OpenRaiser/Intern-Atlas huggingface dataset:
CBIL: Collective Behavior Imitation Learning for Fish from Real Videos
Venue: arXiv cs.GR 2025
Year: 2025

Reproducing realistic collective behaviors presents a captivating yet formidable challenge. Traditional rule-based methods rely on hand-crafted principles, limiting motion diversity and realism in generated collective behaviors. Recent imitation learning methods learn from data but often require ground truth motion trajectories and struggle with authenticity, especially in high-density groups with erratic movements. In this paper, we present a scalable approach, Collective Behavior Imitation Learning (CBIL), for learning fish schooling behavior directly from videos, without relying on captured motion trajectories. Our method first leverages Video Representation Learning, where a Masked Video AutoEncoder (MVAE) extracts implicit states from video inputs in a self-supervised manner. The MVAE effectively maps 2D observations to implicit states that are compact and expressive for following the imitation learning stage. Then, we propose a novel adversarial imitation learning method to effectively capture complex movements of the schools of fish, allowing for efficient imitation of the distribution for motion patterns measured in the latent space. It also incorporates bio-inspired rewards alongside priors to regularize and stabilize training. Once trained, CBIL can be used for various animation tasks with the learned collective motion priors. We further show its effectiveness across different species. Finally, we demonstrate the application of our system in detecting abnormal fish behavior from in-the-wild videos.
NeRF-Based defect detection
Venue: arXiv cs.CV 2025
Year: 2025

The rapid growth of industrial automation has highlighted the need for precise and efficient defect detection in large-scale machinery. Traditional inspection techniques, involving manual procedures such as scaling tall structures for visual evaluation, are labor-intensive, subjective, and often hazardous. To overcome these challenges, this paper introduces an automated defect detection framework built on Neural Radiance Fields (NeRF) and the concept of digital twins. The system utilizes UAVs to capture images and reconstruct 3D models of machinery, producing both a standard reference model and a current-state model for comparison. Alignment of the models is achieved through the Iterative Closest Point (ICP) algorithm, enabling precise point cloud analysis to detect deviations that signify potential defects. By eliminating manual inspection, this method improves accuracy, enhances operational safety, and offers a scalable solution for defect detection. The proposed approach demonstrates great promise for reliable and efficient industrial applications.
Text Chunking for Document Classification for Urban System Management using Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Urban systems are managed using complex textual documentation that need coding and analysis to set requirements and evaluate built environment performance. This paper contributes to the study of applying large-language models (LLM) to qualitative coding activities to reduce resource requirements while maintaining comparable reliability to humans. Qualitative coding and assessment face challenges like resource limitations and bias, accuracy, and consistency between human evaluators. Here we report the application of LLMs to deductively code 10 case documents on the presence of 17 digital twin characteristics for the management of urban systems. We utilize two prompting methods to compare the semantic processing of LLMs with human coding efforts: whole text analysis and text chunk analysis using OpenAI's GPT-4o, GPT-4o-mini, and o1-mini models. We found similar trends of internal variability between methods and results indicate that LLMs may perform on par with human coders when initialized with specific deductive coding contexts. GPT-4o, o1-mini and GPT-4o-mini showed significant agreement with human raters when employed using a chunking method. The application of both GPT-4o and GPT-4o-mini as an additional rater with three manual raters showed statistically significant agreement across all raters, indicating that the analysis of textual documents is benefited by LLMs. Our findings reveal nuanced sub-themes of LLM application suggesting LLMs follow human memory coding processes where whole-text analysis may introduce multiple meanings. The novel contributions of this paper lie in assessing the performance of OpenAI GPT models and introduces the chunk-based prompting approach, which addresses context aggregation biases by preserving localized context.
Do Chinese models speak Chinese languages?
Venue: arXiv cs.CL 2025
Year: 2025

The release of top-performing open-weight LLMs has cemented China's role as a leading force in AI development. Do these models support languages spoken in China? Or do they speak the same languages as Western models? Comparing multilingual capabilities is important for two reasons. First, language ability provides insights into pre-training data curation, and thus into resource allocation and development priorities. Second, China has a long history of explicit language policy, varying between inclusivity of minority languages and a Mandarin-first policy. To test whether Chinese LLMs today reflect an agenda about China's languages, we test performance of Chinese and Western open-source LLMs on Asian regional and Chinese minority languages. Our experiments on Information Parity and reading comprehension show Chinese models' performance across these languages correlates strongly (r=0.93) with Western models', with the sole exception being better Mandarin. Sometimes, Chinese models cannot identify languages spoken by Chinese minorities such as Kazakh and Uyghur, even though they are good at French and German. These results provide a window into current development priorities, suggest options for future development, and indicate guidance for end users.
Demystifying CO2: lessons from nutrition labeling and step counting
Venue: arXiv cs.HC 2025
Year: 2025

There is growing concern about climate change and increased interest in taking action. However, people have difficulty understanding abstract units like CO2 and the relative environmental impact of different behaviors. This position piece explores findings from nutritional labeling and step counting research, two domains aimed at making abstract concepts (i.e., calories and exercise) more familiar to the general public. Research in these two domains suggests that consistent, widespread communication can make people more familiar and think more precisely about abstract units, but that better communication and understanding does not guarantee behavior change. These findings suggest that consistent and ubiquitous communication can make CO2 units more familiar to people, which in turn could help interventions aimed at encouraging more sustainable behaviors.
Transductive One-Shot Learning Meet Subspace Decomposition
Venue: arXiv cs.CV 2025
Year: 2025

One-shot learning focuses on adapting pretrained models to recognize newly introduced and unseen classes based on a single labeled image. While variations of few-shot and zero-shot learning exist, one-shot learning remains a challenging yet crucial problem due to its ability to generalize knowledge to unseen classes from just one human-annotated image. In this paper, we introduce a transductive one-shot learning approach that employs subspace decomposition to utilize the information from labeled images in the support set and unlabeled images in the query set. These images are decomposed into a linear combination of latent variables representing primitives captured by smaller subspaces. By representing images in the query set as linear combinations of these latent primitives, we can propagate the label from a single image in the support set to query images that share similar combinations of primitives. Through a comprehensive quantitative analysis across various neural network feature extractors and datasets, we demonstrate that our approach can effectively generalize to novel classes from just one labeled image.
Computer-based Deceptive Game Design in Commercial Virtual Reality Games: A Preliminary Investigation
Venue: arXiv cs.HC 2025
Year: 2025

As Virtual Reality (VR) games become more popular, it is crucial to understand how deceptive game design patterns manifest and impact player experiences in this emerging medium. Our study sheds light on the presence and effects of manipulative design techniques in commercial VR games compared to a traditional computer game. We conducted an autoethnography study and developed a VR Deceptive Game Design Assessment Guide based on a critical literature review. Using our guide, we compared how deceptive patterns in a popular computer game are different from two commercial VR titles. While VR's technological constraints, such as battery life and limited temporal manipulation, VR's unique sensory immersion amplified the impact of emotional and sensory deception. Current VR games showed similar but evolved forms of deceptive design compared to the computer game. We forecast more sophisticated player manipulation as VR technology advances. Our findings contribute to a better understanding of how deceptive game design persists and escalates in VR. We highlight the urgent need to develop ethical design guidelines for the rapidly advancing VR games industry.
Are you really listening? Boosting Perceptual Awareness in Music-QA Benchmarks
Venue: arXiv cs.SD 2025
Year: 2025

Large Audio Language Models (LALMs), where pretrained text LLMs are finetuned with audio input, have made remarkable progress in music understanding. However, current evaluation methodologies exhibit critical limitations: on the leading Music Question Answering benchmark, MuchoMusic, text-only LLMs without audio perception capabilities achieve surprisingly high accuracy of up to 56.4%, on par or above most LALMs. Furthermore, when presented with random Gaussian noise instead of actual audio, LALMs still perform significantly above chance. These findings suggest existing benchmarks predominantly assess reasoning abilities rather than audio perception. To overcome this challenge, we present RUListening: Robust Understanding through Listening, a framework that enhances perceptual evaluation in Music-QA benchmarks. We introduce the Perceptual Index (PI), a quantitative metric that measures a question's reliance on audio perception by analyzing log probability distributions from text-only language models. Using this metric, we generate synthetic, challenging distractors to create QA pairs that necessitate genuine audio perception. When applied to MuchoMusic, our filtered dataset successfully forces models to rely on perceptual information-text-only LLMs perform at chance levels, while LALMs similarly deteriorate when audio inputs are replaced with noise. These results validate our framework's effectiveness in creating benchmarks that more accurately evaluate audio perception capabilities.
Spatiotemporal Attention Learning Framework for Event-Driven Object Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Event-based vision sensors, inspired by biological neural systems, asynchronously capture local pixel-level intensity changes as a sparse event stream containing position, polarity, and timestamp information. These neuromorphic sensors offer significant advantages in dynamic range, latency, and power efficiency. Their working principle inherently addresses traditional camera limitations such as motion blur and redundant background information, making them particularly suitable for dynamic vision tasks. While recent works have proposed increasingly complex event-based architectures, the computational overhead and parameter complexity of these approaches limit their practical deployment. This paper presents a novel spatiotemporal learning framework for event-based object recognition, utilizing a VGG network enhanced with Convolutional Block Attention Module (CBAM). Our approach achieves comparable performance to state-of-the-art ResNet-based methods while reducing parameter count by 2.3% compared to the original VGG model. Specifically, it outperforms ResNet-based methods like MVF-Net, achieving the highest Top-1 accuracy of 76.4% (pretrained) and 71.3% (not pretrained) on CIFAR10-DVS, and 72.4% (not pretrained) on N-Caltech101. These results highlight the robustness of our method when pretrained weights are not used, making it suitable for scenarios where transfer learning is unavailable. Moreover, our approach reduces reliance on data augmentation. Experimental results on standard event-based datasets demonstrate the framework's efficiency and effectiveness for real-world applications.
When Persuasion Overrides Truth in Multi-Agent LLM Debates: Introducing a Confidence-Weighted Persuasion Override Rate (CW-POR)
Venue: arXiv cs.CL 2025
Year: 2025

In many real-world scenarios, a single Large Language Model (LLM) may encounter contradictory claims-some accurate, others forcefully incorrect-and must judge which is true. We investigate this risk in a single-turn, multi-agent debate framework: one LLM-based agent provides a factual answer from TruthfulQA, another vigorously defends a falsehood, and the same LLM architecture serves as judge. We introduce the Confidence-Weighted Persuasion Override Rate (CW-POR), which captures not only how often the judge is deceived but also how strongly it believes the incorrect choice. Our experiments on five open-source LLMs (3B-14B parameters), where we systematically vary agent verbosity (30-300 words), reveal that even smaller models can craft persuasive arguments that override truthful answers-often with high confidence. These findings underscore the importance of robust calibration and adversarial testing to prevent LLMs from confidently endorsing misinformation.
CamoSAM2: Motion-Appearance Induced Auto-Refining Prompts for Video Camouflaged Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

The Segment Anything Model 2 (SAM2), a prompt-guided video foundation model, has remarkably performed in video object segmentation, drawing significant attention in the community. Due to the high similarity between camouflaged objects and their surroundings, which makes them difficult to distinguish even by the human eye, the application of SAM2 for automated segmentation in real-world scenarios faces challenges in camouflage perception and reliable prompts generation. To address these issues, we propose CamoSAM2, a motion-appearance prompt inducer (MAPI) and refinement framework to automatically generate and refine prompts for SAM2, enabling high-quality automatic detection and segmentation in VCOD task. Initially, we introduce a prompt inducer that simultaneously integrates motion and appearance cues to detect camouflaged objects, delivering more accurate initial predictions than existing methods. Subsequently, we propose a video-based adaptive multi-prompts refinement (AMPR) strategy tailored for SAM2, aimed at mitigating prompt error in initial coarse masks and further producing good prompts. Specifically, we introduce a novel three-step process to generate reliable prompts by camouflaged object determination, pivotal prompting frame selection, and multi-prompts formation. Extensive experiments conducted on two benchmark datasets demonstrate that our proposed model, CamoSAM2, significantly outperforms existing state-of-the-art methods, achieving increases of 8.0% and 10.1% in mIoU metric. Additionally, our method achieves the fastest inference speed compared to current VCOD models.
AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments
Venue: arXiv cs.DC 2025
Year: 2025

Edge computing facilitates deep learning in resource-constrained environments, but challenges such as resource heterogeneity and dynamic constraints persist. This paper introduces AMP4EC, an Adaptive Model Partitioning framework designed to optimize deep learning inference in edge environments through real-time resource monitoring, dynamic model partitioning, and adaptive task scheduling. AMP4EC features a resource-aware model partitioner that splits deep learning models based on device capabilities, a task scheduler that ensures efficient load balancing using a weighted scoring mechanism, and a Docker-based deployment environment for validation. Experimental results show up to a 78% reduction in latency and a 414% improvement in throughput compared to baseline methods. The framework achieves consistent performance with low scheduling overhead across varying resource profiles, demonstrating adaptability in high-resource (1 CPU, 1GB RAM) and low-resource (0.4 CPU, 512MB RAM) scenarios. These results highlight AMP4EC's scalability, efficiency, and robustness for real-world edge deployments, addressing the critical need for efficient distributed inference in dynamic, resource-constrained environments.
Form-Substance Discrimination: Concept, Cognition, and Pedagogy
Venue: arXiv cs.HC 2025
Year: 2025

The skill to separate form from substance in writing has gained new prominence in the age of AI-generated content. The challenge - discriminating between fluent expression and substantive thought - constitutes a critical literacy skill for modern education. This paper examines form-substance discrimination (FSD) as an essential learning outcome for curriculum development in higher education. We analyze its cognitive foundations in fluency bias and inhibitory control, trace its evolution from composition theory concepts like "higher-order concerns," and explore how readers progress from novice acceptance of polished text to expert critical assessment. Drawing on research in cognitive psychology, composition studies, and emerging AI pedagogy, we propose practical strategies for fostering this ability through curriculum design, assessment practices, and explicit instruction. By prioritizing substance over surface in writing education, institutions can prepare students to navigate an information landscape where AI-generated content amplifies the ancient tension between style and meaning, ultimately safeguarding the value of authentic human thought in knowledge construction and communication.
LLM-Assisted Proactive Threat Intelligence for Automated Reasoning
Venue: arXiv cs.CR 2025
Year: 2025

Successful defense against dynamically evolving cyber threats requires advanced and sophisticated techniques. This research presents a novel approach to enhance real-time cybersecurity threat detection and response by integrating large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems with continuous threat intelligence feeds. Leveraging recent advancements in LLMs, specifically GPT-4o, and the innovative application of RAG techniques, our approach addresses the limitations of traditional static threat analysis by incorporating dynamic, real-time data sources. We leveraged RAG to get the latest information in real-time for threat intelligence, which is not possible in the existing GPT-4o model. We employ the Patrowl framework to automate the retrieval of diverse cybersecurity threat intelligence feeds, including Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), Exploit Prediction Scoring System (EPSS), and Known Exploited Vulnerabilities (KEV) databases, and integrate these with the all-mpnet-base-v2 model for high-dimensional vector embeddings, stored and queried in Milvus. We demonstrate our system's efficacy through a series of case studies, revealing significant improvements in addressing recently disclosed vulnerabilities, KEVs, and high-EPSS-score CVEs compared to the baseline GPT-4o. This work not only advances the role of LLMs in cybersecurity but also establishes a robust foundation for the development of automated intelligent cyberthreat information management systems, addressing crucial gaps in current cybersecurity practices.
HERA: Hybrid Edge-cloud Resource Allocation for Cost-Efficient AI Agents
Venue: arXiv cs.LG 2025
Year: 2025

In the realm of AI, large language models (LLMs) like GPT-4, central to the operation of AI agents, predominantly operate in the cloud, incurring high operational costs. With local-based small language models (SLMs) becoming more accurate, the necessity of cloud-exclusive processing is being reconsidered. An AI agent's response to a user's request comprises a series of subtasks or iterations. Existing approaches only allocate a single request between SLM and LLM to ensure their outputs are similar, but adopting this approach in the AI agent scenario for assigning each subtask is not effective since SLM will output a different subsequent subtask, which affects the accuracy of the final output. In this paper, we first conduct experimental analysis to understand the features of AI agent operations. Leveraging our findings, we propose the Adaptive Iteration-level Model Selector (AIMS), a lightweight scheduler to automatically partition AI agent's subtasks between local-based SLM and cloud-based LLM. AIMS considers the varying subtask features and strategically decides the location for each subtask in order to use SLM as much as possible while attaining the accuracy level. Our experimental results demonstrate that AIMS increases accuracy by up to 9.1% and SLM usage by up to 10.8% compared to HybridLLM. It offloads 45.67% of subtasks to a local SLM while attaining similar accuracy on average compared with the cloud-only LLM approach.
Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement
Venue: arXiv cs.RO 2025
Year: 2025

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m\times10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.