OpenRaiser/Intern-Atlas huggingface dataset:
RVAFM: Re-parameterizing Vertical Attention Fusion Module for Handwritten Paragraph Text Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Handwritten Paragraph Text Recognition (HPTR) is a challenging task in Computer Vision, requiring the transformation of a paragraph text image, rich in handwritten text, into text encoding sequences. One of the most advanced models for this task is Vertical Attention Network (VAN), which utilizes a Vertical Attention Module (VAM) to implicitly segment paragraph text images into text lines, thereby reducing the difficulty of the recognition task. However, from a network structure perspective, VAM is a single-branch module, which is less effective in learning compared to multi-branch modules. In this paper, we propose a new module, named Re-parameterizing Vertical Attention Fusion Module (RVAFM), which incorporates structural re-parameterization techniques. RVAFM decouples the structure of the module during training and inference stages. During training, it uses a multi-branch structure for more effective learning, and during inference, it uses a single-branch structure for faster processing. The features learned by the multi-branch structure are fused into the single-branch structure through a special fusion method named Re-parameterization Fusion (RF) without any loss of information. As a result, we achieve a Character Error Rate (CER) of 4.44% and a Word Error Rate (WER) of 14.37% on the IAM paragraph-level test set. Additionally, the inference speed is slightly faster than VAN.
A Multimodal Framework for Topic Propagation Classification in Social Networks
Venue: arXiv cs.SI 2025
Year: 2025

The rapid proliferation of the Internet and the widespread adoption of social networks have significantly accelerated information dissemination. However, this transformation has introduced complexities in information capture and processing, posing substantial challenges for researchers and practitioners. Predicting the dissemination of topic-related information within social networks has thus become a critical research focus. This paper proposes a predictive model for topic dissemination in social networks by integrating multidimensional features derived from key dissemination characteristics. Specifically, we introduce two novel indicators, user relationship breadth and user authority, into the PageRank algorithm to quantify user influence more effectively. Additionally, we employ a Text-CNN model for sentiment classification, extracting sentiment features from textual content. Temporal embeddings of nodes are encoded using a Bi-LSTM model to capture temporal dynamics. Furthermore, we refine the measurement of user interaction traces with topics, replacing traditional topic view metrics with a more precise communication characteristics measure. Finally, we integrate the extracted multidimensional features using a Transformer model, significantly enhancing predictive performance. Experimental results demonstrate that our proposed model outperforms traditional machine learning and unimodal deep learning models in terms of FI-Score, AUC, and Recall, validating its effectiveness in predicting topic propagation within social networks.
Position: Model Collapse Does Not Mean What You Think
Venue: arXiv cs.LG 2025
Year: 2025

The proliferation of AI-generated content online has fueled concerns over \emph{model collapse}, a degradation in future generative models' performance when trained on synthetic data generated by earlier models. Industry leaders, premier research journals and popular science publications alike have prophesied catastrophic societal consequences stemming from model collapse. In this position piece, we contend this widespread narrative fundamentally misunderstands the scientific evidence. We highlight that research on model collapse actually encompasses eight distinct and at times conflicting definitions of model collapse, and argue that inconsistent terminology within and between papers has hindered building a comprehensive understanding of model collapse. To assess how significantly different interpretations of model collapse threaten future generative models, we posit what we believe are realistic conditions for studying model collapse and then conduct a rigorous assessment of the literature's methodologies through this lens. While we leave room for reasonable disagreement, our analysis of research studies, weighted by how faithfully each study matches real-world conditions, leads us to conclude that certain predicted claims of model collapse rely on assumptions and conditions that poorly match real-world conditions, and in fact several prominent collapse scenarios are readily avoidable. Altogether, this position paper argues that model collapse has been warped from a nuanced multifaceted consideration into an oversimplified threat, and that the evidence suggests specific harms more likely under society's current trajectory have received disproportionately less attention.
A Predict-Then-Optimize Customer Allocation Framework for Online Fund Recommendation
Venue: arXiv cs.CE 2025
Year: 2025

With the rapid growth of online investment platforms, funds can be distributed to individual customers online. The central issue is to match funds with potential customers under constraints. Most mainstream platforms adopt the recommendation formulation to tackle the problem. However, the traditional recommendation regime has its inherent drawbacks when applying the fund-matching problem with multiple constraints. In this paper, we model the fund matching under the allocation formulation. We design PTOFA, a Predict-Then-Optimize Fund Allocation framework. This data-driven framework consists of two stages, i.e., prediction and optimization, which aim to predict expected revenue based on customer behavior and optimize the impression allocation to achieve the maximum revenue under the necessary constraints, respectively. Extensive experiments on real-world datasets from an industrial online investment platform validate the effectiveness and efficiency of our solution. Additionally, the online A/B tests demonstrate PTOFA's effectiveness in the real-world fund recommendation scenario.
Efficient Parallel Execution of Blockchain Transactions Leveraging Conflict Specifications
Venue: arXiv cs.DC 2025
Year: 2025

Parallel execution of smart contract transactions in large multicore architectures is critical for higher efficiency and improved throughput. The main bottleneck for maximizing the throughput of a node through parallel execution is transaction conflict resolution: when two transactions interact with the same data, like an account balance, their order matters. Imagine one transaction sends tokens from account A to account B, and another tries to send tokens from account B to account C. If the second transaction happens before the first one, the token balance in account B might be wrong, causing the entire system to break. Conflicts like these must be managed carefully, or you end up with an inconsistent, unusable blockchain state. Traditional software transactional memory (STM) has been identified as a possible abstraction for the concurrent execution of transactions within a block, with Block-STM pioneering its application for efficient blockchain transaction processing on multicore validator nodes. This paper presents a parallel execution methodology that leverages conflict specification information of the transactions for block transactional memory (BTM) algorithms. Our experimental analysis, conducted over synthetic transactional workloads and real-world blocks, demonstrates that BTMs leveraging conflict specifications outperform their plain counterparts on both EVM and MoveVM. Our proposed BTM implementations achieve up to 1.75x speedup over sequential execution and outperform the state-of-the-art Parallel-EVM (PEVM) execution by up to 1.33x across synthetic workloads.
Sensing Rate Optimization for Multi-Band Cooperative ISAC Systems
Venue: arXiv cs.IT 2025
Year: 2025

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies for future wireless networks, which potentially need to operate in multiple frequency bands to satisfy ever-increasing demands for both communication and sensing services. Motivated by this, we consider the sum sensing rate (SR) optimization for a cooperative ISAC system with linear precoding, where each base station (BS) works in a different frequency band. With this aim, we propose an optimization algorithm based on the semi-definite rank relaxation that introduces covariance matrices as optimization variables, and we apply the inner approximation (IA) method to deal with the nonconvexity of the resulting problem. Simulation results show that the proposed algorithm increases the SR by approximately 25 % and 40 % compared to the case of equal power distribution in a cooperative ISAC system with two and three BSs, respectively. Additionally, the algorithm converges in only a few iterations, while its most beneficial implementation scenario is in the low power regime
Less is more? Rewards in RL for Cyber Defence
Venue: arXiv cs.LG 2025
Year: 2025

The last few years have seen an explosion of interest in autonomous cyber defence agents based on deep reinforcement learning. Such agents are typically trained in a cyber gym environment, also known as a cyber simulator, at least 32 of which have already been built. Most, if not all cyber gyms provide dense "scaffolded" reward functions which combine many penalties or incentives for a range of (un)desirable states and costly actions. Whilst dense rewards help alleviate the challenge of exploring complex environments, yielding seemingly effective strategies from relatively few environment steps; they are also known to bias the solutions an agent can find, potentially towards suboptimal solutions. This is especially a problem in complex cyber environments where policy weaknesses may not be noticed until exploited by an adversary. In this work we set out to evaluate whether sparse reward functions might enable training more effective cyber defence agents. Towards this goal we first break down several evaluation limitations in existing work by proposing a ground truth evaluation score that goes beyond the standard RL paradigm used to train and evaluate agents. By adapting a well-established cyber gym to accommodate our methodology and ground truth score, we propose and evaluate two sparse reward mechanisms and compare them with a typical dense reward. Our evaluation considers a range of network sizes, from 2 to 50 nodes, and both reactive and proactive defensive actions. Our results show that sparse rewards, particularly positive reinforcement for an uncompromised network state, enable the training of more effective cyber defence agents. Furthermore, we show that sparse rewards provide more stable training than dense rewards, and that both effectiveness and training stability are robust to a variety of cyber environment considerations.
BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Accurate motion understanding of the dynamic objects within the scene in bird's-eye-view (BEV) is critical to ensure a reliable obstacle avoidance system and smooth path planning for autonomous vehicles. However, this task has received relatively limited exploration when compared to object detection and segmentation with only a few recent vision-based approaches presenting preliminary findings that significantly deteriorate in low-light, nighttime, and adverse weather conditions such as rain. Conversely, LiDAR and radar sensors remain almost unaffected in these scenarios, and radar provides key velocity information of the objects. Therefore, we introduce BEVMOSNet, to our knowledge, the first end-to-end multimodal fusion leveraging cameras, LiDAR, and radar to precisely predict the moving objects in BEV. In addition, we perform a deeper analysis to find out the optimal strategy for deformable cross-attention-guided sensor fusion for cross-sensor knowledge sharing in BEV. While evaluating BEVMOSNet on the nuScenes dataset, we show an overall improvement in IoU score of 36.59% compared to the vision-based unimodal baseline BEV-MoSeg (Sigatapu et al., 2023), and 2.35% compared to the multimodel SimpleBEV (Harley et al., 2022), extended for the motion segmentation task, establishing this method as the state-of-the-art in BEV motion segmentation.
Supervised Visual Docking Network for Unmanned Surface Vehicles Using Auto-labeling in Real-world Water Environments
Venue: arXiv cs.RO 2025
Year: 2025

Unmanned Surface Vehicles (USVs) are increasingly applied to water operations such as environmental monitoring and river-map modeling. It faces a significant challenge in achieving precise autonomous docking at ports or stations, still relying on remote human control or external positioning systems for accuracy and safety which limits the full potential of human-out-of-loop deployment for USVs.This paper introduces a novel supervised learning pipeline with the auto-labeling technique for USVs autonomous visual docking. Firstly, we designed an auto-labeling data collection pipeline that appends relative pose and image pair to the dataset. This step does not require conventional manual labeling for supervised learning. Secondly, the Neural Dock Pose Estimator (NDPE) is proposed to achieve relative dock pose prediction without the need for hand-crafted feature engineering, camera calibration, and peripheral markers. Moreover, The NDPE can accurately predict the relative dock pose in real-world water environments, facilitating the implementation of Position-Based Visual Servo (PBVS) and low-level motion controllers for efficient and autonomous docking.Experiments show that the NDPE is robust to the disturbance of the distance and the USV velocity. The effectiveness of our proposed solution is tested and validated in real-world water environments, reflecting its capability to handle real-world autonomous docking tasks.
The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation
Venue: arXiv cs.CL 2025
Year: 2025

Does neural machine translation yield translations that are congenial with common sense? In this paper, we present a test suite to evaluate the commonsense reasoning capability of neural machine translation. The test suite consists of three test sets, covering lexical and contextless/contextual syntactic ambiguity that requires commonsense knowledge to resolve. We manually create 1,200 triples, each of which contain a source sentence and two contrastive translations, involving 7 different common sense types. Language models pretrained on large-scale corpora, such as BERT, GPT-2, achieve a commonsense reasoning accuracy of lower than 72% on target translations of this test suite. We conduct extensive experiments on the test suite to evaluate commonsense reasoning in neural machine translation and investigate factors that have impact on this capability. Our experiments and analyses demonstrate that neural machine translation performs poorly on commonsense reasoning of the three ambiguity types in terms of both reasoning accuracy (60.1%) and reasoning consistency (31%). The built commonsense test suite is available at https://github.com/tjunlp-lab/CommonMT.
From Infants to AI: Incorporating Infant-like Learning in Models Boosts Efficiency and Generalization in Learning Social Prediction Tasks
Venue: arXiv cs.AI 2025
Year: 2025

Early in development, infants learn a range of useful concepts, which can be challenging from a computational standpoint. This early learning comes together with an initial understanding of aspects of the meaning of concepts, e.g., their implications, causality, and using them to predict likely future events. All this is accomplished in many cases with little or no supervision, and from relatively few examples, compared with current network models. In learning about objects and human-object interactions, early acquired and possibly innate concepts are often used in the process of learning additional, more complex concepts. In the current work, we model how early-acquired concepts are used in the learning of subsequent concepts, and compare the results with standard deep network modeling. We focused in particular on the use of the concepts of animacy and goal attribution in learning to predict future events. We show that the use of early concepts in the learning of new concepts leads to better learning (higher accuracy) and more efficient learning (requiring less data). We further show that this integration of early and new concepts shapes the representation of the concepts acquired by the model. The results show that when the concepts were learned in a human-like manner, the emerging representation was more useful, as measured in terms of generalization to novel data and tasks. On a more general level, the results suggest that there are likely to be basic differences in the conceptual structures acquired by current network models compared to human learning.
Direct Sparse Odometry with Continuous 3D Gaussian Maps for Indoor Environments
Venue: arXiv cs.RO 2025
Year: 2025

Accurate localization is essential for robotics and augmented reality applications such as autonomous navigation. Vision-based methods combining prior maps aim to integrate LiDAR-level accuracy with camera cost efficiency for robust pose estimation. Existing approaches, however, often depend on unreliable interpolation procedures when associating discrete point cloud maps with dense image pixels, which inevitably introduces depth errors and degrades pose estimation accuracy. We propose a monocular visual odometry framework utilizing a continuous 3D Gaussian map, which directly assigns geometrically consistent depth values to all extracted high-gradient points without interpolation. Evaluations on two public datasets demonstrate superior tracking accuracy compared to existing methods. We have released the source code of this work for the development of the community.
The Serendipity of Claude AI: Case of the 13 Low-Resource National Languages of Mali
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in artificial intelligence (AI) and natural language processing (NLP) have improved the representation of underrepresented languages. However, most languages, including Mali's 13 official national languages, continue to be poorly supported or unsupported by automatic translation and generative AI. This situation appears to have slightly improved with certain recent LLM releases. The study evaluated Claude AI's translation performance on each of the 13 national languages of Mali. In addition to ChrF2 and BLEU scores, human evaluators assessed translation accuracy, contextual consistency, robustness to dialect variations, management of linguistic bias, adaptation to a limited corpus, and ease of understanding. The study found that Claude AI performs robustly for languages with very modest language resources and, while unable to produce understandable and coherent texts for Malian languages with minimal resources, still manages to produce results which demonstrate the ability to mimic some elements of the language.
"Till I can get my satisfaction": Open Questions in the Public Desire to Punish AI
Venue: arXiv cs.HC 2025
Year: 2025

There are countless examples of how AI can cause harm, and increasing evidence that the public are willing to ascribe blame to the AI itself, regardless of how "illogical" this might seem. This raises the question of whether and how the public might expect AI to be punished for this harm. However, public expectations of the punishment of AI have been vastly underexplored. Understanding these expectations is vital, as the public may feel the lingering effect of harm unless their desire for punishment is satisfied. We synthesise research from psychology, human-computer and -robot interaction, philosophy and AI ethics, and law to highlight how our understanding of this issue is still lacking. We call for an interdisciplinary programme of research to establish how we can best satisfy victims of AI harm, for fear of creating a "satisfaction gap" where legal punishment of AI (or not) fails to meet public expectations.
SoK: Microservice Architectures from a Dependability Perspective
Venue: arXiv cs.DC 2025
Year: 2025

The microservice software architecture leverages the idea of splitting large monolithic applications into multiple smaller services that interact using lightweight communication schemes. While the microservice architecture has proven its ability to support modern business applications, it also introduces new possible weak points in a system. Some scientific literature surveys have already addressed fault tolerance or security concerns but most of them lack analysis on the fault and vulnerability coverage that is introduced by microservice architectures. We explore the known faults and vulnerabilities that microservice architecture might suffer from, and the recent scientific literature that addresses them. We emphasize runtime detection and recovery mechanisms instead of offline prevention and mitigation mechanisms to limit the scope of this document.
Evolutionary Prediction Games
Venue: arXiv cs.LG 2025
Year: 2025

When a prediction algorithm serves a collection of users, disparities in prediction quality are likely to emerge. If users respond to accurate predictions by increasing engagement, inviting friends, or adopting trends, repeated learning creates a feedback loop that shapes both the model and the population of its users. In this work, we introduce evolutionary prediction games, a framework grounded in evolutionary game theory which models such feedback loops as natural-selection processes among groups of users. Our theoretical analysis reveals a gap between idealized and real-world learning settings: In idealized settings with unlimited data and computational power, repeated learning creates competition and promotes competitive exclusion across a broad class of behavioral dynamics. However, under realistic constraints such as finite data, limited compute, or risk of overfitting, we show that stable coexistence and mutualistic symbiosis between groups becomes possible. We analyze these possibilities in terms of their stability and feasibility, present mechanisms that can sustain their existence, and empirically demonstrate our findings.
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Hemodynamic parameters such as pressure and wall shear stress play an important role in diagnosis, prognosis, and treatment planning in cardiovascular diseases. These parameters can be accurately computed using computational fluid dynamics (CFD), but CFD is computationally intensive. Hence, deep learning methods have been adopted as a surrogate to rapidly estimate CFD outcomes. A drawback of such data-driven models is the need for time-consuming reference CFD simulations for training. In this work, we introduce an active learning framework to reduce the number of CFD simulations required for the training of surrogate models, lowering the barriers to their deployment in new applications. We propose three distinct querying strategies to determine for which unlabeled samples CFD simulations should be obtained. These querying strategies are based on geometrical variance, ensemble uncertainty, and adherence to the physics governing fluid dynamics. We benchmark these methods on velocity field estimation in synthetic coronary artery bifurcations and find that they allow for substantial reductions in annotation cost. Notably, we find that our strategies reduce the number of samples required by up to 50% and make the trained models more robust to difficult cases. Our results show that active learning is a feasible strategy to increase the potential of deep learning-based CFD surrogates.