Visual query localization (VQL) is pivotal for constructing episodic memory from egocentric videos. However, current methods often rely on computationally intensive attention mechanisms and rigid one-shot regression, which inherently struggle to model uncertainty and exhibit limited adaptability to degraded query appearances. This contrasts sharply with the human brain’s selective encoding and iterative hypothesis verification processes for episodic memory. Inspired by the human brain’s ability to selectively filter irrelevant information and reconstruct vague memory fragments through generative inference, we propose BiOVQL, a brain-inspired one-stage VQL framework. First, inspired by the hippocampus’s selective retention mechanism, we propose the Hippocampus-like Query-guided Spatio-Temporal Compression (HQSTC) module. By leveraging a selective state space model, visual queries are treated as neuromodulators, dynamically gating the video stream to maintain a compact latent state. This guides the model to consistently focus on query-relevant visual cues, enabling query-conditioned feature compression and efficient spatio-temporal memory encoding. Second, inspired by the prefrontal cortex’s re-localization mechanisms, we propose the Prefrontal-like Generative Refinement Localization (PGRL) module. We leverage a diffusion model to reconstruct the localization process as iterative denoising from noise to certainty, which aligns well with the human visual system’s coarse-to-fine perceptual reasoning. This enhances the model’s robustness in handling spatial ambiguities and achieving precise spatio-temporal retrieval. We conducted extensive experiments on the Ego4D-VQ benchmark, demonstrating that BiOVQL achieves state-of-the-art performance with comparable computational efficiency, thus offering an efficient and brain-inspired paradigm for VQL.

Cognitive diagnosis aims to infer students' mastery levels over knowledge components from their learning interactions, supporting personalized education applications. However, existing models encode responses as binary correctness labels, discarding information about which specific option a student selected. This input-level information loss limits their ability to distinguish qualitatively different error types. As a result, providing interpretable diagnostic outputs becomes challenging. To address these limitations, we propose SACD, a semantic-augmented cognitive diagnosis framework that integrates LLM-based semantic analysis with student behavioral modeling. SACD comprises the following key components. First, an LLM-based exercise diagnostic generator analyzes exercise content and produces structured semantic annotations for each answer choice, capturing the specific misconceptions each option represents. Second, a kernel-based alignment mechanism projects semantic embeddings and behavioral representations into a unified kernel space, enabling effective fusion of heterogeneous information. Third, an interpretable diagnosis layer predicts student performance and generates fine-grained mastery estimates, which LLMs further process to produce actionable learning plans. Extensive experiments on three real-world datasets demonstrate that SACD achieves superior prediction accuracy while enabling interpretable, actionable diagnostics.

Knowledge tracing (KT) aims to personalize online education on large-scale web-based platforms by modeling students' evolving knowledge states from their interaction sequences. However, most KT models rely on a single encoder architecture (e.g., self-attention or RNN), with fixed inductive biases that fails to capture the diversity of learning behaviors. Specifically, student learning unfolds across multiple timescales, and interaction sequences contain diverse frequency components ranging from short-term variations to long-term trends. Our data-driven analysis reveals that existing encoders exhibit characteristic frequency biases (e.g., self-attention tends to emphasize low-frequency patterns), highlighting the limitations of any single architecture. To address this problem, we propose FA-KT, a frequency-aware mixture of heterogeneous experts framework. FA-KT combines self-attention, Mamba, CNN, and LSTM experts, each with complementary frequency biases. A frequency-aware router analyzes each sequence's frequency characteristics and adaptively combines experts to create dynamic, personalized encoders for individual students. Across five benchmark datasets, FA-KT consistently outperforms 20 strong KT baselines in predicting future performance. Code is available at https://pykt.org/.

Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in incomplete social networks, where sensitive attributes are frequently missing due to privacy and ethical restrictions. Existing solutions commonly generate these incomplete attributes, which may introduce additional biases and further compromise user privacy. To address this challenge, FairGE (Fair Graph Encoding) is introduced as a fairness-aware framework for GTs in incomplete social networks. Instead of generating sensitive attributes, FairGE encodes fairness directly through spectral graph theory. By leveraging the principal eigenvector to represent structural information and padding incomplete sensitive attributes with zeros to maintain independence, FairGE ensures fairness without data reconstruction. Theoretical analysis demonstrates that the method suppresses the influence of non-principal spectral components, thereby enhancing fairness. Extensive experiments on seven real-world social network datasets confirm that FairGE achieves at least a 16% improvement in both statistical parity and equality of opportunity compared with state-of-the-art baselines.

Semantic understanding of popularity bias is a crucial yet underexplored challenge in recommender systems, where popular items are often favored at the expense of niche content. Most existing debiasing methods treat the semantic understanding of popularity bias as a matter of diversity enhancement or long-tail coverage, neglecting the deeper semantic layer that embodies the causal origins of the bias itself. Consequently, such shallow interpretations limit both their debiasing effectiveness and recommendation accuracy. In this paper, we propose FairLRM, a novel framework that bridges the gap in the semantic understanding of popularity bias with Recommendation via Large Language Model (RecLLM). FairLRM decomposes popularity bias into item-side and user-side components, using structured instruction-based prompts to enhance the model's comprehension of both global item distributions and individual user preferences. Unlike traditional methods that rely on surface-level features such as ''diversity'' or ''debiasing'', FairLRM improves the model's ability to semantically interpret and address the underlying bias. Through empirical evaluation, we demonstrate that FairLRM enhances fairness and recommendation accuracy through a trustworthy, semantically grounded treatment of popularity bias. The source code is shown in https://github.com/LuoRenqiang/FairLRM.

Knowledge tracing (KT) is a problem of modeling students’ knowledge states to predict their future performance by observing their historical learning interactions. The collection of educational data presents significant challenges, as students’ limited learning engagement restricts the generation of large-scale interaction data, while stringent privacy regulations further limit the availability of student learning sequences from online platforms. Hence, it is crucial to enhance the capabilities of deep learning-based KT (DLKT) models by constructing large-scale datasets through the integration of student interaction data across multiple subjects and sources. The success of ChatGPT demonstrates that the decoder-only Transformer architecture is highly effective in capturing complex information from large-scale sequential data. Against this background, we propose a novel decoder-only Transformer architecture-based model, named Unified DLKT ( UniKT ), to learn coherent and unified representations across a wide range of data sources. Specifically, we combine student learning sequences from six educational scenarios and utilize a multi-source encoding to learn unified representations of interactions from mixed data. UniKT is a stack of Transformer decoder layers for handling long-term dependencies among students’ historical interactions and future performance. We evaluate UniKT on six publicly available real-world educational datasets, and experimental results demonstrate that our method outperforms the majority of existing DLKT models in terms of AUC and accuracy. Furthermore, the empirical analysis shows the strong transferability and adaptability of UniKT in learning from multiple sources. To encourage reproducible research, we make our data and code publicly available at https://pykt.org/ .

Knowledge tracing (KT) aims to predict students' future performance based on their past learning activities. However, no one is perfect. Factors such as carelessness, fatigue, and stress often cause students to make mistakes on problems they have already mastered, leading to anomalies in their historical learning data. These anomalies disrupt inherent patterns in the data, misleading the KT model. Extracting cognitive patterns that accurately reflect students' knowledge mastery from such error-prone data remains a significant challenge. Against this background, this paper proposes a novel KT method named RoubstKT, inspired by educational measurement theory and frequency-based decomposition. A cognitive decoupling analyzer is proposed to decouple the student's cognitive pattern and random factors from the data through smoothing and subtraction operations, then recombine them using a gating mechanism or adaptive parameter fusion strategy. To more effectively diagnose students' knowledge mastery, we employ a decay-based attention mechanism that focuses on random behaviors at adjacent time steps. We conducted comprehensive experiments based on real-world datasets and targeted datasets with added random noise. The experimental results demonstrated the effectiveness of the proposed method.

Knowledge tracing (KT) involves utilizing historical data from students’ learning interactions to model their mastery of knowledge over time, with the aim of predicting their future performance in interactions. Recently, significant advancements have been achieved through the application of various deep learning methodologies to address the KT challenge. However, a considerable proportion of deep learning-based knowledge tracing (DLKT) approaches exhibit striking similarities in their methodologies, and model designs, and even the outcomes demonstrate minimal divergence. In addition, the evaluation procedures employed in current DLKT studies are not standardized, resulting in substantial inconsistencies in the reported area under the curve (AUC) outcomes, despite analyzing the same model on identical datasets. To address the two aforementioned problems, this paper proposes a generalized DLKT framework and represents the existing DLKT models with five components, i.e., multimodal data encoder, student knowledge memory, auxiliary knowledge base, learning outcome objective, and computational efficiency and scalability. Furthermore, we develop and open source a standardized DLKT benchmark platform namedpyKT,1that consists of a standardized set of integrated data preprocessing procedures on 9 popular datasets across different domains, and 21 frequently compared DLKT model implementations. WithpyKT, we conduct empirical and reproducible research to assess the performance of prevalent DLKT algorithms in an unbiased and clear setting over multiple data sources. Finally, we discuss the applications of KT techniques in the educational sector and their future development directions.

Accurately judging student on-going performance is crucial for adaptive teaching. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in online one-on-one classes. As a step toward this direction, we introduce the Multi-turn Classroom Dialogue (MCD) dataset as a benchmark testing the capabilities of machine learning models in classroom conversation understanding of student performance judgment. Our dataset contains aligned multi-turn spoken language of 5000+ unique samples of solving grade-8 math questions collected from 500+ hours' worth of online one-on-one tutoring classes. In our experiments, we assess various state-of-the-art models on the MCD dataset, highlighting the importance of understanding multi-turn dialogues and handling noisy ASR transcriptions. Our findings demonstrate the dataset's utility in advancing research on automated student performance assessment. To encourage reproducible research, we make our data publicly available at https://github.com/ai4ed/MCD.

The COVID-19 pandemic has posed great challenges to public health services, government agencies, and policymakers, raising huge social conflicts between public health and economic resilience. Policies such as reopening or closure of business activities are formulated based on scientific projections of infection risks obtained from infection dynamics models. Though most parameters in epidemic prediction service models can be set with domain knowledge of COVID-19, a key parameter, namely, human mobility, is often challenging to estimate due to complex spatio-temporal correlations and social contexts under escalating COVID-19 facilities. Moreover, how to integrate the various implicit features to accurately predict infectious cases is still an open issue. To address this challenge, we formulate the problem as a spatio-temporal network representation problem and propose STEP, a Spatio-Temporal Epidemic Prediction framework, to estimate pandemic infection risk of a city by integrating various real-world conditions (e.g., City Risk Index, climate, and medical conditions) into graph-structured data. We also employ a multi-head attention mechanism in representation learning to extract implicit features for a given city. Extensive experiments have been conducted upon the real-world dataset for 51 states (50 states and Washington, D.C.) of the USA. Experimental results show that STEP can yield more accurate pandemic infection risk estimation than baseline methods. Moreover, STEP outperforms other methods in both short-term and long-term prediction.