OpenRaiser/Intern-Atlas huggingface dataset:
Combining Denoised Neural Network and Genetic Symbolic Regression for Memory Behavior Modeling via Dynamic Asynchronous Optimization
Authors: Jianwen Sun, Qirong Chen, Zhenya Huang, Zhihai Hu, Ruxia Liang, Xiaoxuan Shen
Venue: KDD
Year: 2025

Memory behavior modeling is a key topic in cognitive psychology and education. Traditional approaches use experimental data to build memory equations, but these models often lack precision and are debated in form. Recently, data-driven methods have improved predictive accuracy but struggle with interpretability, limiting cognitive insights. Although knowledge-informed neural networks have succeeded in fields like physics, their use in behavior modeling is still limited. This paper proposes a Self-evolving Psychology-informed Neural Network (SPsyINN), which leverages classical memory equations as knowledge modules to constrain neural network training. To address challenges such as the difficulty in quantifying descriptors and the limited interpretability of classical memory equations, a genetic symbolic regression algorithm is introduced to conduct evolutionary searches for more optimal expressions based on classical memory equations, enabling the mutual progress of the knowledge module and the neural network module. Specifically, the proposed approach combines genetic symbolic regression and neural networks in a parallel training framework, with a dynamic joint optimization loss function ensuring effective knowledge alignment between the two modules. Then, for addressing the training efficiency differences arising from the distinct optimization methods and computational hardware requirements of genetic algorithms and neural networks, an asynchronous interaction mechanism mediated by proxy data is developed to facilitate effective communication between modules and improve optimization efficiency. Finally, a denoising module is integrated into the neural network to enhance robustness against data noise and improve generalization performance. Experimental results on five large-scale real-world memory behavior demonstrate that SPsyINN outperforms state-of-the-art methods in predictive accuracy. Ablation studies confirm the model's co-evolution capability, improving accuracy while discovering more interpretable memory equations, showing its potential for psychological research. Our code is released at: https://github.com/JiaqiDijon/SPsyINN
FlexiReg: Flexible Urban Region Representation Learning
Authors: Fengze Sun, Yanchuan Chang, Egemen Tanin, Shanika Karunasekera, Jianzhong Qi
Venue: KDD
Year: 2025

The increasing availability of urban data offers new opportunities for learning region representations, which can be used as input to machine learning models for downstream tasks such as check-in or crime prediction. While existing solutions have produced promising results, an issue is their fixed formation of regions and fixed input region features, which may not suit the needs of different downstream tasks. To address this limitation, we propose a model named FlexiReg for urban region representation learning that is flexible with both the formation of urban regions and the input region features. FlexiReg is based on a spatial grid partitioning over the spatial area of interest. It learns representations for the grid cells, leveraging publicly accessible data, including POI, land use, satellite imagery, and street view imagery. We propose adaptive aggregation to fuse the cell representations and prompt learning techniques to tailor the representations towards different tasks, addressing the needs of varying formations of urban regions and downstream tasks. Extensive experiments on five real-world datasets demonstrate that FlexiReg outperforms state-of-the-art models by up to 202% in term of the accuracy of four diverse downstream tasks using the produced urban region representations.
Generalizable Graph Prompt Learning Framework with Model-level Prompt Injection and Two-Stage Prompt Tuning
Authors: Mingchen Sun, Jiahui Hou, Yutong Zhang, Yingji Li, Ying Wang
Venue: KDD
Year: 2025

Graph prompt learning represents a novel paradigm aimed at enhancing the performance of graph learning models on a variety of downstream tasks by providing specific graph prompts. Despite its promise, current graph prompt learning methods are limited by the following limitations. On the one hand, existing methods often rely on manually selected graph information or simple learnable vectors, which can introduce human biases and lack expressiveness. These methods also fall short in guiding models to induce historical prior knowledge and improve generalization. Furthermore, the direct end-to-end tuning strategy of prompts lacks a necessary gentle transition, which impacts model stability and generalization. To overcome these limitations, we introduce the generalizable graph prompt learning framework (GGPL), which incorporates model-level prompt injection and a two-stage prompt tuning strategy. GGPL focuses on encoding subgraph structures and attributes during pre-training and uses SimGRACE to predict subgraph similarities, enhancing the base model's generalization. The model-level prompt injection module, with its prompt embedding backbone and self-prompt generation, seamlessly integrates invariant knowledge. Our two-stage tuning strategy, including transition and task-specific tuning, ensures better guidance and stability. By designing learnable prompt tokens and fine-tuning them with task-specific information, GGPL enables the model to generalize more robustly to downstream tasks. We conduct extensive experiments on six benchmark datasets to verify the model's effectiveness.
Unifying Adversarial Multi-Deconfounded Learning Paradigm for Fake News Detection
Authors: Zixun Sun, Mingye Xu, Guanming Liang, Qi Liu
Venue: KDD
Year: 2025

In the task of fake news detection, ensuring authenticity and accuracy is of paramount importance. This task, however, is susceptible to the influence of confounders, necessitating effective confounder debiasing strategies. Conventional methods are typically designed to address specific confounders, resulting in frameworks that relatively lack generalization and overlook potential correlations among confounders. The presence of multiple confounders further escalates the complexity and challenges of debiasing learning. To tackle this issue, we introduce the Adversarial Multi-Deconfounded (AMD) Learning Paradigm, a generic training framework designed to eliminate biases from multiple confounders. Our approach leverages adversarial networks to extract confounder-invariant feature representations, guiding the model to ignore potential biases introduced by confounders and extract stable representations independent of these confounders, thereby enhancing generalization. Comprehensive experiments demonstrate that our approach outperforms state-of-the-art methods on the Weibo and GossipCop datasets, and significantly exceeds other methods in generalization evaluation on CHEF. Additionally, we validate that our AMD framework exhibits improved robustness against confounders.
Evaluating Generative Models via Cubical Homology based Persistent Entropy
Authors: Suryaka Suresh, Vinayak Abrol
Venue: KDD
Year: 2025

Topological tools have become popular in improving and evaluating the performance of generative models by exploring the connection between their representation power and topological properties. This has led to the development of various measures that can assess the diversity and quality of generated data. However, existing methods are impractical in higher dimensions and large-scale datasets/models. To address this, we propose a scalable framework based on persistent entropy. We first establish a theoretical relation between the homological complexity of the underlying topology and the persistent entropy. We then empirically study the topological transformation during training of the generated data manifold using cubical homology. The proposed method is domain & model-agnostic and scales well for various neural architectures at different depths.
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
Authors: Yuni Susanti, Michael Färber
Venue: KDD
Year: 2025

Inferring causal relationships between variable pairs is crucial for understanding multivariate interactions in complex systems. Knowledge-based causal discovery -which involves inferring causal relationships by reasoning over the metadata of variables (e.g., names or textual context)-offers a compelling alternative to traditional methods that rely on observational data. However, existing methods using Large Language Models (LLMs) often produce unstable and inconsistent results, compromising their reliability for causal inference. To address this, we introduce a novel approach that integrates Knowledge Graphs (KGs) with LLMs to enhance knowledge-based causal discovery. Our approach identifies informative metapath -based subgraphs within KGs and further refines the selection of these subgraphs using Learning-to-Rank-based models. The top-ranked subgraphs are then incorporated into zero-shot prompts, improving the effectiveness of LLMs in inferring the causal relationship. Extensive experiments on biomedical and open-domain datasets demonstrate that our method outperforms most baselines by up to 44.4 points in F1 scores, evaluated across diverse LLMs and KGs. Our code and datasets are available on GitHub.. https://github.com/susantiyuni/path-to-causality
Second Workshop on Generative AI for Recommender Systems and Personalization
Authors: Narges Tabari, Aniket Deshmukh, Wang-Cheng Kang, Julian J. McAuley, James Caverlee, Neil Shah, George Karypis
Venue: KDD
Year: 2025

Building personalized recommender systems is a cornerstone of the modern data mining and applied machine learning (ML) community. Modern online platforms have a confluence of data including user-item interaction graphs, user and item-associated semantics (text, visual content, etc.), and metadata. Recent advancements in generative models and semantic encoders via large language models (LLMs), visual and audio encoders have significantly impacted research in relevant domains, enabling new directions in knowledge discovery and ability of models to better incorporate semantic context. This workshop bridges the research gap between the use of generative models and recommendation for personalized systems. We will focus on topics spanning the interplay between such models and conventional personalized systems.
DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models
Authors: Niloufar Alipour Talemi, Hossein Kashiani, Hossein R. Nowdeh, Fatemeh Afghah
Venue: KDD
Year: 2025

Prompt learning has emerged as a powerful paradigm for adapting vision-language models such as CLIP to downstream tasks. However, existing methods often overfit to seen data, leading to significant performance degradation when generalizing to novel classes or unseen domains. To address this limitation, we propose DiSa, a Directional Saliency-Aware Prompt Learning framework that integrates two complementary regularization strategies to enhance generalization. First, our Cross-Interactive Regularization (CIR) fosters cross-modal alignment by enabling cooperative learning between prompted and frozen encoders. Within CIR, a saliency-aware masking strategy guides the image encoder to prioritize semantically critical image regions, reducing reliance on less informative patches. Second, we introduce a directional regularization strategy that aligns visual embeddings with class-wise prototype features in a directional manner to prioritize consistency in feature orientation over strict proximity. This approach ensures robust generalization by leveraging stable prototype directions derived from class-mean statistics. Extensive evaluations on 11 diverse image classification benchmarks demonstrate that DiSa consistently outperforms state-of-the-art prompt learning methods across various settings, including base-to-novel generalization, cross-dataset transfer, domain generalization, and few-shot learning.
BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction
Authors: Ruifeng Tan, Weixiang Hong, Jiayue Tang, Xibin Lu, Ruijun Ma, Xiang Zheng, Jia Li, Jiaqiang Huang, Tong-Yi Zhang
Venue: KDD
Year: 2025

Battery Life Prediction (BLP), which relies on time series data produced by battery degradation tests, is crucial for battery utilization, optimization, and production. Despite impressive advancements, this research area faces three key challenges. Firstly, the limited size of existing datasets impedes insights into modern battery life data. Secondly, most datasets are restricted to small-capacity lithium-ion batteries tested under a narrow range of diversity in labs, raising concerns about the generalizability of findings. Thirdly, inconsistent and limited benchmarks across studies obscure the effectiveness of baselines and leave it unclear if models popular in other time series fields are effective for BLP. To address these challenges, we propose BatteryLife, a comprehensive dataset and benchmark for BLP. BatteryLife integrates 16 datasets, offering a 2.5 times sample size compared to the previous largest dataset, and provides the most diverse battery life resource with batteries from 8 formats, 59 chemical systems, 9 operating temperatures, and 421 charge/discharge protocols, including both laboratory and industrial tests. Notably, BatteryLife is the first to release battery life datasets of zinc-ion batteries, sodium-ion batteries, and industry-tested large-capacity lithium-ion batteries. With the comprehensive dataset, we revisit the effectiveness of baselines popular in this and other time series fields. Furthermore, we propose CyclePatch, a plug-in technique that can be employed in various neural networks. Extensive benchmarking of 18 methods reveals that models popular in other time series fields can be unsuitable for BLP, and CyclePatch consistently improves model performance establishing state-of-the-art benchmarks. Moreover, BatteryLife evaluates model performance across aging conditions and domains. BatteryLife is available at https://github.com/Ruifeng-Tan/BatteryLife.
Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity
Authors: Shiyin Tan, Dongyuan Li, Renhe Jiang, Zhen Wang, Xingtong Yu, Manabu Okumura
Venue: KDD
Year: 2025

Popularity bias occurs when popular items are recommended far more frequently than they should be, negatively impacting both user experience and recommendation accuracy. Existing debiasing methods mitigate popularity bias often uniformly across all users and only partially consider the time evolution of users or items. However, users have different levels of preference for item popularity, and this preference is evolving over time. To address these issues, we propose a novel method called CausalEPP (Causal Intervention on Evolving Personal Popularity) for taming recommendation bias, which accounts for the evolving personal popularity of users. Specifically, we first introduce a metric called Evolving Personal Popularity to quantify each user's preference for popular items. Then, we design a causal graph that integrates evolving personal popularity into the conformity effect, and apply deconfounded training to mitigate the popularity bias of the causal graph. During inference, we consider the evolution consistency between users and items to achieve a better recommendation. Empirical studies demonstrate that CausalEPP outperforms baseline methods in reducing popularity bias while improving recommendation accuracy.
Struct-X: Enhancing the Reasoning Capabilities of Large Language Models in Structured Data Scenarios
Authors: Xiaoyu Tan, Haoyu Wang, Xihe Qiu, Leijun Cheng, Yuan Cheng, Wei Chu, Yinghui Xu, Yuan Qi
Venue: KDD
Year: 2025

Conducting reasoning tasks with large language models (LLMs) on structured and redundant data poses significant challenges, primarily due to the complexity introduced by the structured markdown tokens and the presence of extraneous contextual information. These elements can overburden and disrupt the generation process of LLMs, complicating the extraction of relevant insights and the production of coherent outputs. To address this, we propose Struct-X, a novel framework that operates through five key phases: ''read-model-fill-reflect-reason'' efficiently enabling LLMs to utilize structured data. It begins by encoding structured data into a topological space using graph embeddings, followed by filling in missing entity information with knowledge retrieval modules, and filtering out irrelevant tokens via a self-supervised module. The final phase involves constructing a topological network with selected tokens to further reduce the total token length for more effective LLM inference. Additionally, Struct-X includes an Auxiliary Module trained to generate prompts, aiding LLMs in analyzing structured data. Extensive experiments on open-source benchmarks, including the knowledge graph question-answer task and the long document reading comprehension task, show that Struct-X notably improves LLM reasoning in complex structured input context. Finally, we deployed Struct-X in a real-world financial report analysis task, where it exhibits enhanced reasoning capabilities when applied to authentic scenario. The code has been undergoing open-source development to facilitate easy replication.
Spatially Compact Dense Block Mining in Spatial Tensors
Authors: Weike Tang, Dingming Wu, Tsz Nam Chan, Kezhong Lu
Venue: KDD
Year: 2025

Spatial tensors have been extensively used in a wide range of applications, including remote sensing, geospatial information systems, conservation planning, and urban planning. We study the problem of Spatially Compact Dense (SCD) block mining in a spatial tensor, which targets for discovering dense blocks that cover small spatial regions. However, most of existing dense block mining (DBM) algorithms cannot solve the SCD-block mining problem since they only focus on maximizing the density of candidate blocks, so that the discovered blocks are spatially loose, i.e., covering large spatial regions. Therefore, we first formulate the problem of mining top-k Spatially Compact Dense blocks (SCD-blocks) in spatial tensors, which ranks SCD-blocks based on a new scoring function that takes both the density value and the spatial coverage into account. Then, we adopt a filter-refinement framework that first generates candidate SCD-blocks with good scores in the filtering phase and then uses the traditional DBM algorithm to further maximize the density values of the candidates in the refinement phase. Due to the NP-hardness of the problem, we develop two types of solutions in the filtering phase, namely the top-down solution and the bottom-up solution, which can find good candidate SCD-blocks by approximately solving the new scoring function. The evaluations on four real datasets verify that compared with the dense blocks returned by existing DBM algorithms, the proposed solutions are able to find SCD-blocks with comparable density values and significantly smaller spatial coverage.
Generative Retrieval for Book Search
Authors: Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Shihao Liu, Shuaiqiang Wang, Dawei Yin, Xueqi Cheng
Venue: KDD
Year: 2025

In book search, relevant book information should be returned in response to a query. Books contain complex, multi-faceted information such as metadata, outlines, and main text, where the outline provides hierarchical information between chapters and sections. Generative retrieval (GR) is a new retrieval paradigm that consolidates corpus information into a single model to generate identifiers of documents that are relevant to a given query. How can GR be applied to book search? Directly applying GR to book search is a challenge due to the unique characteristics of book search: The model needs to retain the complex, multi-faceted information of the book, which increases the demand for labeled data. Splitting book information and treating it as a collection of separate segments for learning might result in a loss of hierarchical information. We propose an effective Generative retrieval framework for Book Search (GBS) that features two main components: data augmentation and outline-oriented book encoding. For data augmentation, GBS constructs multiple query-book pairs for training; it constructs multiple book identifiers based on the outline, various forms of book contents, and simulates real book retrieval scenarios with varied pseudo-queries. This includes coverage-promoting book identifier augmentation, allowing the model to learn to index effectively, and diversity-enhanced query augmentation, allowing the model to learn to retrieve effectively. Outline-oriented book encoding improves length extrapolation through bi-level positional encoding and retentive attention mechanisms to maintain context over long sequences. Experiments on a proprietary Baidu dataset demonstrate that GBS outperforms strong baselines, achieving a 9.8% improvement in terms of MRR@20, over the state-of-the-art RIPOR method. Experiments on public datasets confirm the robustness and generalizability of GBS, highlighting its potential to enhance book retrieval.
AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay
Authors: Ziyi Tang, Zechuan Chen, Jiarui Yang, Jiayao Mai, Yongsen Zheng, Keze Wang, Jinrui Chen, Liang Lin
Venue: KDD
Year: 2025

Alpha mining, a critical component in quantitative investment, focuses on discovering predictive signals for future asset returns in increasingly complex financial markets. However, the pervasive issue of alpha decay-where factors lose their predictive power over time-poses a significant challenge for alpha mining. Traditional methods such as genetic programming are prone to rapid alpha decay, primarily due to their susceptibility to overfitting. At the same time, approaches driven by Large Language Models (LLMs), despite their promise, often fail to impose regularization against factor homogenization-resulting in crowded signals and accelerated decay. To address this challenge, we propose AlphaAgent, an autonomous framework that effectively integrates LLM-driven agents with ad hoc regularization for mining decay-resistant alpha factors. AlphaAgent employs three key mechanisms: (i) originality enforcement through a similarity measure based on abstract syntax trees (ASTs) against existing alphas(ii) hypothesis-factor alignment via LLM-evaluated semantic consistency between market hypotheses and generated factors, and (iii) complexity control via AST-based structural constraints, preventing over-engineered constructions that are prone to overfitting. These mechanisms collectively guide the alpha generation process to balance originality, financial rationale, and adaptability to evolving market conditions, mitigating the risk of alpha decay. Extensive evaluations show that AlphaAgent outperforms traditional and LLM-based methods in mitigating alpha decay across bull and bear markets, consistently delivering significant alpha in Chinese CSI 500 and U.S. S&P 500 markets over the past four years. Notably, AlphaAgent showcases remarkable resistance to alpha decay, elevating the potential for yielding powerful factors.