OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing Programming Pair Workshops: The Case of Teacher Pre-Prompting
Venue: arXiv cs.CY 2025
Year: 2025

This paper explores the pedagogical potential of "teacher pre-prompting" as a means of guiding student collaboration in programming education. In particular, we investigate how brief teacher-initiated questions posed before students engage in pair programming workshops can help shape problem interpretation and division of labor. Based on qualitative analysis of video data from a university course in systems development, we identify five distinct pre-prompting patterns. Our findings suggest that such prompts can foster structured discussions, clarify task requirements, and create opportunities for shared learning experiences.
Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding
Venue: arXiv cs.LG 2025
Year: 2025

The hardness of learning a function that attains a target task relates to its input-sensitivity. For example, image classification tasks are input-insensitive as minor corruptions should not affect the classification results, whereas arithmetic and symbolic computation, which have been recently attracting interest, are highly input-sensitive as each input variable connects to the computation results. This study presents the first learning-based Quick Response (QR) code decoding and investigates learning functions of medium sensitivity. Our experiments reveal that Transformers can successfully decode QR codes, even beyond the theoretical error-correction limit, by learning the structure of embedded texts. They generalize from English-rich training data to other languages and even random strings. Moreover, we observe that the Transformer-based QR decoder focuses on data bits while ignoring error-correction bits, suggesting a decoding mechanism distinct from standard QR code readers.
Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead
Venue: arXiv cs.RO 2025
Year: 2025

Legged robots are increasingly being adopted in industries such as oil, gas, mining, nuclear, and agriculture. However, new challenges exist when moving into natural, less-structured environments, such as forestry applications. This paper presents a prototype system for autonomous, under-canopy forest inventory with legged platforms. Motivated by the robustness and mobility of modern legged robots, we introduce a system architecture which enabled a quadruped platform to autonomously navigate and map forest plots. Our solution involves a complete navigation stack for state estimation, mission planning, and tree detection and trait estimation. We report the performance of the system from trials executed over one and a half years in forests in three European countries. Our results with the ANYmal robot demonstrate that we can survey plots up to 1 ha plot under 30 min, while also identifying trees with typical DBH accuracy of 2cm. The findings of this project are presented as five lessons and challenges. Particularly, we discuss the maturity of hardware development, state estimation limitations, open problems in forest navigation, future avenues for robotic forest inventory, and more general challenges to assess autonomous systems. By sharing these lessons and challenges, we offer insight and new directions for future research on legged robots, navigation systems, and applications in natural environments. Additional videos can be found in https://dynamic.robots.ox.ac.uk/projects/legged-robots
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
Venue: arXiv cs.CL 2025
Year: 2025

We introduce Biomed-Enriched, a biomedical text dataset constructed from PubMed via a two-stage annotation process. In the first stage, a large language model annotates 400K paragraphs from PubMed scientific articles, assigning scores for their type (review, study, clinical case, other), domain (clinical, biomedical, other), and educational quality. The educational quality score (rated 1 to 5) estimates how useful a paragraph is for college-level learning. These annotations are then used to fine-tune a small language model, which propagates the labels across the full PMC-OA corpus. The resulting metadata allows us to extract refined subsets, including 2M clinical case paragraphs with over 450K high-quality ones from articles with commercial-use licenses, and to construct several variants via quality filtering and domain upsampling. Clinical text is typically difficult to access due to privacy constraints, as hospital records cannot be publicly shared. Hence, our dataset provides an alternative large-scale, openly available collection of clinical cases from PubMed, making it a valuable resource for biomedical and clinical NLP. Preliminary continual-pretraining experiments with OLMo2 suggest these curated subsets enable targeted improvements, with clinical upsampling boosting performance by ~5% on MMLU ProfMed and educational quality filtering improving MedQA and MedMCQA by ~1%. Combinations of these techniques led to faster convergence, reaching same performance with a third of training tokens, indicating potential for more efficient and effective biomedical pretraining strategies.
Emergent Granger Causality in Neural Networks: Can Prediction Alone Reveal Structure?
Venue: arXiv cs.LG 2025
Year: 2025

Granger Causality (GC) offers an elegant statistical framework to study the association between multivariate time series data. Vector autoregressive models (VAR) are simple and easy to fit, but have limited application because of their inherent inability to capture more complex (e.g., non-linear) associations. Numerous attempts have already been made in the literature that exploit the functional approximation power of deep neural networks (DNNs) for GC. However, these methods treat GC as a variable selection problem. We present a novel paradigm for investigating the learned GC from a single neural network used for joint modeling of all components of multivariate time series data, which is essentially linked with prediction and assessing the distribution shift in residuals. A deep learning model, with proper regularization, may learn the true GC structure when jointly used for all components of the time series when there is sufficient training data. We propose to uncover the learned GC structure by comparing the model uncertainty or distribution of the residuals when the past of everything is used as compared to the one where a specific time series component is dropped from the model. We also compare the effect of input layer dropout on the ability of a neural network to learn GC. We show that a well-regularized model can learn the true GC structure from the data without explicitly adding terms in the loss function that guide the model to select variables or perform sparse regression under specific settings. We also provide a comparison of deep learning architectures such as CNN, LSTM and transformer models on their ability to discover Granger Causality. The numerical experiments demonstrate that, compared to sparse regression models, a simple joint model is a strong baseline for learning the true GC which has the advantage that it does not require tuning of many extra hyper-parameters.
Tabular Feature Discovery With Reasoning Type Exploration
Venue: arXiv cs.AI 2025
Year: 2025

Feature engineering for tabular data remains a critical yet challenging step in machine learning. Recently, large language models (LLMs) have been used to automatically generate new features by leveraging their vast knowledge. However, existing LLM-based approaches often produce overly simple or repetitive features, partly due to inherent biases in the transformations the LLM chooses and the lack of structured reasoning guidance during generation. In this paper, we propose a novel method REFeat, which guides an LLM to discover diverse and informative features by leveraging multiple types of reasoning to steer the feature generation process. Experiments on 59 benchmark datasets demonstrate that our approach not only achieves higher predictive accuracy on average, but also discovers more diverse and meaningful features. These results highlight the promise of incorporating rich reasoning paradigms and adaptive strategy selection into LLM-driven feature discovery for tabular data.
Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
Venue: arXiv cs.LG 2025
Year: 2025

We present LaplaceGNN, a novel self-supervised graph learning framework that bypasses the need for negative sampling by leveraging spectral bootstrapping techniques. Our method integrates Laplacian-based signals into the learning process, allowing the model to effectively capture rich structural representations without relying on contrastive objectives or handcrafted augmentations. By focusing on positive alignment, LaplaceGNN achieves linear scaling while offering a simpler, more efficient, self-supervised alternative for graph neural networks, applicable across diverse domains. Our contributions are twofold: we precompute spectral augmentations through max-min centrality-guided optimization, enabling rich structural supervision without relying on handcrafted augmentations, then we integrate an adversarial bootstrapped training scheme that further strengthens feature learning and robustness. Our extensive experiments on different benchmark datasets show that LaplaceGNN achieves superior performance compared to state-of-the-art self-supervised graph methods, offering a promising direction for efficiently learning expressive graph representations.
CARMA: Context-Aware Situational Grounding of Human-Robot Group Interactions by Combining Vision-Language Models with Object and Action Recognition
Venue: arXiv cs.RO 2025
Year: 2025

We introduce CARMA, a system for situational grounding in human-robot group interactions. Effective collaboration in such group settings requires situational awareness based on a consistent representation of present persons and objects coupled with an episodic abstraction of events regarding actors and manipulated objects. This calls for a clear and consistent assignment of instances, ensuring that robots correctly recognize and track actors, objects, and their interactions over time. To achieve this, CARMA uniquely identifies physical instances of such entities in the real world and organizes them into grounded triplets of actors, objects, and actions. To validate our approach, we conducted three experiments, where multiple humans and a robot interact: collaborative pouring, handovers, and sorting. These scenarios allow the assessment of the system's capabilities as to role distinction, multi-actor awareness, and consistent instance identification. Our experiments demonstrate that the system can reliably generate accurate actor-action-object triplets, providing a structured and robust foundation for applications requiring spatiotemporal reasoning and situated decision-making in collaborative settings.
TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Satellite Earth-observation (EO) time series in the optical and microwave ranges of the electromagnetic spectrum are often irregular due to orbital patterns and cloud obstruction. Compositing addresses these issues but loses information with respect to vegetation phenology, which is critical for many downstream tasks. Instead, we present TESSERA, a pixel-wise foundation model for multi-modal (Sentinel-1/2) EO time series that learns robust, label-efficient embeddings. During model training, TESSERA uses Barlow Twins and sparse random temporal sampling to enforce invariance to the selection of valid observations. We employ two key regularizers: global shuffling to decorrelate spatial neighborhoods and mix-based regulation to improve invariance under extreme sparsity. We find that for diverse classification, segmentation, and regression tasks, TESSERA embeddings deliver state-of-the-art accuracy with high label efficiency, often requiring only a small task head and minimal computation. To democratize access, adhere to FAIR principles, and simplify use, we release global, annual, 10m, pixel-wise int8 embeddings together with open weights/code and lightweight adaptation heads, thus providing practical tooling for large-scale retrieval and inference at planetary scale. The model training/inference code, downstream task code, and pre-generated embeddings can be accessed at https://github.com/ucam-eo
Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking
Venue: arXiv cs.CV 2025
Year: 2025

Transformer-based visual trackers have demonstrated significant advancements due to their powerful modeling capabilities. However, their practicality is limited on resource-constrained devices because of their slow processing speeds. To address this challenge, we present HiT, a novel family of efficient tracking models that achieve high performance while maintaining fast operation across various devices. The core innovation of HiT lies in its Bridge Module, which connects lightweight transformers to the tracking framework, enhancing feature representation quality. Additionally, we introduce a dual-image position encoding approach to effectively encode spatial information. HiT achieves an impressive speed of 61 frames per second (fps) on the NVIDIA Jetson AGX platform, alongside a competitive AUC of 64.6% on the LaSOT benchmark, outperforming all previous efficient trackers.Building on HiT, we propose DyHiT, an efficient dynamic tracker that flexibly adapts to scene complexity by selecting routes with varying computational requirements. DyHiT uses search area features extracted by the backbone network and inputs them into an efficient dynamic router to classify tracking scenarios. Based on the classification, DyHiT applies a divide-and-conquer strategy, selecting appropriate routes to achieve a superior trade-off between accuracy and speed. The fastest version of DyHiT achieves 111 fps on NVIDIA Jetson AGX while maintaining an AUC of 62.4% on LaSOT.Furthermore, we introduce a training-free acceleration method based on the dynamic routing architecture of DyHiT. This method significantly improves the execution speed of various high-performance trackers without sacrificing accuracy. For instance, our acceleration method enables the state-of-the-art tracker SeqTrack-B256 to achieve a 2.68 times speedup on an NVIDIA GeForce RTX 2080 Ti GPU while maintaining the same AUC of 69.9% on the LaSOT.
On plane cycles in geometric multipartite graphs
Venue: arXiv cs.CG 2025
Year: 2025

A geometric graph is a drawing of a graph in the plane where the vertices are drawn as points in general position and the edges as straight-line segments connecting their endpoints. It is plane if it contains no crossing edges. We study plane cycles in geometric complete multipartite graphs. We prove that if a geometric complete multipartite graph contains a plane cycle of length $t$, with $t \geq 6$, it also contains a smaller plane cycle of length at least $\lfloor t/2\rfloor + 1$. We further give a characterization of geometric complete multipartite graphs that contain plane cycles with a color class appearing at least twice. For geometric drawings of $K_{n,n}$, we give a sufficient condition under which they have, for each $s \leq n$, a plane cycle of length 2s. We also provide an algorithm to decide whether a given geometric drawing of $K_{n,n}$ contains a plane Hamiltonian cycle in time $O(n \log n + nk^2) + O(k^{5k})$, where k is the number of vertices inside the convex hull of all vertices. Finally, we prove that it is NP-complete to decide if a subset of edges of a geometric complete bipartite graph H is contained in a plane Hamiltonian cycle in H.
That's Not the Feedback I Need! -- Student Engagement with GenAI Feedback in the Tutor Kai
Venue: arXiv cs.CY 2025
Year: 2025

The potential of Generative AI (GenAI) for generating feedback in computing education has been the subject of numerous studies. However, there is still limited research on how computing students engage with this feedback and to what extent it supports their problem-solving. For this reason, we built a custom web application providing students with Python programming tasks, a code editor, GenAI feedback, and compiler feedback. Via a think-aloud protocol including eye-tracking and a post-interview with 11 undergraduate students, we investigate (1) how much attention the generated feedback received from learners and (2) to what extent the generated feedback is helpful (or not). In addition, students' attention to GenAI feedback is compared with that towards the compiler feedback. We further investigate differences between students with and without prior programming experience. The findings indicate that GenAI feedback generally receives a lot of visual attention, with inexperienced students spending twice as much fixation time. More experienced students requested GenAI less frequently, and could utilize it better to solve the given problem. It was more challenging for inexperienced students to do so, as they could not always comprehend the GenAI feedback. They often relied solely on the GenAI feedback, while compiler feedback was not read. Understanding students' attention and perception toward GenAI feedback is crucial for developing educational tools that support student learning.
The Composition of Digital Twins for Systems-of-Systems: a Systematic Literature Review
Venue: arXiv cs.SE 2025
Year: 2025

Digital Twins (DTs) are increasingly used to model complex systems, especially in Cyber-Physical Systems (CPS) and System-of-Systems (SoS), where effective integration is key. This systematic literature review investigates DT composition and verification and validation (V&V) methodologies. Analyzing 21 studies from 2022-2024, we examined composition mechanisms, SoS characteristics, and V&V formality, scope, and challenges. While composition is discussed, formalization is limited. V&V approaches vary, with semi-formal methods and simulations dominating; formal verification is underutilized. Key technical challenges include model uncertainty and integration complexity. Methodological challenges highlight the lack of standardized DT-specific V&V frameworks. There is a need to move beyond model validation to address integration and cyber-physical consistency. This review contributes a structured classification of V&V approaches and emphasizes the need for standardized, scalable V&V and rigorous composition methodologies for complex DT implementations.
Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Hard-to-Learn Data
Venue: arXiv cs.CR 2025
Year: 2025

Vulnerability detection is crucial for identifying security weaknesses in software systems. However, training effective machine learning models for this task is often constrained by the high cost and expertise required for data annotation. Active learning is a promising approach to mitigate this challenge by intelligently selecting the most informative data points for labeling. This paper proposes a novel method to significantly enhance the active learning process by using dataset maps. Our approach systematically identifies samples that are hard-to-learn for a model and integrates this information to create a more sophisticated sample selection strategy. Unlike traditional active learning methods that focus primarily on model uncertainty, our strategy enriches the selection process by considering learning difficulty, allowing the active learner to more effectively pinpoint truly informative examples. The experimental results show that our approach can improve F1 score over random selection by 61.54% (DeepGini) and 45.91% (K-Means) and outperforms standard active learning by 8.23% (DeepGini) and 32.65% (K-Means) for CodeBERT on the Big-Vul dataset, demonstrating the effectiveness of integrating dataset maps for optimizing sample selection in vulnerability detection. Furthermore, our approach also enhances model robustness, improves sample selection by filtering hard-to-learn data, and stabilizes active learning performance across iterations. By analyzing the characteristics of these outliers, we provide insights for future improvements in dataset construction, making vulnerability detection more reliable and cost-effective.
Analyzing Security and Privacy Challenges in Generative AI Usage Guidelines for Higher Education
Venue: arXiv cs.HC 2025
Year: 2025

Educators and learners worldwide are embracing the rise of Generative Artificial Intelligence (GenAI) as it reshapes higher education. However, GenAI also raises significant privacy and security concerns, as models and privacy-sensitive user data, such as student records, may be misused by service providers. Unfortunately, end-users often have little awareness of or control over how these models operate. To address these concerns, universities are developing institutional policies to guide GenAI use while safeguarding security and privacy. This work examines these emerging policies and guidelines, with a particular focus on the often-overlooked privacy and security dimensions of GenAI integration in higher education, alongside other academic values. Through a qualitative analysis of GenAI usage guidelines from universities across 12 countries, we identify key challenges and opportunities institutions face in providing effective privacy and security protections, including the need for GenAI safeguards tailored specifically to the academic context.
Counterfactual Influence as a Distributional Quantity
Venue: arXiv cs.LG 2025
Year: 2025

Machine learning models are known to memorize samples from their training data, raising concerns around privacy and generalization. Counterfactual self-influence is a popular metric to study memorization, quantifying how the model's prediction for a sample changes depending on the sample's inclusion in the training dataset. However, recent work has shown memorization to be affected by factors beyond self-influence, with other training samples, in particular (near-)duplicates, having a large impact. We here study memorization treating counterfactual influence as a distributional quantity, taking into account how all training samples influence how a sample is memorized. For a small language model, we compute the full influence distribution of training samples on each other and analyze its properties. We find that solely looking at self-influence can severely underestimate tangible risks associated with memorization: the presence of (near-)duplicates seriously reduces self-influence, while we find these samples to be (near-)extractable. We observe similar patterns for image classification, where simply looking at the influence distributions reveals the presence of near-duplicates in CIFAR-10. Our findings highlight that memorization stems from complex interactions across training data and is better captured by the full influence distribution than by self-influence alone.