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Received — 27 May 2026 ⏭

KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinyi Gao · Qiucheng Wu · Lu Ding · Q. Vera Liao · Kaizhi Qian · Ying Xu · Shiyu Chang · Yang Zhang
26 May 2026 at 12:00
arXiv:2605.23933v1 Announce Type: cross Abstract: Educational Question Generation (EQG) aims to synthesize customized exercise questions that enhance student learning. An effective EQG system should ideally personalize questions for each student by modeling the student's knowledge state and generating questions that provide the greatest learning benefit. However, few existing EQG approaches are able to achieve such fine-grained personalization. In this paper, we explore how EQG can benefit from knowledge tracing (KT), which models students' knowledge states based on historical performance and predicts future performance. We propose KT4EQG, a personalized EQG framework that generates effective questions for individual students under the guidance of a KT model. Specifically, KT4EQG seeks to maximize a student's potential improvement in overall knowledge mastery by leveraging the KT model to select the most suitable knowledge concept for the student to practice. An LLM-based question generator is then trained to produce a question faithfully grounded in the selected concept. Experimental results on XES3G5M and MOOCRadar show that KT4EQG consistently generates more effective questions than methods with limited or no personalization.

DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyi Wang · Tianyi Zeng · Zimo Zeng · Feiyang Zhang · Yujin Wang · Xiangyu Li · Yiming Xu · Sikai Chen · Junfeng Jiao · Christian Claudel · Xinbo Chen
26 May 2026 at 12:00
arXiv:2605.24860v1 Announce Type: cross Abstract: Advanced driver assistance systems (ADAS) play an important role in modern automotive intelligence, significantly enhancing vehicle safety and stability. The performance of ADAS critically relies on accurate and reliable vehicle state estimation, particularly from vehicle dynamic sensors. Among these signals, wheel load is a key variable for chassis control and safety-critical functions, yet it remains difficult to estimate robustly due to complex suspension geometry, nonlinear dynamics, and measurement noise. To address this issue, we propose DBPnet, a Bayesian physics-informed neural network (PINN) with a physics-aware embedding module inspired by damper characteristics. First, this paper presents a suspension linkage-level modeling (SLLM) approach that constructs a nonlinear instantaneous dynamic model by explicitly considering the complex geometric structure of the suspension. Building upon SLLM, Bayesian inference is integrated into the PINN to effectively cope with noise and uncertainty in the vehicle chassis system, thereby improving the model's robustness. Then, a physics-informed loss function is employed to ensure consistency with fundamental physical principles, while the damper characteristics-inspired embedding module extracts temporal variation features of input signals and incorporates them into each layer of the PINN, ensuring that physical observations guide the neural network without being constrained by fixed physical models. Extensive evaluations on high-fidelity simulations and real-world experiments demonstrate that our DBPnet consistently achieves lower RMSE and MaxError than baseline methods. These results highlight the potential of our DBPnet to advance wheel load estimation and contribute to the development of more reliable ADAS actuator functions.

Hide to Guide: Learning via Semantic Masking

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruitao Liu · Qinghao Hu · Alex Hu · Yecheng Wu · Shang Yang · Luke J. Huang · Zhuoyang Zhang · Han Cai · Song Han
26 May 2026 at 12:00
arXiv:2605.25198v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a powerful paradigm for improving language models on reasoning-intensive tasks, but its effectiveness is often limited by exploration. For example, models often fail on hard problems, leaving little useful reward signal. External expert traces offer a natural source of guidance, yet they may also expose reward-relevant content along the critical path to the verifier target, such as final answers, intermediate values, executable implementations, or answer-related entities. This content can create an unintended reward hacking channel, allowing the policy to obtain reward by copying the trace rather than learning the underlying reasoning or agentic behavior. Existing guided-RL methods reduce this risk by using partial trajectories, but they mainly control how much expert information is shown heuristically rather than which parts should be hidden. To this end, we propose Semantic Masked Expert Policy Optimization (SMEPO), a fine-grained semantic masking strategy for expert-guided RLVR. Instead of truncating traces coarsely or revealing them unchanged, SMEPO masks reward-relevant semantic spans along the critical path while preserving the expert's decomposition, plan, and procedural structure. This turns hard problems from reasoning from scratch into a fill-in-the-blank process: the policy can follow the expert's problem-solving route, but must still reconstruct the missing values, code, or entities by itself. SMEPO is simple to apply and requires no changes to the reward function or RL objective. Across diverse domains, including math, code, and agentic search, SMEPO improves accuracy by up to 3.2 points over GRPO and reduces training time by up to 4.2x. The code is available at https://github.com/mit-han-lab/SMEPO.

Benchmarking Pathology Foundation Models for Spatial Domain Understanding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bokai Zhao · Yiyang Zhang · Yuanchi Zhu · Hanqing Chao · Long Bai · Tai Ma · Minfeng Xu · Ming Song · Tianzi Jiang
26 May 2026 at 12:00
arXiv:2605.25764v1 Announce Type: cross Abstract: Pathology foundation models (PFMs) have emerged as a core approach for learning transferable representations from whole slide images (WSIs), and they are typically benchmarked through downstream clinical endpoints. While such task level evaluations are indispensable, they offer limited insight into what the representations themselves encode, particularly whether PFM embeddings can distinguish meaningful tissue regions and capture their spatial relationships. We present SpaPath-Bench, a representation level benchmark designed to diagnose spatial representation capability in PFMs. SpaPath-Bench formulates spatial domain identification (SDI) on paired whole slide image and spatial transcriptomics (ST) data as a diagnostic task. It curates 42 public paired WSI and ST slides, enables large scale evaluation across 19 encoders and seven SDI methods, and measures partition quality using three complementary criteria: unsupervised spatial coherence, transcriptomics referenced agreement, and expert referenced agreement. Across 83K runs, SpaPath-Bench reveals that different pretraining paradigms capture distinct aspects of tissue spatial architecture, and it provides practical guidance for building the next generation of spatially aware computational pathology models. Code and data pipelines are publicly available at https://bokai-zhao.github.io/SpaPath-benchboard/.

Topology-Driven Transferability Estimation of Medical Foundation Models for Segmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqi Tang · Shaoyang Zhang · Xiaoqi Wang · Jiaying Zhou · Yang Liu · Qingchao Chen
26 May 2026 at 12:00
arXiv:2602.23916v2 Announce Type: replace-cross Abstract: The advent of large-scale self-supervised learning (SSL) has produced a vast zoo of medical foundation models. However, selecting optimal medical foundation models for specific segmentation tasks remains a computational bottleneck. Existing Transferability Estimation (TE) metrics, primarily designed for classification, rely on global statistical assumptions and fail to capture the topological complexity essential for dense prediction. We propose a novel Topology-Driven Transferability Estimation framework that evaluates manifold tractability rather than statistical overlap. Our approach introduces three components: (1) Global Representation Topology Divergence (GRTD), utilizing Minimum Spanning Trees to quantify feature-label structural isomorphism; (2) Local Boundary-Aware Topological Consistency (LBTC), which assesses manifold separability specifically at critical anatomical boundaries; and (3) Task-Adaptive Fusion, which dynamically integrates global and local metrics based on the semantic cardinality of the target task. Validated on the large-scale OpenMind benchmark across diverse anatomical targets and SSL foundation models, our approach significantly outperforms state-of-the-art baselines by around 31% relative improvement in the weighted Kendall metric, providing a robust, training-free proxy for efficient model selection without the cost of fine-tuning. The code will be made publicly available upon acceptance.

Integrative multi-omics and experimental validation reveal UBE2C as a central hub gene and prognostic biomarker in hepatocellular carcinoma

✇Omics in Hepatocellular
By: Lihuang Su · Ouyang Zhang · Yujie Wang · Jiaojian Lv · Qingxiu Liu · Mengchen Ma · Liang Xu · Tongtong Pan
19 May 2026 at 18:00

Int Immunopharmacol. 2026 May 19;183:116866. doi: 10.1016/j.intimp.2026.116866. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) is a lethal malignancy with a high recurrence rate and limited treatment options. Ubiquitin-conjugating enzyme E2 C (UBE2C) is implicated in various cancers, yet its impact on the HCC immune landscape remains incompletely understood. Herein, hub genes in HCC were identified, by integrating co-expression networks and protein-protein interaction analyses, from the TCGA, GEO, and CPTAC databases. Their expression was analysed using a single-cell transcriptomic database and verified in HCC tissues and cell lines via quantitative reverse transcription-PCR and immunoblotting. Functional roles of UBE2C were assessed using in vitro knockdown experiments and an in vivo subcutaneous tumour model. The tumour immune microenvironment was profiled using spatial transcriptomics, RNA-seq data, and ssGSEA. A prognostic nomogram was constructed based on multivariate Cox regression. UBE2C was identified as a significantly upregulated hub gene in HCC. Single-cell RNA-seq revealed predominant expression of UBE2C in hepatocytes, with dynamic upregulation along differentiation trajectories. UBE2C knockdown suppressed proliferation, induced apoptosis, and inhibited tumour growth. Spatial transcriptomics highlighted UBE2C-high regions within proliferative niches exhibiting immunosuppressive traits-including TGFB1 enrichment, impaired CXCL9-CXCR3 signalling, and exclusion of cytotoxic T cells-which were reduced in immunotherapy responders. UBE2C expression correlated with immune checkpoint genes and specific immune cell subsets. A UBE2C-based nomogram integrating T stage and tumour stage robustly predicted patient survival, and miR-300 and miR-381-3p were identified as potential upstream regulators. These findings establish UBE2C as a key driver of HCC progression and a biomarker for prognosis and immunotherapy stratification.

PMID:42155390 | DOI:10.1016/j.intimp.2026.116866

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