earth, Life, and Materials Sciences, making a novel and original contribution to the field. Concurrently, to assess the capabilities of LLMs across multiple scientific disciplines, we establish 16 assessment dimensions based on five core indicators Knowledge, Understanding, Reasoning, Multimodality, and Values spanning Mathematics, Physics, Chemistry, Life Sciences, and Earth and Space Sciences. Using the developed benchmark datasets, we have conducted a comprehensive evaluation of over 50 representative open-source and closed source LLMs. All the results are publicly available and can be accessed online at www.scihorizon.cn/en.
From missteps to mastery: Enhancing low-resource dense retrieval through adaptive query generation
Authors: Zhenyu Tong, Chuan Qin, Chuyu Fang, Kaichun Yao, Xi Chen, Jingshuai Zhang, Chen Zhu, Hengshu Zhu
Venue: Conference KDD 2025
Year: 2025

Document retrieval, designed to recall query-relevant documents from expansive collections, is essential for information-seeking tasks, such as web search and open-domain question-answering. Advances in representation learning and pretrained language models (PLMs) have driven a paradigm shift from traditional sparse retrieval methods to more effective dense retrieval approaches, forging enhanced semantic connections between queries and documents and establishing new performance benchmarks. However, reliance on extensive annotated document-query pairs limits their competitiveness in low-resource scenarios. Recent research efforts employing the few-shot capabilities of large language models (LLMs) and prompt engineering for synthetic data generation have emerged as a promising solution. Nonetheless, these approaches are hindered by the generation of lower-quality data within the conventional dense retrieval training process. To this end, in this paper, we introduce iGFT, a framework aimed at enhancing low-resource dense retrieval by integrating a three-phase process --- Generation, Filtering, and Tuning --- coupled with an iterative optimization strategy. Specifically, we first employ supervised fine-tuning on limited ground truth data, enabling an LLM to function as the generator capable of producing potential queries from given documents. Subsequently, we present a multi-stage filtering module to minimize noise in the generated data while retaining samples poised to significantly improve the dense retrieval model's performance in the follow-up fine-tuning process. Furthermore, we design a novel iterative optimization strategy that dynamically optimizes the query generator for producing more informative queries, thereby enhancing the efficacy of the entire framework. Finally, extensive experiments conducted on a series of publicly available retrieval benchmark datasets have demonstrated the effectiveness of the proposed iGFT.
Variable Interval Time Sequence Modeling for Career Trajectory Prediction: Deep Collaborative Perspective
Authors: Chao Wang, Hengshu Zhu, Qiming Hao, Keli Xiao, Hui Xiong
Venue: Conference WWW 2021
Year: 2021

In today’s fast-evolving job market, the timely and effective understanding of the career trajectories of talents can help them quickly develop necessary skills and make the right career transitions at the right time. However, it is a non-trivial task for developing a successful career trajectory prediction method, which should have the abilities for finding the right timing for job-hopping, identifying the right companies, and matching the right positions for the candidates. While people have been trying to develop solutions for providing some of the above abilities, there is no total solution or complete framework to integrate all these abilities together. To this end, in this paper, we propose a unified time-aware career trajectory prediction framework, namely TACTP, which is capable of jointly providing the above three abilities for better understanding the career trajectories of talents. Along this line, we first exploit a hierarchical deep sequential modeling network for career embedding and extract latent talent factors from multiple networks, which are designed with different functions of handling related issues of the timing, companies, and positions for job-hopping. Then, we perform collaborative filtering for generating personalized predictions. Furthermore, we propose a temporal encoding mechanism to handle dynamic temporal information so that TACTP is capable of generating time-aware predictions by addressing the challenges for variable interval time sequence modeling. Finally, we have conducted extensive experiments on large-scale real-world data to evaluate TACTP against the state-of-the-art baselines, and the results show that TACTP has advantages over baselines on all targeted tasks for career trajectory prediction.
Structure-enhanced protein instruction tuning: Towards general-purpose protein understanding with llms
Authors: Wei Wu, Chao Wang, Liyi Chen, Mingze Yin, Yiheng Zhu, Kun Fu, Jieping Ye, Hui Xiong, Zheng Wang
Venue: Conference KDD 2025
Year: 2025

Proteins, as essential biomolecules, play a central role in biological processes, including metabolic reactions and DNA replication. Accurate prediction of their properties and functions is crucial in biological applications. Recent development of protein language models (pLMs) with supervised fine tuning provides a promising solution to this problem. However, the fine-tuned model is tailored for particular downstream prediction task, and achieving general-purpose protein understanding remains a challenge. In this paper, we introduce Structure-Enhanced Protein Instruction Tuning (SEPIT) framework to bridge this gap. Our approach incorporates a novel structure-aware module into pLMs to enrich their structural knowledge, and subsequently integrates these enhanced pLMs with large language models (LLMs) to advance protein understanding. In this framework, we propose a novel instruction tuning pipeline. First, we warm up the enhanced pLMs using contrastive learning and structure denoising. Then, caption-based instructions are used to establish a basic understanding of proteins. Finally, we refine this understanding by employing a mixture of experts (MoEs) to capture more complex properties and functional information with the same number of activated parameters. Moreover, we construct the largest and most comprehensive protein instruction dataset to date, which allows us to train and evaluate the general-purpose protein understanding model. Extensive experiments on both open-ended generation and closed-set answer tasks demonstrate the superior performance of SEPIT over both closed-source general LLMs and open-source LLMs trained with protein knowledge.
Personalized federated recommendation for cold-start users via adaptive knowledge fusion
Authors: Yichen Li, Yijing Shan, Yi Liu, Haozhao Wang, Wei Wang, Yi Wang, Ruixuan Li
Venue: Conference WWW 2025
Year: 2025

Federated Recommendation System (FRS) usually offers recommendation services for users while keeping their data locally to ensure privacy. Currently, most FRS literature assumes that fixed users participate in federated training with personal IoT devices (e.g., mobile phones and PC). However, users may join incrementally, and retraining the entire FRS for each new participating user is unfeasible due to the high training costs and the limited global knowledge contribution from a small number of new users. To guarantee the quality service for these new users, we take a dive into the federated recommendation for cold-start users, a novel scenario where the new participating users can directly obtain a promising recommendation without comprehensive training with all participating users by leveraging both transferred knowledge from the converged warm clients and the knowledge learned from the local data. Nevertheless, the efficient transfer of knowledge from warm clients remains controversial. On the one hand, cold clients may introduce new sparse items, resulting in a shift in the item embedding distribution compared to that converged on warm clients. On the other hand, cold-start users need to match similar user information from warm clients for a collaborative recommendation, but directly sharing user information is a violation of privacy and unacceptable. To tackle these challenges, we propose an efficient and privacy-enhanced federated recommendation for cold-start users (FR-CSU) that each client can adaptively transfer both user and item knowledge separately from warm clients and implement recommendations with local and transferred knowledge fusion. Specifically, each cold client will train a mapping function locally to transfer the aligned item embedding. Meanwhile, warm clients will maintain a user prototype network collaboratively that provides privacy-friendly yet effective user information for cold-start users. Then, a linear function system will integrate the transferred and local knowledge to improve recommendations. Extensive experiments show that FR-CSU achieves superior performance compared to state-of-the-art methods.
Fosr: First-order spectral rewiring for addressing oversquashing in gnns
Authors: Kedar Karhadkar, Pradeep Kr. Banerjee, Guido Montúfar
Venue: Conference ICLR 2023
Year: 2023

Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph. While this allows GNNs to learn features depending on the graph structure, for certain graph topologies it leads to inefficient information propagation and a problem known as oversquashing. This has recently been linked with the curvature and spectral gap of the graph. On the other hand, adding edges to the message-passing graph can lead to increasingly similar node representations and a problem known as oversmoothing. We propose a computationally efficient algorithm that prevents oversquashing by systematically adding edges to the graph based on spectral expansion. We combine this with a relational architecture, which lets the GNN preserve the original graph structure and provably prevents oversmoothing. We find experimentally that our algorithm outperforms existing graph rewiring methods in several graph classification tasks.
Regression compatible listwise objectives for calibrated ranking with binary relevance
Authors: Aijun Bai, Rolf Jagerman, Zhen Qin, Le Yan, Pratyush Kar, Bing-Rong Lin, Xuanhui Wang, Michael Bendersky, Marc Najork
Venue: Conference CIKM 2023
Year: 2023

As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score-sensitive applications. Though a simple multi-objective approach that combines a regression and a ranking objective can effectively learn scale-calibrated scores, we argue that the two objectives are not necessarily compatible, which makes the trade-off less ideal for either of them. In this paper, we propose a practical regression compatible ranking (RCR) approach that achieves a better trade-off, where the two ranking and regression components are proved to be mutually aligned. Although the same idea applies to ranking with both binary and graded relevance, we mainly focus on binary labels in this paper. We evaluate the proposed approach on several public LTR benchmarks and show that it consistently achieves either best or competitive result in terms of both regression and ranking metrics, and significantly improves the Pareto frontiers in the context of multi-objective optimization. Furthermore, we evaluated the proposed approach on YouTube Search and found that it not only improved the ranking quality of the production pCTR model, but also brought gains to the click prediction accuracy. The proposed approach has been successfully deployed in the YouTube production system.
Kag: Boosting llms in professional domains via knowledge augmented generation
Authors: Lei Liang, Zhongpu Bo, Zhengke Gui, Zhongshu Zhu, Ling Zhong, Peilong Zhao, Mengshu Sun, Zhiqiang Zhang, Jun Zhou, Wenguang Chen, Wen Zhang, Huajun Chen
Venue: Conference WWW 2025
Year: 2025

The recently developed Retrieval-Augmented Generation (RAG) technology has enabled the efficient construction of domain-specific applications. The key technologies of RAG are retrieval based on similarity and reasoning based on next-token prediction. However, this approach differs significantly from how humans solve problems. Humans typically follow certain analytical logic, reasoning while retrieving relevant information, and then connecting the clues to serve as references, ultimately generating an answer. In this process, the focus is on the semantic type and clear relationships between the keywords rather than similarity and co-occurrence. This difference in methodology results in the answers generated by RAG technology being insufficiently accurate or valuable. In this work, we concentrate on establishing semantic relationships between keywords to enable a more precise expression of knowledge and propose the Knowledge Augmented Generation(KAG) framework. KAG performs semantic parsing and reasoning on both documents and questions, involving three specific strategies: In the indexing phase, we complete the semantic information of keywords and the semantic relationships between them through information extraction and semantic reasoning; in the reasoning phase of question answering, we leverage semantic parsing to transform questions into Logical Forms with clear semantic types and relationships; in the retrieval phase, we predict the semantic relationships between Logical Form elements and structured index, thereby obtaining the required references. We compared KAG with existing RAG methods in three multi-hop QA datasets and the results show that KAG significantly outperforms existing methods, achieving a new state-of-the-art. We also applied KAG to real E-Government Q&A business scenario, and achieving significant improvements in professionalism compared to traditional RAG methods. Meanwhile, to help developers easily build accurate and efficient domain knowledge QA services, our KAG natively supports the open-source KG engine OpenSPG.
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
Authors: Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao 0001, Jie Jiang 0015
Venue: Conference KDD 2024
Year: 2024

Click-through rate (CTR) prediction is a crucial area of research in online advertising. While binary cross entropy (BCE) has been widely used as the optimization objective for treating CTR prediction as a binary classification problem, recent advancements have shown that combining BCE loss with an auxiliary ranking loss can significantly improve performance. However, the full effectiveness of this combination loss is not yet fully understood. In this paper, we uncover a new challenge associated with the BCE loss in scenarios where positive feedback is sparse: the issue of gradient vanishing for negative samples. We introduce a novel perspective on the effectiveness of the auxiliary ranking loss in CTR prediction: it generates larger gradients on negative samples, thereby mitigating the optimization difficulties when using the BCE loss only and resulting in improved classification ability. To validate our perspective, we conduct theoretical analysis and extensive empirical evaluations on public datasets. Additionally, we successfully integrate the ranking loss into Tencent's online advertising system, achieving notable lifts of 0.70% and 1.26% in Gross Merchandise Value (GMV) for two main scenarios. The code is openly accessible at: https://github.com/SkylerLinn/Understanding-the-Ranking-Loss.
Retrieval-enhanced generative model for large-scale knowledge graph completion
Authors: Donghan Yu, Yiming Yang
Venue: Conference SIGIR 2023
Year: 2023

The task of knowledge graph completion (KGC) is of great importance. To achieve scalability when dealing with large-scale knowledge graphs, recent works formulate KGC as a sequence-to-sequence process, where the incomplete triplet (input) and the missing entity (output) are both verbalized as text sequences. However, inference with these methods relies solely on the model parameters for implicit reasoning and neglects the use of KG itself, which limits the performance since the model lacks the capacity to memorize a vast number of triplets. To tackle this issue, we introduce ReSKGC, a Retrieval-enhanced Seq2seq KGC model, which selects semantically relevant triplets from the KG and uses them as evidence to guide output generation with explicit reasoning. Our method has demonstrated state-of-the-art performance on benchmark datasets Wikidata5M and WikiKG90Mv2, which contain about 5M and 90M entities, respectively.
Agentsociety challenge: Designing llm agents for user modeling and recommendation on web platforms
Authors: Yuwei Yan, Yu Shang, Qingbin Zeng, Yu Li, Keyu Zhao, Zhiheng Zheng, Xuefei Ning, Tianji Wu, Shengen Yan, Yu Wang, Fengli Xu, Yong Li
Venue: Conference WWW 2025
Year: 2025

The AgentSociety Challenge is the first competition in the Web Conference that aims to explore the potential of Large Language Model (LLM) agents in modeling user behavior and enhancing recommender systems on web platforms. The Challenge consists of two tracks: the User Modeling Track and the Recommendation Track. Participants are tasked to utilize a combined dataset from Yelp, Amazon, and Goodreads, along with an interactive environment simulator, to develop innovative LLM agents. The Challenge has attracted 295 teams across the globe and received over 1,400 submissions in total over the course of 37 official competition days. The participants have achieved 21.9% and 20.3% performance improvement for Track 1 and Track 2 in the Development Phase, and 9.1% and 15.9% in the Final Phase, representing a significant accomplishment. This paper discusses the detailed designs of the Challenge, analyzes the outcomes, and highlights the most successful LLM agent designs. To support further research and development, we have open-sourced the benchmark environment at https://tsinghua-fib-lab.github.io/AgentSocietyChallenge.
Non-compliant bandits
Authors: Branislav Kveton, Yi Liu, Johan Matteo Kruijssen, Yisu Nie
Venue: Conference CIKM 2023
Year: 2023

Bandit algorithms arose as a standard approach to learning better models online. As they become more popular, they are increasingly deployed in complex machine learning pipelines, where their actions can be overwritten. For example, in ranking problems, a list of recommended items can be modified by a downstream algorithm to increase diversity. This may break the classic bandit algorithms and lead to linear regret. Specifically, if the proposed action is not taken, uncertainty in its estimated mean reward may not get reduced. In this work, we study this setting and call it non-compliant bandits; as the agent tries to learn rewarding actions that comply with a downstream task. We propose two algorithms, compliant contextual UCB (CompUCB) and Thompson sampling (CompTS), which learn separate reward and compliance models. The compliance model allows the agent to avoid non-compliant actions. We derive a sublinear regret bound for CompUCB. We also conduct experiments that compare our algorithms to classic bandit baselines. The experiments show failures of the baselines and that we mitigate them by learning compliance models.
When more is less: the paradox of choice in search engine use
Authors: Antti Oulasvirta, Janne P. Hukkinen, Barry Schwartz
Venue: Conference SIGIR 2009
Year: 2009

In numerous everyday domains, it has been demonstrated that increasing the number of options beyond a handful can lead to paralysis and poor choice and decrease satisfaction with the choice. Were this so-called paradox of choice to hold in search engine use, it would mean that increasing recall can actually work counter to user satisfaction if it implies choice from a more extensive set of result items. The existence of this effect was demonstrated in an experiment where users (N=24) were shown a search scenario and a query and were required to choose the best result item within 30 seconds. Having to choose from six results yielded both higher subjective satisfaction with the choice and greater confidence in its correctness than when there were 24 items on the results page. We discuss this finding in the wider context of "choice architecture"--that is, how result presentation affects choice and satisfaction.