OpenRaiser/Intern-Atlas huggingface dataset:
Neuromorphic spatiotemporal optical flow: Enabling ultrafast visual perception beyond human capabilities
Venue: arXiv cs.CV 2025
Year: 2025

Optical flow, inspired by the mechanisms of biological visual systems, calculates spatial motion vectors within visual scenes that are necessary for enabling robotics to excel in complex and dynamic working environments. However, current optical flow algorithms, despite human-competitive task performance on benchmark datasets, remain constrained by unacceptable time delays (~0.6 seconds per inference, 4X human processing speed) in practical deployment. Here, we introduce a neuromorphic optical flow approach that addresses delay bottlenecks by encoding temporal information directly in a synaptic transistor array to assist spatial motion analysis. Compared to conventional spatial-only optical flow methods, our spatiotemporal neuromorphic optical flow offers the spatial-temporal consistency of motion information, rapidly identifying regions of interest in as little as 1-2 ms using the temporal motion cues derived from the embedded temporal information in the two-dimensional floating gate synaptic transistors. Thus, the visual input can be selectively filtered to achieve faster velocity calculations and various task execution. At the hardware level, due to the atomically sharp interfaces between distinct functional layers in two-dimensional van der Waals heterostructures, the synaptic transistor offers high-frequency response (~100 μs), robust non-volatility (>10000 s), and excellent endurance (>8000 cycles), enabling robust visual processing. In software benchmarks, our system outperforms state-of-the-art algorithms with a 400% speedup, frequently surpassing human-level performance while maintaining or enhancing accuracy by utilizing the temporal priors provided by the embedded temporal information.
Kalahi: A handcrafted, grassroots cultural LLM evaluation suite for Filipino
Venue: arXiv cs.CL 2025
Year: 2025

Multilingual large language models (LLMs) today may not necessarily provide culturally appropriate and relevant responses to its Filipino users. We introduce Kalahi, a cultural LLM evaluation suite collaboratively created by native Filipino speakers. It is composed of 150 high-quality, handcrafted and nuanced prompts that test LLMs for generations that are relevant to shared Filipino cultural knowledge and values. Strong LLM performance in Kalahi indicates a model's ability to generate responses similar to what an average Filipino would say or do in a given situation. We conducted experiments on LLMs with multilingual and Filipino language support. Results show that Kalahi, while trivial for Filipinos, is challenging for LLMs, with the best model answering only 46.0% of the questions correctly compared to native Filipino performance of 89.10%. Thus, Kalahi can be used to accurately and reliably evaluate Filipino cultural representation in LLMs.
Renaming in distributed certification
Venue: arXiv cs.DC 2025
Year: 2025

Local certification is the area of distributed network computing asking the following question: How to certify to the nodes of a network that a global property holds, if they are limited to a local verification? In this area, it is often essential to have identifiers, that is, unique integers assigned to the nodes. In this short paper, we show how to reduce the range of the identifiers, in three different settings. More precisely, we show how to rename identifiers in the classical local certification setting, when we can (resp.\ cannot) choose the new identifiers, and we show how a global certificate can help to encode very compactly a new identifier assignment that is not injective in general, but still useful in applications. We conclude with a number of applications of these results: For every $\ell$, there are local certification schemes for the properties of having clique number at most $\ell$, having diameter at most $\ell$, and having independence number at most~2, with certificates of size $O(n)$. We also show that there is a global certification scheme for bipartiteness with a certificate of size $O(n)$. All these results are optimal.
Simplifying Textured Triangle Meshes in the Wild
Venue: arXiv cs.GR 2025
Year: 2025

This paper introduces a method for simplifying textured surface triangle meshes in the wild while maintaining high visual quality. While previous methods achieve excellent results on manifold meshes by using the quadric error metric, they struggle to produce high-quality outputs for meshes in the wild, which typically contain non-manifold elements and multiple connected components. In this work, we propose a method for simplifying these wild textured triangle meshes. We formulate mesh simplification as a problem of decimating simplicial 2-complexes to handle multiple non-manifold mesh components collectively. Building on the success of quadric error simplification, we iteratively collapse 1-simplices (vertex pairs). Our approach employs a modified quadric error that converges to the original quadric error metric for watertight manifold meshes, while significantly improving the results on wild meshes. For textures, instead of following existing strategies to preserve UVs, we adopt a novel perspective which focuses on computing mesh correspondences throughout the decimation, independent of the UV layout. This combination yields a textured mesh simplification system that is capable of handling arbitrary triangle meshes, achieving to high-quality results on wild inputs without sacrificing the excellent performance on clean inputs. Our method guarantees to avoid common problems in textured mesh simplification, including the prevalent problem of texture bleeding. We extensively evaluate our method on multiple datasets, showing improvements over prior techniques through qualitative, quantitative, and user study evaluations.
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Venue: arXiv cs.LG 2025
Year: 2025

Learning performance data describe correct and incorrect answers or problem-solving attempts in adaptive learning, such as in intelligent tutoring systems (ITSs). Learning performance data tend to be highly sparse (80\%\(\sim\)90\% missing observations) in most real-world applications due to adaptive item selection. This data sparsity presents challenges to using learner models to effectively predict future performance explore new hypotheses about learning. This article proposes a systematic framework for augmenting learner data to address data sparsity in learning performance data. First, learning performance is represented as a three-dimensional tensor of learners' questions, answers, and attempts, capturing longitudinal knowledge states during learning. Second, a tensor factorization method is used to impute missing values in sparse tensors of collected learner data, thereby grounding the imputation on knowledge tracing tasks that predict missing performance values based on real observations. Third, a module for generating patterns of learning is used. This study contrasts two forms of generative Artificial Intelligence (AI), including Generative Adversarial Networks (GANs) and Generate Pre-Trained Transformers (GPT) to generate data associated with different clusters of learner data. We tested this approach on an adult literacy dataset from AutoTutor lessons developed for Adult Reading Comprehension (ARC). We found that: (1) tensor factorization improved the performance in tracing and predicting knowledge mastery compared with other knowledge tracing techniques without data augmentation, showing higher relative fidelity for this imputation method, and (2) the GAN-based simulation showed greater overall stability and less statistical bias based on a divergence evaluation with varying simulation sample sizes compared to GPT.
Enhancing IoT based Plant Health Monitoring through Advanced Human Plant Interaction using Large Language Models and Mobile Applications
Venue: arXiv cs.AI 2025
Year: 2025

This paper presents the development of a novel plant communication application that allows plants to "talk" to humans using real-time sensor data and AI-powered language models. Utilizing soil sensors that track moisture, temperature, and nutrient levels, the system feeds this data into the Gemini API, where it is processed and transformed into natural language insights about the plant's health and "mood." Developed using Flutter, Firebase, and ThingSpeak, the app offers a seamless user experience with real-time interaction capabilities. By fostering human-plant connectivity, this system enhances plant care practices, promotes sustainability, and introduces innovative applications for AI and IoT technologies in both personal and agricultural contexts. The paper explores the technical architecture, system integration, and broader implications of AI-driven plant communication.
Vulnerabilities that arise from poor governance in Distributed Ledger Technologies
Venue: arXiv cs.CR 2025
Year: 2025

Distributed Ledger Technologies (DLTs) promise decentralization, transparency, and security, yet the reality often falls short due to fundamental governance flaws. Poorly designed governance frameworks leave these systems vulnerable to coercion, vote-buying, centralization of power, and malicious protocol exploits: threats that undermine the very principles of fairness and equity these technologies seek to uphold. This paper surveys the state of DLT governance, identifies critical vulnerabilities, and highlights the absence of universally accepted best practices for good governance. By bridging insights from cryptography, social choice theory, and e-voting systems, we not only present a comprehensive taxonomy of governance properties essential for safeguarding DLTs but also point to technical solutions that can deliver these properties in practice. This work underscores the urgent need for robust, transparent, and enforceable governance mechanisms. Ensuring good governance is not merely a technical necessity but a societal imperative to protect the public interest, maintain trust, and realize the transformative potential of DLTs for social good.
Artificial Human Intelligence: The role of Humans in the Development of Next Generation AI
Venue: arXiv cs.AI 2025
Year: 2025

Human intelligence, the most evident and accessible form of source of reasoning, hosted by biological hardware, has evolved and been refined over thousands of years, positioning itself today to create new artificial forms and preparing to self--design their evolutionary path forward. Beginning with the advent of foundation models, the rate at which human and artificial intelligence interact with each other has exceeded any anticipated quantitative figures. The close engagement led both bits of intelligence to be impacted in various ways, which naturally resulted in complex confluences that warrant close scrutiny. Recent advances, such as DeepSeek, exemplify this interplay: the novel contributions, we argue, draw indirect inspiration from biological principles like modular neural specialization and sparse episodic encoding, addressing computational bottlenecks while aligning with human-inspired scalability. In the sequel, using a novel taxonomy, we shall explore this interplay between human and machine intelligence, focusing on the crucial role humans play in developing ethical, responsible, and robust intelligent systems. We briefly delve into various aspects of implementation inspired by the mechanisms underlying neuroscience and human cognition. In addition, we propose future perspectives, capitalizing on the advantages of symbiotic designs to suggest a human-centered direction for next-generation developments, focusing on the augmentation role of AI. We finalize this evolving document with some thoughts and open questions yet to be addressed by the broader community.
Joint Signal and Topology Optimization for Maximum Instantaneous Field Intensity
Venue: arXiv cs.IT 2025
Year: 2025

This paper introduces a computational approach to identify performance constraints in the time-domain, offering a way to design systems in pulse operation. This work presents a comprehensive application of convex optimization to determine fundamental bounds on time-domain waveforms. The approach is applied to arbitrarily polarized multiport antennas and arrays, demonstrating their capability in maximizing peak radiation intensity in a specified direction and time under energy constraints. This methodology allows us to consider matching, which is crucial in such applications. To highlight the generality of the approach, receiving systems are also studied on an example of maximizing the local field for antiferromagnetic memory switching. Thanks to its efficacy, this work enables joint optimization of excitation and system parameters.
Beyond Text-to-Text: An Overview of Multimodal and Generative Artificial Intelligence for Education Using Topic Modeling
Venue: arXiv cs.AI 2025
Year: 2025

Generative artificial intelligence (GenAI) can reshape education and learning. While large language models (LLMs) like ChatGPT dominate current educational research, multimodal capabilities, such as text-to-speech and text-to-image, are less explored. This study uses topic modeling to map the research landscape of multimodal and generative AI in education. An extensive literature search using Dimensions yielded 4175 articles. Employing a topic modeling approach, latent topics were extracted, resulting in 38 interpretable topics organized into 14 thematic areas. Findings indicate a predominant focus on text-to-text models in educational contexts, with other modalities underexplored, overlooking the broader potential of multimodal approaches. The results suggest a research gap, stressing the importance of more balanced attention across different AI modalities and educational levels. In summary, this research provides an overview of current trends in generative AI for education, underlining opportunities for future exploration of multimodal technologies to fully realize the transformative potential of artificial intelligence in education.
Touch to Pair: Secure and Usable IoT Pairing without Information Loss
Venue: arXiv cs.CR 2025
Year: 2025

Secure pairing is essential for trustworthy deployment and operation of Internet of Things (IoT) devices. However, traditional pairing methods are unsuitable due to the lack of user interfaces such as keyboards. Proximity-based approaches are usable but vulnerable to nearby attackers, while methods relying on physical operations (e.g., shaking) offer higher security but require inertial sensors that most IoT devices lack. We introduceUniversal Operation Sensing, which enables IoT devices to detect user operations without inertial sensors. With this technique, users can complete pairing within seconds through simple actions, such as pressing a button or twisting a knob, using either a smartphone or a smartwatch. We further identify an accuracy issue caused by information loss in the commonly used fuzzy-commitment protocol. To address this issue, we propose TimeWall, an accurate pairing protocol that avoids fuzzy commitment and incurs zero information loss. A comprehensive evaluation shows that it is secure, usable, and efficient.
GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning
Venue: arXiv cs.LG 2025
Year: 2025

Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in handling a range of graph analytical tasks across various domains, such as e-commerce and social networks. Despite their versatility, GNNs face significant challenges in transferability, limiting their utility in real-world applications. Existing research in GNN transfer learning overlooks discrepancies in distribution among various graph datasets, facing challenges when transferring across different distributions. How to effectively adopt a well-trained GNN to new graphs with varying feature and structural distributions remains an under-explored problem. Taking inspiration from the success of Low-Rank Adaptation (LoRA) in adapting large language models to various domains, we propose GraphLoRA, an effective and parameter-efficient method for transferring well-trained GNNs to diverse graph domains. Specifically, we first propose a Structure-aware Maximum Mean Discrepancy (SMMD) to align divergent node feature distributions across source and target graphs. Moreover, we introduce low-rank adaptation by injecting a small trainable GNN alongside the pre-trained one, effectively bridging structural distribution gaps while mitigating the catastrophic forgetting. Additionally, a structure-aware regularization objective is proposed to enhance the adaptability of the pre-trained GNN to target graph with scarce supervision labels. Extensive experiments on eight real-world datasets demonstrate the effectiveness of GraphLoRA against fourteen baselines by tuning only 20% of parameters, even across disparate graph domains. The code is available at https://github.com/AllminerLab/GraphLoRA.
Vulnerability-Triggering Test Case Generation from Third-Party Libraries
Venue: arXiv cs.SE 2025
Year: 2025

Open-source third-party libraries are widely used in software development. These libraries offer substantial advantages in terms of time and resource savings. However, a significant concern arises due to the publicly disclosed vulnerabilities within these libraries. Existing automated vulnerability detection tools often suffer from false positives and fail to accurately assess the propagation of inputs capable of triggering vulnerabilities from client projects to vulnerable code in libraries. In this paper, we propose a novel approach called VULEUT (Vulnerability Exploit Unit Test Generation), which combines vulnerability exploitation reachability analysis and LLM-based unit test generation. VULEUT is designed to automatically verify the exploitability of vulnerabilities in third-party libraries commonly used in client software projects. VULEUT first analyzes the client projects to determine the reachability of vulnerability conditions. And then, it leverages the Large Language Model (LLM) to generate unit tests for vulnerability confirmation. To evaluate the effectiveness of VULEUT, we collect 32 vulnerabilities from various third-party libraries and conduct experiments on 70 real client projects. Besides, we also compare our approach with two representative tools, i.e., TRANSFER and VESTA. Our results demonstrate the effectiveness of VULEUT, with 229 out of 292 generated unit tests successfully confirming vulnerability exploitation across 70 client projects, which outperforms baselines by 24%.
Symbolic State Partitioning for Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Tabular reinforcement learning methods cannot operate directly on continuous state spaces. One solution for this problem is to partition the state space. A good partitioning enables generalization during learning and more efficient exploitation of prior experiences. Consequently, the learning process becomes faster and produces more reliable policies. However, partitioning introduces approximation, which is particularly harmful in the presence of nonlinear relations between state components. An ideal partition should be as coarse as possible, while capturing the key structure of the state space for the given problem. This work extracts partitions from the environment dynamics by symbolic execution. We show that symbolic partitioning improves state space coverage with respect to environmental behavior and allows reinforcement learning to perform better for sparse rewards. We evaluate symbolic state space partitioning with respect to precision, scalability, learning agent performance and state space coverage for the learnt policies.
Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data
Venue: arXiv cs.CV 2025
Year: 2025

In industries such as healthcare, finance, and manufacturing, analysis of unstructured textual data presents significant challenges for analysis and decision making. Uncovering patterns within large-scale corpora and understanding their semantic impact is critical, but depends on domain experts or resource-intensive manual reviews. In response, we introduce Spacewalker in this system demonstration paper, an interactive tool designed to analyze, explore, and annotate data across multiple modalities. It allows users to extract data representations, visualize them in low-dimensional spaces and traverse large datasets either exploratory or by querying regions of interest. We evaluated Spacewalker through extensive experiments and annotation studies, assessing its efficacy in improving data integrity verification and annotation. We show that Spacewalker reduces time and effort compared to traditional methods. The code of this work is open-source and can be found at: https://github.com/code-lukas/Spacewalker
Risk-averse learning with delayed feedback
Venue: arXiv cs.LG 2025
Year: 2025

In real-world scenarios, risk-averse learning is valuable for mitigating potential adverse outcomes. However, the delayed feedback makes it challenging to assess and manage risk effectively. In this paper, we investigate risk-averse learning using Conditional Value at Risk (CVaR) as risk measure, while incorporating feedback with random but bounded delays. We develop two risk-averse learning algorithms that rely on one-point and two-point zeroth-order optimization approaches, respectively. The dynamic regrets of the algorithms are analyzed in terms of the cumulative delay and the number of total samplings. In the absence of delay, the regret bounds match the established bounds of zeroth-order stochastic gradient methods for risk-averse learning. Furthermore, the two-point risk-averse learning outperforms the one-point algorithm by achieving a smaller regret bound. We provide numerical experiments on a dynamic pricing problem to demonstrate the performance of the algorithms.
Underwater Camouflaged Object Tracking Meets Vision-Language SAM2
Venue: arXiv cs.CV 2025
Year: 2025

Over the past decade, significant progress has been made in visual object tracking, largely due to the availability of large-scale datasets. However, these datasets have primarily focused on open-air scenarios and have largely overlooked underwater animal tracking-especially the complex challenges posed by camouflaged marine animals. To bridge this gap, we take a step forward by proposing the first large-scale multi-modal underwater camouflaged object tracking dataset, namely UW-COT220. Based on the proposed dataset, this work first comprehensively evaluates current advanced visual object tracking methods, including SAM- and SAM2-based trackers, in challenging underwater environments, \eg, coral reefs. Our findings highlight the improvements of SAM2 over SAM, demonstrating its enhanced ability to handle the complexities of underwater camouflaged objects. Furthermore, we propose a novel vision-language tracking framework called VL-SAM2, based on the video foundation model SAM2. Extensive experimental results demonstrate that the proposed VL-SAM2 achieves state-of-the-art performance across underwater and open-air object tracking datasets. The dataset and codes are available at~{\color{magenta}{https://github.com/983632847/Awesome-Multimodal-Object-Tracking}}.