OpenRaiser/Intern-Atlas huggingface dataset:
Matrix Pencil-Based DoA Estimation for Hybrid Receivers in Snapshot-Limited Scenarios
Venue: arXiv cs.IT 2025
Year: 2025

The goal of this paper is to estimate the directions of arrival (DoAs) for hybrid analog/digital (HAD) receivers when the number of snapshots is too small for statistical averaging to be reliable. This goal is achieved in fully-digital receivers by employing the matrix pencil method (MPM). Unfortunately, the MPM cannot be directly applied in HAD receivers because of the entanglement induced by the underlying analog combiners on the output signals. Furthermore, these analog combiners project the received signal onto a low-dimensional space, jeopardizing the reception of signals arriving from particular DoA ranges. To circumvent these difficulties, we propose two approaches to enable the MPM to extract the DoAs in HAD receivers. The two approaches avoid severe attenuation induced by low-dimensional projection by cycling over an exhaustive set of analog combiners, collectively spanning the entire space. The first approach can be applied to both fully-connected (FC) and partially-connected (PC) HADs and relies on the availability of periodic, potentially unknown, signals to disentangle the output of the HAD receiver. The second approach applies to PC-HADs only, and eliminates contingency on periodic signals by exploiting the underlying block diagonal structure. The superiority of the proposed approaches is demonstrated via numerical simulations and comparisons with the Cramér-Rao lower bound.
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models
Venue: arXiv cs.CV 2025
Year: 2025

Monocular depth estimation has recently progressed beyond ordinal depth to provide metric depth predictions. However, its reliability in underwater environments remains limited due to light attenuation and scattering, color distortion, turbidity, and the lack of high-quality metric ground truth data. In this paper, we present a comprehensive benchmark of zero-shot and fine-tuned monocular metric depth estimation models on real-world underwater datasets with metric depth annotations, including FLSea and SQUID. We evaluated a diverse set of state-of-the-art Vision Foundation Models across a range of underwater conditions and depth ranges. Our results show that large-scale models trained on terrestrial data (real or synthetic) are effective in in-air settings, but perform poorly underwater due to significant domain shifts. To address this, we fine-tune Depth Anything V2 with a ViT-S backbone encoder on a synthetic underwater variant of the Hypersim dataset, which we simulated using a physically based underwater image formation model. Our fine-tuned model consistently improves performance across all benchmarks and outperforms baselines trained only on the clean in-air Hypersim dataset. This study presents a detailed evaluation and visualization of monocular metric depth estimation in underwater scenes, emphasizing the importance of domain adaptation and scale-aware supervision for achieving robust and generalizable metric depth predictions using foundation models in challenging environments.
The Revolution Has Arrived: What the Current State of Large Language Models in Education Implies for the Future
Venue: arXiv cs.HC 2025
Year: 2025

Large language Models have only been widely available since 2022 and yet in less than three years have had a significant impact on approaches to education and educational technology. Here we review the domains in which they have been used, and discuss a variety of use cases, their successes and failures. We then progress to discussing how this is changing the dynamic for learners and educators, consider the main design challenges facing LLMs if they are to become truly helpful and effective as educational systems, and reflect on the learning paradigms they support. We make clear that the new interaction paradigms they bring are significant and argue that this approach will become so ubiquitous it will become the default way in which we interact with technologies, and revolutionise what people expect from computer systems in general. This leads us to present some specific and significant considerations for the design of educational technology in the future that are likely to be needed to ensure acceptance by the changing expectations of learners and users.
Computer Science Education in the Age of Generative AI
Venue: arXiv cs.CY 2025
Year: 2025

Generative AI tools - most notably large language models (LLMs) like ChatGPT and Codex - are rapidly revolutionizing computer science education. These tools can generate, debug, and explain code, thereby transforming the landscape of programming instruction. This paper examines the profound opportunities that AI offers for enhancing computer science education in general, from coding assistance to fostering innovative pedagogical practices and streamlining assessments. At the same time, it highlights challenges including academic integrity concerns, the risk of over-reliance on AI, and difficulties in verifying originality. We discuss what computer science educators should teach in the AI era, how to best integrate these technologies into curricula, and the best practices for assessing student learning in an environment where AI can generate code, prototypes and user feedback. Finally, we propose a set of policy recommendations designed to harness the potential of generative AI while preserving the integrity and rigour of computer science education. Empirical data and emerging studies are used throughout to support our arguments.
Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer
Venue: arXiv cs.CL 2025
Year: 2025

Chain-of-thought (CoT) reasoning has enabled transformer-based language models to excel at complex mathematics and multi-step planning. However, in standard decoder-only architectures, these reasoning steps are externalized in natural language, improving interpretability at the cost of efficiency. To capture reasoning that is not easily represented in words, many works have explored recurrent architectures that aim to internalize reasoning in latent space, potentially supporting latent CoT. In this paper, we investigate whether such reasoning structures emerge in Huginn-3.5B, a depth-recurrent Transformer that reuses layers at inference time without increasing parameter count. We examine the model's internal behavior on arithmetic tasks using a suite of probing techniques including the Logit Lens and Coda Lens. Our findings reveal limited evidence of interpretable latent CoT by tracking rank trajectories of final and intermediate result tokens. Furthermore, we uncover significant probing inconsistencies across recurrent blocks, where the interpretability of hidden states depends heavily on both the layer index and the decoding method. Finally, we empirically show that increasing recurrence depth yields only marginal gains and falls well short of models that explicitly externalize reasoning steps. The code is available at https://github.com/wenquanlu/huginn-latent-cot.
SciGA: A Comprehensive Dataset for Designing Graphical Abstracts in Academic Papers
Venue: arXiv cs.CV 2025
Year: 2025

Graphical Abstracts (GAs) play a crucial role in visually conveying the key findings of scientific papers. While recent research has increasingly incorporated visual materials such as Figure 1 as de facto GAs, their potential to enhance scientific communication remains largely unexplored. Moreover, designing effective GAs requires advanced visualization skills, creating a barrier to their widespread adoption. To tackle these challenges, we introduce SciGA-145k, a large-scale dataset comprising approximately 145,000 scientific papers and 1.14 million figures, explicitly designed for supporting GA selection and recommendation as well as facilitating research in automated GA generation. As a preliminary step toward GA design support, we define two tasks: 1) Intra-GA recommendation, which identifies figures within a given paper that are well-suited to serve as GAs, and 2) Inter-GA recommendation, which retrieves GAs from other papers to inspire the creation of new GAs. We provide reasonable baseline models for these tasks. Furthermore, we propose Confidence Adjusted top-1 ground truth Ratio (CAR), a novel recommendation metric that offers a fine-grained analysis of model behavior. CAR addresses limitations in traditional ranking-based metrics by considering cases where multiple figures within a paper, beyond the explicitly labeled GA, may also serve as GAs. By unifying these tasks and metrics, our SciGA-145k establishes a foundation for advancing visual scientific communication while contributing to the development of AI for Science.
Understanding Trade offs When Conditioning Synthetic Data
Venue: arXiv cs.CV 2025
Year: 2025

Learning robust object detectors from only a handful of images is a critical challenge in industrial vision systems, where collecting high quality training data can take months. Synthetic data has emerged as a key solution for data efficient visual inspection and pick and place robotics. Current pipelines rely on 3D engines such as Blender or Unreal, which offer fine control but still require weeks to render a small dataset, and the resulting images often suffer from a large gap between simulation and reality. Diffusion models promise a step change because they can generate high quality images in minutes, yet precise control, especially in low data regimes, remains difficult. Although many adapters now extend diffusion beyond plain text prompts, the effect of different conditioning schemes on synthetic data quality is poorly understood. We study eighty diverse visual concepts drawn from four standard object detection benchmarks and compare two conditioning strategies: prompt based and layout based. When the set of conditioning cues is narrow, prompt conditioning yields higher quality synthetic data; as diversity grows, layout conditioning becomes superior. When layout cues match the full training distribution, synthetic data raises mean average precision by an average of thirty four percent and by as much as one hundred seventy seven percent compared with using real data alone.
High-Fidelity Differential-information Driven Binary Vision Transformer
Venue: arXiv cs.CV 2025
Year: 2025

The binarization of vision transformers (ViTs) offers a promising approach to addressing the trade-off between high computational/storage demands and the constraints of edge-device deployment. However, existing binary ViT methods often suffer from severe performance degradation or rely heavily on full-precision modules. To address these issues, we propose DIDB-ViT, a novel binary ViT that is highly informative while maintaining the original ViT architecture and computational efficiency. Specifically, we design an informative attention module incorporating differential information to mitigate information loss caused by binarization and enhance high-frequency retention. To preserve the fidelity of the similarity calculations between binary Q and K tensors, we apply frequency decomposition using the discrete Haar wavelet and integrate similarities across different frequencies. Additionally, we introduce an improved RPReLU activation function to restructure the activation distribution, expanding the model's representational capacity. Experimental results demonstrate that our DIDB-ViT significantly outperforms state-of-the-art network quantization methods in multiple ViT architectures, achieving superior image classification and segmentation performance.
VERBA: Verbalizing Model Differences Using Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

In the current machine learning landscape, we face a "model lake" phenomenon: Given a task, there is a proliferation of trained models with similar performances despite different behavior. For model users attempting to navigate and select from the models, documentation comparing model pairs is helpful. However, for every $N$ models there could be $O(N^2)$ pairwise comparisons, a number prohibitive for the model developers to manually perform pairwise comparisons and prepare documentations. To facilitate fine-grained pairwise comparisons among models, we introduced $\textbf{VERBA}$. Our approach leverages a large language model (LLM) to generate verbalizations of model differences by sampling from the two models. We established a protocol that evaluates the informativeness of the verbalizations via simulation. We also assembled a suite with a diverse set of commonly used machine learning models as a benchmark. For a pair of decision tree models with up to 5% performance difference but 20-25% behavioral differences, $\textbf{VERBA}$ effectively verbalizes their variations with up to 80% overall accuracy. When we included the models' structural information, the verbalization's accuracy further improved to 90%. $\textbf{VERBA}$ opens up new research avenues for improving the transparency and comparability of machine learning models in a post-hoc manner.
Uncertainty-aware Reward Design Process
Venue: arXiv cs.LG 2025
Year: 2025

Designing effective reward functions is a cornerstone of reinforcement learning (RL), yet it remains a challenging process due to the inefficiencies and inconsistencies inherent in conventional reward engineering methodologies. Recent advances have explored leveraging large language models (LLMs) to automate reward function design. However, their suboptimal performance in numerical optimization often yields unsatisfactory reward quality, while the evolutionary search paradigm demonstrates inefficient utilization of simulation resources, resulting in prohibitively lengthy design cycles with disproportionate computational overhead. To address these challenges, we propose the Uncertainty-aware Reward Design Process (URDP), a novel framework that integrates large language models to streamline reward function design and evaluation in RL environments. URDP quantifies candidate reward function uncertainty based on self-consistency analysis, enabling simulation-free identification of ineffective reward components while discovering novel reward components. Furthermore, we introduce uncertainty-aware Bayesian optimization (UABO), which incorporates uncertainty estimation to significantly enhance hyperparameter configuration efficiency. Finally, we construct a bi-level optimization architecture by decoupling the reward component optimization and the hyperparameter tuning. URDP orchestrates synergistic collaboration between the reward logic reasoning of the LLMs and the numerical optimization strengths of the Bayesian Optimization. We conduct a comprehensive evaluation of URDP across 35 diverse tasks spanning three benchmark environments. Our experimental results demonstrate that URDP not only generates higher-quality reward functions but also achieves significant improvements in the efficiency of automated reward design compared to existing approaches.
Measurements and Modeling of Air-Ground Integrated Channel in Forest Environment Based on OFDM Signals
Venue: arXiv cs.IT 2025
Year: 2025

Forests are frequently impacted by climate conditions, vegetation density, and intricate terrain and geology, which contribute to natural disasters. Personnel engaged in or supporting rescue operations in such environments rely on robust communication systems to ensure their safety, highlighting the criticality of channel measurements in forest environments. However, according to current research, there is limited research on channel detection and modeling in forest areas in the existing literature. This paper describes the channel measurements campaign of air and ground in the Arxan National Forest Park of Inner Mongolia. It presents measurement results and propagation models for ground-to-ground (G2G) and air-to-ground (A2G) scenarios. The measurement campaign uses orthogonal frequency division multiplexing signals centered at 1.4 GHz for channel sounding. In the G2G measurement, in addition to using omnidirectional antennas to record data, we also use directional antennas to record the arrival angle information of the signal at the receiver. In the A2G measurement, we pre-plan the flight trajectory of the unmanned aerial vehicle so that it can fly at a fixed angle relative to the ground. We present path loss models suitable for G2G and A2G in forest environments based on the analysis of measurement results. The results indicate that the proposed model reduces error margins compared with other path loss models. Furthermore, we derive the multipath model expression specific to forest environments and conduct statistical analysis on key channel parameters e.g., shadow fading factor, root mean square delay spread, and Rician K factor. Our findings reveal that signal propagation obstruction due to tree crowns in A2G communication is more pronounced than tree trunk obstructions in G2G communication. Adjusting the elevation angle between air and ground can enhance communication quality.
Rethinking Broken Object Level Authorization Attacks Under Zero Trust Principle
Venue: arXiv cs.CR 2025
Year: 2025

RESTful APIs facilitate data exchange between applications, but they also expose sensitive resources to potential exploitation. Broken Object Level Authorization (BOLA) is the top vulnerability in the OWASP API Security Top 10, exemplifies a critical access control flaw where attackers manipulate API parameters to gain unauthorized access. To address this, we propose BOLAZ, a defense framework grounded in zero trust principles. BOLAZ analyzes the data flow of resource IDs, pinpointing BOLA attack injection points and determining the associated authorization intervals to prevent horizontal privilege escalation. Our approach leverages static taint tracking to categorize APIs into producers and consumers based on how they handle resource IDs. By mapping the propagation paths of resource IDs, BOLAZ captures the context in which these IDs are produced and consumed, allowing for precise identification of authorization boundaries. Unlike defense methods based on common authorization models, BOLAZ is the first authorization-guided method that adapts defense rules based on the system's best-practice authorization logic. We validate BOLAZ through empirical research on 10 GitHub projects. The results demonstrate BOLAZ's effectiveness in defending against vulnerabilities collected from CVE and discovering 35 new BOLA vulnerabilities in the wild, demonstrating its practicality in real-world deployments.
Hita: Holistic Tokenizer for Autoregressive Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

Vanilla autoregressive image generation models generate visual tokens step-by-step, limiting their ability to capture holistic relationships among token sequences. Moreover, because most visual tokenizers map local image patches into latent tokens, global information is limited. To address this, we introduce \textit{Hita}, a novel image tokenizer for autoregressive (AR) image generation. It introduces a holistic-to-local tokenization scheme with learnable holistic queries and local patch tokens. Hita incorporates two key strategies to better align with the AR generation process: 1) {arranging} a sequential structure with holistic tokens at the beginning, followed by patch-level tokens, and using causal attention to maintain awareness of previous tokens; and 2) adopting a lightweight fusion module before feeding the de-quantized tokens into the decoder to control information flow and prioritize holistic tokens. Extensive experiments show that Hita accelerates the training speed of AR generators and outperforms those trained with vanilla tokenizers, achieving \textbf{2.59 FID} and \textbf{281.9 IS} on the ImageNet benchmark. Detailed analysis of the holistic representation highlights its ability to capture global image properties, such as textures, materials, and shapes. Additionally, Hita also demonstrates effectiveness in zero-shot style transfer and image in-painting. The code is available at \href{https://github.com/CVMI-Lab/Hita}{https://github.com/CVMI-Lab/Hita}.
QFFN-BERT: An Empirical Study of Depth, Performance, and Data Efficiency in Hybrid Quantum-Classical Transformers
Venue: arXiv cs.CL 2025
Year: 2025

Parameterized quantum circuits (PQCs) have recently emerged as promising components for enhancing the expressibility of neural architectures. In this work, we introduce QFFN-BERT, a hybrid quantum-classical transformer where the feedforward network (FFN) modules of a compact BERT variant are replaced by PQC-based layers. This design is motivated by the dominant parameter contribution of FFNs, which account for approximately two-thirds of the parameters within standard Transformer encoder blocks. While prior studies have primarily integrated PQCs into self-attention modules, our work focuses on the FFN and systematically investigates the trade-offs between PQC depth, expressibility, and trainability. Our final PQC architecture incorporates a residual connection, both $R_Y$ and $R_Z$ rotations, and an alternating entanglement strategy to ensure stable training and high expressibility. Our experiments, conducted on a classical simulator, on the SST-2 and DBpedia benchmarks demonstrate two key findings. First, a carefully configured QFFN-BERT achieves up to 102.0% of the baseline accuracy, surpassing its classical counterpart in a full-data setting while reducing FFN-specific parameters by over 99%. Second, our model exhibits a consistent and competitive edge in few-shot learning scenarios, confirming its potential for superior data efficiency. These results, supported by an ablation study on a non-optimized PQC that failed to learn, confirm that PQCs can serve as powerful and parameter-efficient alternatives to classical FFNs when co-designed with foundational deep learning principles.
An Experimental Approach for Running-Time Estimation of Multi-objective Evolutionary Algorithms in Numerical Optimization
Venue: arXiv cs.NE 2025
Year: 2025

Multi-objective evolutionary algorithms (MOEAs) have become essential tools for solving multi-objective optimization problems (MOPs), making their running time analysis crucial for assessing algorithmic efficiency and guiding practical applications. While significant theoretical advances have been achieved for combinatorial optimization, existing studies for numerical optimization primarily rely on algorithmic or problem simplifications, limiting their applicability to real-world scenarios. To address this gap, we propose an experimental approach for estimating upper bounds on the running time of MOEAs in numerical optimization without simplification assumptions. Our approach employs an average gain model that characterizes algorithmic progress through the Inverted Generational Distance metric. To handle the stochastic nature of MOEAs, we use statistical methods to estimate the probabilistic distribution of gains. Recognizing that gain distributions in numerical optimization exhibit irregular patterns with varying densities across different regions, we introduce an adaptive sampling method that dynamically adjusts sampling density to ensure accurate surface fitting for running time estimation. We conduct comprehensive experiments on five representative MOEAs (NSGA-II, MOEA/D, AR-MOEA, AGEMOEA-II, and PREA) using the ZDT and DTLZ benchmark suites. The results demonstrate the effectiveness of our approach in estimating upper bounds on the running time without requiring algorithmic or problem simplifications. Additionally, we provide a web-based implementation to facilitate broader adoption of our methodology. This work provides a practical complement to theoretical research on MOEAs in numerical optimization.