OpenRaiser/Intern-Atlas huggingface dataset:
Local Foreground Selection aware Attentive Feature Reconstruction for few-shot fine-grained plant species classification
Venue: arXiv cs.CV 2025
Year: 2025

Plant species exhibit significant intra-class variation and minimal inter-class variation. To enhance classification accuracy, it is essential to reduce intra-class variation while maximizing inter-class variation. This paper addresses plant species classification using a limited number of labelled samples and introduces a novel Local Foreground Selection(LFS) attention mechanism. LFS is a straightforward module designed to generate discriminative support and query feature maps. It operates by integrating two types of attention: local attention, which captures local spatial details to enhance feature discrimination and increase inter-class differentiation, and foreground selection attention, which emphasizes the foreground plant object while mitigating background interference. By focusing on the foreground, the query and support features selectively highlight relevant feature sequences and disregard less significant background sequences, thereby reducing intra-class differences. Experimental results from three plant species datasets demonstrate the effectiveness of the proposed LFS attention mechanism and its complementary advantages over previous feature reconstruction methods.
Towards Fair and Privacy-Aware Transfer Learning for Educational Predictive Modeling: A Case Study on Retention Prediction in Community Colleges
Venue: arXiv cs.CY 2025
Year: 2025

Predictive analytics is widely used in learning analytics, but many resource-constrained institutions lack the capacity to develop their own models or rely on proprietary ones trained in different contexts with little transparency. Transfer learning holds promise for expanding equitable access to predictive analytics but remains underexplored due to legal and technical constraints. This paper examines transfer learning strategies for retention prediction at U.S. two-year community colleges. We envision a scenario where community colleges collaborate with each other and four-year universities to develop retention prediction models under privacy constraints and evaluate risks and improvement strategies of cross-institutional model transfer. Using administrative records from 4 research universities and 23 community colleges covering over 800,000 students across 7 cohorts, we identify performance and fairness degradation when external models are deployed locally without adaptation. Publicly available contextual information can forecast these performance drops and offer early guidance for model portability. For developers under privacy regulations, sequential training selecting institutions based on demographic similarities enhances fairness without compromising performance. For institutions lacking local data to fine-tune source models, customizing evaluation thresholds for sensitive groups outperforms standard transfer techniques in improving performance and fairness. Our findings suggest the value of transfer learning for more accessible educational predictive modeling and call for judicious use of contextual information in model training, selection, and deployment to achieve reliable and equitable model transfer.
Shake-VLA: Vision-Language-Action Model-Based System for Bimanual Robotic Manipulations and Liquid Mixing
Venue: arXiv cs.RO 2025
Year: 2025

This paper introduces Shake-VLA, a Vision-Language-Action (VLA) model-based system designed to enable bimanual robotic manipulation for automated cocktail preparation. The system integrates a vision module for detecting ingredient bottles and reading labels, a speech-to-text module for interpreting user commands, and a language model to generate task-specific robotic instructions. Force Torque (FT) sensors are employed to precisely measure the quantity of liquid poured, ensuring accuracy in ingredient proportions during the mixing process. The system architecture includes a Retrieval-Augmented Generation (RAG) module for accessing and adapting recipes, an anomaly detection mechanism to address ingredient availability issues, and bimanual robotic arms for dexterous manipulation. Experimental evaluations demonstrated a high success rate across system components, with the speech-to-text module achieving a 93% success rate in noisy environments, the vision module attaining a 91% success rate in object and label detection in cluttered environment, the anomaly module successfully identified 95% of discrepancies between detected ingredients and recipe requirements, and the system achieved an overall success rate of 100% in preparing cocktails, from recipe formulation to action generation.
An Empirical Study of Deep Reinforcement Learning in Continuing Tasks
Venue: arXiv cs.AI 2025
Year: 2025

In reinforcement learning (RL), continuing tasks refer to tasks where the agent-environment interaction is ongoing and can not be broken down into episodes. These tasks are suitable when environment resets are unavailable, agent-controlled, or predefined but where all rewards-including those beyond resets-are critical. These scenarios frequently occur in real-world applications and can not be modeled by episodic tasks. While modern deep RL algorithms have been extensively studied and well understood in episodic tasks, their behavior in continuing tasks remains underexplored. To address this gap, we provide an empirical study of several well-known deep RL algorithms using a suite of continuing task testbeds based on Mujoco and Atari environments, highlighting several key insights concerning continuing tasks. Using these testbeds, we also investigate the effectiveness of a method for improving temporal-difference-based RL algorithms in continuing tasks by centering rewards, as introduced by Naik et al. (2024). While their work primarily focused on this method in conjunction with Q-learning, our results extend their findings by demonstrating that this method is effective across a broader range of algorithms, scales to larger tasks, and outperforms two other reward-centering approaches.
The Einstein Test: Towards a Practical Test of a Machine's Ability to Exhibit Superintelligence
Venue: arXiv cs.AI 2025
Year: 2025

Creative and disruptive insights (CDIs), such as the development of the theory of relativity, have punctuated human history, marking pivotal shifts in our intellectual trajectory. Recent advancements in artificial intelligence (AI) have sparked debates over whether state of the art models possess the capacity to generate CDIs. We argue that the ability to create CDIs should be regarded as a significant feature of machine superintelligence (SI).To this end, we propose a practical test to evaluate whether an approach to AI targeting SI can yield novel insights of this kind. We propose the Einstein test: given the data available prior to the emergence of a known CDI, can an AI independently reproduce that insight (or one that is formally equivalent)? By achieving such a milestone, a machine can be considered to at least match humanity's past top intellectual achievements, and therefore to have the potential to surpass them.
A Hessian-informed hyperparameter optimization for differential learning rate
Venue: arXiv cs.LG 2025
Year: 2025

Differential learning rate (DLR), a technique that applies different learning rates to different model parameters, has been widely used in deep learning and achieved empirical success via its various forms. For example, parameter-efficient fine-tuning (PEFT) applies zero learning rates to most parameters so as to significantly save the computational cost. At the core, DLR leverages the observation that different parameters can have different loss curvature, which is hard to characterize in general. We propose the Hessian-informed differential learning rate (Hi-DLR), an efficient approach that solves the hyperparameter optimization (HPO) of learning rates and captures the loss curvature for any model and optimizer adaptively. Given a proper grouping of parameters, we empirically demonstrate that Hi-DLR can improve the convergence by dynamically determining the learning rates during the training.
Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps
Venue: arXiv cs.LG 2025
Year: 2025

Cascaded models are multi-scale generative models with a marked capacity for producing perceptually impressive samples at high resolutions. In this work, we show that they can also be excellent likelihood models, so long as we overcome a fundamental difficulty with probabilistic multi-scale models: the intractability of the likelihood function. Chiefly, in cascaded models each intermediary scale introduces extraneous variables that cannot be tractably marginalized out for likelihood evaluation. This issue vanishes by modeling the diffusion process on latent spaces induced by a class of transformations we call hierarchical volume-preserving maps, which decompose spatially structured data in a hierarchical fashion without introducing local distortions in the latent space. We demonstrate that two such maps are well-known in the literature for multiscale modeling: Laplacian pyramids and wavelet transforms. Not only do such reparameterizations allow the likelihood function to be directly expressed as a joint likelihood over the scales, we show that the Laplacian pyramid and wavelet transform also produces significant improvements to the state-of-the-art on a selection of benchmarks in likelihood modeling, including density estimation, lossless compression, and out-of-distribution detection. Investigating the theoretical basis of our empirical gains we uncover deep connections to score matching under the Earth Mover's Distance (EMD), which is a well-known surrogate for perceptual similarity. Code can be found at \href{https://github.com/lihenryhfl/pcdm}{this https url}.
Sthymuli: a Static Educational Robot. Leveraging the Thymio II Platform
Venue: arXiv cs.RO 2025
Year: 2025

The use of robots in education represents a challenge for teachers and a fixed vision of what robots can do for students. This paper presents the development of Sthymuli, a static educational robot designed to explore new classroom interactions between robots, students and teachers. We propose the use of the Thymio II educational platform as a base, ensuring a robust benchmark for a fair comparison of the commonly available wheeled robots and our exploratory approach with Sthymuli. This paper outlines the constraints and requirements for developing such a robot, the current state of development and future work.
AlgoRxplorers | Precision in Mutation: Enhancing Drug Design with Advanced Protein Stability Prediction Tools
Venue: arXiv cs.LG 2025
Year: 2025

Predicting the impact of single-point amino acid mutations on protein stability is essential for understanding disease mechanisms and advancing drug development. Protein stability, quantified by changes in Gibbs free energy ($ΔΔG$), is influenced by these mutations. However, the scarcity of data and the complexity of model interpretation pose challenges in accurately predicting stability changes. This study proposes the application of deep neural networks, leveraging transfer learning and fusing complementary information from different models, to create a feature-rich representation of the protein stability landscape. We developed four models, with our third model, ThermoMPNN+, demonstrating the best performance in predicting $ΔΔG$ values. This approach, which integrates diverse feature sets and embeddings through latent transfusion techniques, aims to refine $ΔΔG$ predictions and contribute to a deeper understanding of protein dynamics, potentially leading to advancements in disease research and drug discovery.
Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models
Venue: arXiv cs.LG 2025
Year: 2025

This study explores the use of Generative Adversarial Networks (GANs) to detect AI deepfakes and fraudulent activities in online payment systems. With the growing prevalence of deepfake technology, which can manipulate facial features in images and videos, the potential for fraud in online transactions has escalated. Traditional security systems struggle to identify these sophisticated forms of fraud. This research proposes a novel GAN-based model that enhances online payment security by identifying subtle manipulations in payment images. The model is trained on a dataset consisting of real-world online payment images and deepfake images generated using advanced GAN architectures, such as StyleGAN and DeepFake. The results demonstrate that the proposed model can accurately distinguish between legitimate transactions and deepfakes, achieving a high detection rate above 95%. This approach significantly improves the robustness of payment systems against AI-driven fraud. The paper contributes to the growing field of digital security, offering insights into the application of GANs for fraud detection in financial services. Keywords- Payment Security, Image Recognition, Generative Adversarial Networks, AI Deepfake, Fraudulent Activities
Rethinking Knowledge in Distillation: An In-context Sample Retrieval Perspective
Venue: arXiv cs.CV 2025
Year: 2025

Conventional knowledge distillation (KD) approaches are designed for the student model to predict similar output as the teacher model for each sample. Unfortunately, the relationship across samples with same class is often neglected. In this paper, we explore to redefine the knowledge in distillation, capturing the relationship between each sample and its corresponding in-context samples (a group of similar samples with the same or different classes), and perform KD from an in-context sample retrieval perspective. As KD is a type of learned label smoothing regularization (LSR), we first conduct a theoretical analysis showing that the teacher's knowledge from the in-context samples is a crucial contributor to regularize the student training with the corresponding samples. Buttressed by the analysis, we propose a novel in-context knowledge distillation (IC-KD) framework that shows its superiority across diverse KD paradigms (offline, online, and teacher-free KD). Firstly, we construct a feature memory bank from the teacher model and retrieve in-context samples for each corresponding sample through retrieval-based learning. We then introduce Positive In-Context Distillation (PICD) to reduce the discrepancy between a sample from the student and the aggregated in-context samples with the same class from the teacher in the logit space. Moreover, Negative In-Context Distillation (NICD) is introduced to separate a sample from the student and the in-context samples with different classes from the teacher in the logit space. Extensive experiments demonstrate that IC-KD is effective across various types of KD, and consistently achieves state-of-the-art performance on CIFAR-100 and ImageNet datasets.
Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities
Venue: arXiv cs.CR 2025
Year: 2025

Smart contract vulnerabilities caused significant economic losses in blockchain applications. Large Language Models (LLMs) provide new possibilities for addressing this time-consuming task. However, state-of-the-art LLM-based detection solutions are often plagued by high false-positive rates. In this paper, we push the boundaries of existing research in two key ways. First, our evaluation is based on Solidity v0.8, offering the most up-to-date insights compared to prior studies that focus on older versions (v0.4). Second, we leverage the latest five LLM models (across companies), ensuring comprehensive coverage across the most advanced capabilities in the field. We conducted a series of rigorous evaluations. Our experiments demonstrate that a well-designed prompt can reduce the false-positive rate by over 60%. Surprisingly, we also discovered that the recall rate for detecting some specific vulnerabilities in Solidity v0.8 has dropped to just 13% compared to earlier versions (i.e., v0.4). Further analysis reveals the root cause of this decline: the reliance of LLMs on identifying changes in newly introduced libraries and frameworks during detection.
Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning
Venue: arXiv cs.IR 2025
Year: 2025

In the contemporary context of rapid advancements in information technology and the exponential growth of data volume, language models are confronted with significant challenges in effectively navigating the dynamic and ever-evolving information landscape to update and adapt to novel knowledge in real time. In this work, an online update method is proposed, which is based on the existing Retrieval Enhanced Generation (RAG) model with multiple innovation mechanisms. Firstly, the dynamic memory is used to capture the emerging data samples, and then gradually integrate them into the core model through a tunable knowledge distillation strategy. At the same time, hierarchical indexing and multi-layer gating mechanism are introduced into the retrieval module to ensure that the retrieved content is more targeted and accurate. Finally, a multi-stage network structure is established for different types of inputs in the generation stage, and cross-attention matching and screening are carried out on the intermediate representations of each stage to ensure the effective integration and iterative update of new and old knowledge. Experimental results show that the proposed method is better than the existing mainstream comparison models in terms of knowledge retention and inference accuracy.
The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering
Venue: arXiv cs.CV 2025
Year: 2025

Visual Question Answering (VQA) is an interdisciplinary field that bridges the gap between computer vision (CV) and natural language processing(NLP), enabling Artificial Intelligence(AI) systems to answer questions about images. Since its inception in 2015, VQA has rapidly evolved, driven by advances in deep learning, attention mechanisms, and transformer-based models. This survey traces the journey of VQA from its early days, through major breakthroughs, such as attention mechanisms, compositional reasoning, and the rise of vision-language pre-training methods. We highlight key models, datasets, and techniques that shaped the development of VQA systems, emphasizing the pivotal role of transformer architectures and multimodal pre-training in driving recent progress. Additionally, we explore specialized applications of VQA in domains like healthcare and discuss ongoing challenges, such as dataset bias, model interpretability, and the need for common-sense reasoning. Lastly, we discuss the emerging trends in large multimodal language models and the integration of external knowledge, offering insights into the future directions of VQA. This paper aims to provide a comprehensive overview of the evolution of VQA, highlighting both its current state and potential advancements.
Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video Recommendation
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal information (e.g., visual, acoustic, and textual) has been widely used to enhance representation learning for micro-video recommendation. For integrating multimodal information into a joint representation of micro-video, multimodal fusion plays a vital role in the existing micro-video recommendation approaches. However, the static multimodal fusion used in previous studies is insufficient to model the various relationships among multimodal information of different micro-videos. In this paper, we develop a novel meta-learning-based multimodal fusion framework called Meta Multimodal Fusion (MetaMMF), which dynamically assigns parameters to the multimodal fusion function for each micro-video during its representation learning. Specifically, MetaMMF regards the multimodal fusion of each micro-video as an independent task. Based on the meta information extracted from the multimodal features of the input task, MetaMMF parameterizes a neural network as the item-specific fusion function via a meta learner. We perform extensive experiments on three benchmark datasets, demonstrating the significant improvements over several state-of-the-art multimodal recommendation models, like MMGCN, LATTICE, and InvRL. Furthermore, we lighten our model by adopting canonical polyadic decomposition to improve the training efficiency, and validate its effectiveness through experimental results. Codes are available at https://github.com/hanliu95/MetaMMF.
The Value of Battery Energy Storage in the Continuous Intraday Market: Forecast vs. Perfect Foresight Strategies
Venue: arXiv cs.CE 2025
Year: 2025

Grid-scale battery energy storage systems (BESSs) can provide flexibility to the power system and capture shortterm price volatility by shifting energy in time through controlled charging and discharging. The highly volatile European continuous intraday (CID) market allows trading until just a few minutes before physical delivery, offering significant earning potential. However, its high trading frequency poses substantial modeling challenges. Accurate modeling of BESSs trading in the CID market is essential to estimate revenue potential and optimize trading strategies. Additionally, comparing CID profits with other spot markets helps determine whether participating in the CID is worthwhile despite its complexity. We propose a forecast-driven model to optimize BESS trading in the CID market. Our strategy employs a rolling window modeling framework to capture market dynamics. Price forecasts for impending CID products are generated at the beginning of each window and used to optimize trading schedules for subsequent execution. We also benchmark our approach across various spot markets, offering a broad cross-market profit comparison. We evaluate our forecast-driven model across different BESS power-to-capacity ratios, comparing it to a perfect-foresight scenario and key CID market indices, such as ID1 and ID3. Using real 2023 German CID data, a 1 MW/1 MWh system adopting our method earns EUR 146 237, only 11% below perfect foresight, surpassing all other markets and indices. Our approach surpasses ID1 and ID3 by over 4% and 32%, respectively, confirming ID1 as a reliable lower-bound estimate for earnings potential in the CID market.
CureGraph: Contrastive Multi-Modal Graph Representation Learning for Urban Living Circle Health Profiling and Prediction
Venue: arXiv cs.AI 2025
Year: 2025

The early detection and prediction of health status decline among the elderly at the neighborhood level are of great significance for urban planning and public health policymaking. While existing studies affirm the connection between living environments and health outcomes, most rely on single data modalities or simplistic feature concatenation of multi-modal information, limiting their ability to comprehensively profile the health-oriented urban environments. To fill this gap, we propose CureGraph, a contrastive multi-modal representation learning framework for urban health prediction that employs graph-based techniques to infer the prevalence of common chronic diseases among the elderly within the urban living circles of each neighborhood. CureGraph leverages rich multi-modal information, including photos and textual reviews of residential areas and their surrounding points of interest, to generate urban neighborhood embeddings. By integrating pre-trained visual and textual encoders with graph modeling techniques, CureGraph captures cross-modal spatial dependencies, offering a comprehensive understanding of urban environments tailored to elderly health considerations. Extensive experiments on real-world datasets demonstrate that CureGraph improves the best baseline by $28\%$ on average in terms of $R^2$ across elderly disease risk prediction tasks. Moreover, the model enables the identification of stage-wise chronic disease progression and supports comparative public health analysis across neighborhoods, offering actionable insights for sustainable urban development and enhanced quality of life. The code is publicly available at https://github.com/jinlin2021/CureGraph.