OpenRaiser/Intern-Atlas huggingface dataset:
Objaverse++: Curated 3D Object Dataset with Quality Annotations
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents Objaverse++, a curated subset of Objaverse enhanced with detailed attribute annotations by human experts. Recent advances in 3D content generation have been driven by large-scale datasets such as Objaverse, which contains over 800,000 3D objects collected from the Internet. Although Objaverse represents the largest available 3D asset collection, its utility is limited by the predominance of low-quality models. To address this limitation, we manually annotate 10,000 3D objects with detailed attributes, including aesthetic quality scores, texture color classifications, multi-object composition flags, transparency characteristics, etc. Then, we trained a neural network capable of annotating the tags for the rest of the Objaverse dataset. Through experiments and a user study on generation results, we demonstrate that models pre-trained on our quality-focused subset achieve better performance than those trained on the larger dataset of Objaverse in image-to-3D generation tasks. In addition, by comparing multiple subsets of training data filtered by our tags, our results show that the higher the data quality, the faster the training loss converges. These findings suggest that careful curation and rich annotation can compensate for the raw dataset size, potentially offering a more efficient path to develop 3D generative models. We release our enhanced dataset of approximately 500,000 curated 3D models to facilitate further research on various downstream tasks in 3D computer vision. In the near future, we aim to extend our annotations to cover the entire Objaverse dataset.
Electronic Warfare Cyberattacks, Countermeasures and Modern Defensive Strategies of UAV Avionics: A Survey
Venue: arXiv cs.CR 2025
Year: 2025

Unmanned Aerial Vehicles (UAVs) play a pivotal role in modern autonomous air mobility, and the reliability of UAV avionics systems is critical to ensuring mission success, sustainability practices, and public safety. The success of UAV missions depends on effectively mitigating various aspects of electronic warfare, including non-destructive and destructive cyberattacks, transponder vulnerabilities, and jamming threats, while rigorously implementing countermeasures and defensive aids. This paper provides a comprehensive review of UAV cyberattacks, countermeasures, and defensive strategies. It explores UAV-to-UAV coordination attacks and their associated features, such as dispatch system attacks, Automatic Dependent Surveillance-Broadcast (ADS-B) attacks, Traffic Alert and Collision Avoidance System (TCAS)-induced collisions, and TCAS attacks. Additionally, the paper examines UAV-to-command center coordination attacks, as well as UAV functionality attacks. The review also covers various countermeasures and defensive aids designed for UAVs. Lastly, a comparison of common cyberattacks and countermeasure approaches is conducted, along with a discussion of future trends in the field. Keywords: Electronic warfare, UAVs, Avionics Systems, cyberattacks, coordination attacks, functionality attacks, countermeasure, defensive-aids.
Model Discrepancy Learning: Synthetic Faces Detection Based on Multi-Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Advances in image generation enable hyper-realistic synthetic faces but also pose risks, thus making synthetic face detection crucial. Previous research focuses on the general differences between generated images and real images, often overlooking the discrepancies among various generative techniques. In this paper, we explore the intrinsic relationship between synthetic images and their corresponding generation technologies. We find that specific images exhibit significant reconstruction discrepancies across different generative methods and that matching generation techniques provide more accurate reconstructions. Based on this insight, we propose a Multi-Reconstruction-based detector. By reversing and reconstructing images using multiple generative models, we analyze the reconstruction differences among real, GAN-generated, and DM-generated images to facilitate effective differentiation. Additionally, we introduce the Asian Synthetic Face Dataset (ASFD), containing synthetic Asian faces generated with various GANs and DMs. This dataset complements existing synthetic face datasets. Experimental results demonstrate that our detector achieves exceptional performance, with strong generalization and robustness.
RLibm-MultiRound: Correctly Rounded Math Libraries Without Worrying about the Application's Rounding Mode
Venue: arXiv cs.MS 2025
Year: 2025

Our RLibm project generates a single implementation for an elementary function that produces correctly rounded results for multiple rounding modes and representations with up to 32-bits. They are appealing for developing fast reference libraries without double rounding issues. The key insight is to build polynomials that produce the correctly rounded result for a representation with two additional bits when compared to the largest target representation and with the "non-standard" round-to-odd rounding mode, which makes double rounding the RLibm math library result to any smaller target representation innocuous. The resulting approximations generated by the RLibm approach are implemented with machine supported floating-point operations with the round-to-nearest rounding mode. When an application uses a rounding mode other than the round-to-nearest mode, the RLibm math library saves the application's rounding mode, changes the system's rounding mode to round-to-nearest, computes the correctly rounded result, and restores the application's rounding mode. This frequent change of rounding modes has a performance cost. This paper proposes two new methods, which we call rounding-invariant outputs and rounding-invariant input bounds, to avoid the frequent changes to the rounding mode and the dependence on the round-to-nearest mode. First, our new rounding-invariant outputs method proposes using the round-to-zero rounding mode to implement RLibm's polynomial approximations. We propose fast, error-free transformations to emulate a round-to-zero result from any standard rounding mode without changing the rounding mode. Second, our rounding-invariant input bounds method factors any rounding error due to different rounding modes using interval bounds in the RLibm pipeline. Both methods make a different set of trade-offs and improve the performance of resulting libraries by more than 2X.
The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses
Venue: arXiv cs.LG 2025
Year: 2025

This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical savings.
Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications, yet current evaluation methods struggle to keep pace with their rapid development. One core challenge of evaluation in the large language model (LLM) era is the generalization issue: how to infer a model's near-unbounded abilities from inevitably bounded benchmarks. We address this challenge by proposing Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores. MUI quantifies the effort a model expends on a task, defined as the proportion of activated neurons or features during inference. Intuitively, a truly capable model should achieve higher performance with lower effort. Extensive experiments across popular LLMs reveal a consistent inverse logarithmic relationship between MUI and performance, which we formulate as the Utility Law. From this law we derive four practical corollaries that (i) guide training diagnostics, (ii) expose data contamination issue, (iii) enable fairer model comparisons, and (iv) design model-specific dataset diversity. Our code can be found at https://github.com/ALEX-nlp/MUI-Eva.
Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts
Venue: arXiv cs.CL 2025
Year: 2025

We propose a novel framework for generating causal graphs from narrative texts, bridging high-level causality and detailed event-specific relationships. Our method first extracts concise, agent-centered vertices using large language model (LLM)-based summarization. We introduce an "Expert Index," comprising seven linguistically informed features, integrated into a Situation-Task-Action-Consequence (STAC) classification model. This hybrid system, combining RoBERTa embeddings with the Expert Index, achieves superior precision in causal link identification compared to pure LLM-based approaches. Finally, a structured five-iteration prompting process refines and constructs connected causal graphs. Experiments on 100 narrative chapters and short stories demonstrate that our approach consistently outperforms GPT-4o and Claude 3.5 in causal graph quality, while maintaining readability. The open-source tool provides an interpretable, efficient solution for capturing nuanced causal chains in narratives.
Multi-Modal Data Fusion for Moisture Content Prediction in Apple Drying
Venue: arXiv cs.LG 2025
Year: 2025

Fruit drying is widely used in food manufacturing to reduce product moisture, ensure product safety, and extend product shelf life. Accurately predicting final moisture content (MC) is critically needed for quality control of drying processes. State-of-the-art methods can build deterministic relationships between process parameters and MC, but cannot adequately account for inherent process variabilities that are ubiquitous in fruit drying. To address this gap, this paper presents a novel multi-modal data fusion framework to effectively fuse two modalities of data: tabular data (process parameters) and high-dimensional image data (images of dried apple slices) to enable accurate MC prediction. The proposed modeling architecture permits flexible adjustment of information portion from tabular and image data modalities. Experimental validation shows that the multi-modal approach improves predictive accuracy substantially compared to state-of-the-art methods. The proposed method reduces root-mean-squared errors by 19.3%, 24.2%, and 15.2% over tabular-only, image-only, and standard tabular-image fusion models, respectively. Furthermore, it is demonstrated that our method is robust in varied tabular-image ratios and capable of effectively capturing inherent small-scale process variabilities. The proposed framework is extensible to a variety of other drying technologies.
Rendering Large Volume Datasets in Unreal Engine 5: A Survey
Venue: arXiv cs.GR 2025
Year: 2025

In this technical report, we discuss several approaches to in-core rendering of large volumetric datasets in Unreal Engine 5 (UE5). We explore the following methods: the TBRayMarcher Plugin, the Niagara Fluids Plugin , and various approaches using Sparse Volume Textures (SVT), with a particular focus on Heterogeneous Volumes (HV). We found the HV approach to be the most promising. The biggest challenge we encountered with other approaches was the need to chunk datasets so that each fits into volume textures smaller than one gigavoxel. While this enables display of the entire dataset at reasonable frame rates, it introduces noticeable artifacts at chunk borders due to incorrect lighting, as each chunk lacks information about its neighbors. After addressing some (signed) int32 overflows in the Engine's SVT-related source code by converting them to to (unsigned) uint32 or int64, the SVT-based HV system allows us to render sparse datasets up to 32k x 32k x 16k voxels, provided the compressed tile data (including MIP data and padding for correct interpolation) does not exceed 4 gigavoxels. In the future, we intend to extend the existing SVT streaming functionality to support out-of-core rendering, in order to eventually overcome VRAM limitations, graphics API constraints, and the performance issues associated with 64-bit arithmetic in GPU shaders.
Drive in Corridors: Enhancing the Safety of End-to-end Autonomous Driving via Corridor Learning and Planning
Venue: arXiv cs.RO 2025
Year: 2025

Safety remains one of the most critical challenges in autonomous driving systems. In recent years, the end-to-end driving has shown great promise in advancing vehicle autonomy in a scalable manner. However, existing approaches often face safety risks due to the lack of explicit behavior constraints. To address this issue, we uncover a new paradigm by introducing the corridor as the intermediate representation. Widely adopted in robotics planning, the corridors represents spatio-temporal obstacle-free zones for the vehicle to traverse. To ensure accurate corridor prediction in diverse traffic scenarios, we develop a comprehensive learning pipeline including data annotation, architecture refinement and loss formulation. The predicted corridor is further integrated as the constraint in a trajectory optimization process. By extending the differentiability of the optimization, we enable the optimized trajectory to be seamlessly trained within the end-to-end learning framework, improving both safety and interpretability. Experimental results on the nuScenes dataset demonstrate state-of-the-art performance of our approach, showing a 66.7% reduction in collisions with agents and a 46.5% reduction with curbs, significantly enhancing the safety of end-to-end driving. Additionally, incorporating the corridor contributes to higher success rates in closed-loop evaluations. Project page: https://zhiwei-pg.github.io/Drive-in-Corridors.
GPT Carry-On: Training Foundation Model for Customization Could Be Simple, Scalable and Affordable
Venue: arXiv cs.LG 2025
Year: 2025

Modern large language foundation models (LLM) have now entered the daily lives of millions of users. We ask a natural question whether it is possible to customize LLM for every user or every task. From system and industrial economy consideration, general continue-training or fine-tuning still require substantial computation and memory of training GPU nodes, whereas most inference nodes under deployment, possibly with lower-end GPUs, are configured to make forward pass fastest possible. We propose a framework to take full advantages of existing LLMs and systems of online service. We train an additional branch of transformer blocks on the final-layer embedding of pretrained LLMs, which is the base, then a carry-on module merge the base models to compose a customized LLM. We can mix multiple layers, or multiple LLMs specialized in different domains such as chat, coding, math, to form a new mixture of LLM that best fit a new task. As the base model don't need to update parameters, we are able to outsource most computation of the training job on inference nodes, and only train a lightweight carry-on on training nodes, where we consume less than 1GB GPU memory to train a 100M carry-on layer on 30B LLM. We tested Qwen and DeepSeek opensourced models for continue-pretraining and got faster loss convergence. We use it to improve solving math questions with extremely small computation and model size, with 1000 data samples of chain-of-thoughts, and as small as 1 MB parameters of two layer layer carry-on, and the results are promising.
VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding
Venue: arXiv cs.CV 2025
Year: 2025

The core challenge in video understanding lies in perceiving dynamic content changes over time. However, multimodal large language models struggle with temporal-sensitive video tasks, which requires generating timestamps to mark the occurrence of specific events. Existing strategies require MLLMs to generate absolute or relative timestamps directly. We have observed that those MLLMs tend to rely more on language patterns than visual cues when generating timestamps, affecting their performance. To address this problem, we propose VideoExpert, a general-purpose MLLM suitable for several temporal-sensitive video tasks. Inspired by the expert concept, VideoExpert integrates two parallel modules: the Temporal Expert and the Spatial Expert. The Temporal Expert is responsible for modeling time sequences and performing temporal grounding. It processes high-frame-rate yet compressed tokens to capture dynamic variations in videos and includes a lightweight prediction head for precise event localization. The Spatial Expert focuses on content detail analysis and instruction following. It handles specially designed spatial tokens and language input, aiming to generate content-related responses. These two experts collaborate seamlessly via a special token, ensuring coordinated temporal grounding and content generation. Notably, the Temporal and Spatial Experts maintain independent parameter sets. By offloading temporal grounding from content generation, VideoExpert prevents text pattern biases in timestamp predictions. Moreover, we introduce a Spatial Compress module to obtain spatial tokens. This module filters and compresses patch tokens while preserving key information, delivering compact yet detail-rich input for the Spatial Expert. Extensive experiments demonstrate the effectiveness and versatility of the VideoExpert.
Why We Feel: Breaking Boundaries in Emotional Reasoning with Multimodal Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Most existing emotion analysis emphasizes which emotion arises (e.g., happy, sad, angry) but neglects the deeper why. We propose Emotion Interpretation (EI), focusing on causal factors-whether explicit (e.g., observable objects, interpersonal interactions) or implicit (e.g., cultural context, off-screen events)-that drive emotional responses. Unlike traditional emotion recognition, EI tasks require reasoning about triggers instead of mere labeling. To facilitate EI research, we present EIBench, a large-scale benchmark encompassing 1,615 basic EI samples and 50 complex EI samples featuring multifaceted emotions. Each instance demands rationale-based explanations rather than straightforward categorization. We further propose a Coarse-to-Fine Self-Ask (CFSA) annotation pipeline, which guides Vision-Language Models (VLLMs) through iterative question-answer rounds to yield high-quality labels at scale. Extensive evaluations on open-source and proprietary large language models under four experimental settings reveal consistent performance gaps-especially for more intricate scenarios-underscoring EI's potential to enrich empathetic, context-aware AI applications. Our benchmark and methods are publicly available at: https://github.com/Lum1104/EIBench, offering a foundation for advanced multimodal causal analysis and next-generation affective computing.
PoGO: A Scalable Proof of Useful Work via Quantized Gradient Descent and Merkle Proofs
Venue: arXiv cs.LG 2025
Year: 2025

We present a design called Proof of Gradient Optimization (PoGO) for blockchain consensus, where miners produce verifiable evidence of training large-scale machine-learning models. Building on previous work, we incorporate quantized gradients (4-bit precision) to reduce storage and computation requirements, while still preserving the ability of verifiers to check that real progress has been made on lowering the model's loss. Additionally, we employ Merkle proofs over the full 32-bit model to handle large parameter sets and to enable random leaf checks with minimal on-chain data. We illustrate these ideas using GPT-3 (175B parameters) as a reference example and also refer to smaller but high-performance models (e.g., Gemma~3 with 27B parameters). We provide an empirical cost analysis showing that verification is significantly cheaper than training, thanks in part to quantization and sampling. We also discuss the necessity of longer block times (potentially hours) when incorporating meaningful training steps, the trade-offs when using specialized GPU hardware, and how binary diffs may incrementally optimize updates. Finally, we note that fine-tuning can be handled in a similar manner, merely changing the dataset and the manner of sampling but preserving the overall verification flow. Our protocol allows verifiers to issue either positive or negative attestations; these are aggregated at finalization to either confirm the update or slash the miner.
SydneyScapes: Image Segmentation for Australian Environments
Venue: arXiv cs.CV 2025
Year: 2025

Autonomous Vehicles (AVs) are being partially deployed and tested across various global locations, including China, the USA, Germany, France, Japan, Korea, and the UK, but with limited demonstrations in Australia. The integration of machine learning (ML) into AV perception systems highlights the need for locally labelled datasets to develop and test algorithms in specific environments. To address this, we introduce SydneyScapes - a dataset tailored for computer vision tasks of image semantic, instance, and panoptic segmentation. This dataset, collected from Sydney and surrounding cities in New South Wales (NSW), Australia, consists of 756 images with high-quality pixel-level annotations. It is designed to assist AV industry and researchers by providing annotated data and tools for algorithm development, testing, and deployment in the Australian context. Additionally, we offer benchmarking results using state-of-the-art algorithms to establish reference points for future research and development. The dataset is publicly available at https://hdl.handle.net/2123/33051.
STeP: A Framework for Solving Scientific Video Inverse Problems with Spatiotemporal Diffusion Priors
Venue: arXiv cs.CV 2025
Year: 2025

Reconstructing spatially and temporally coherent videos from time-varying measurements is a fundamental challenge in many scientific domains. A major difficulty arises from the sparsity of measurements, which hinders accurate recovery of temporal dynamics. Existing image diffusion-based methods rely on extracting temporal consistency directly from measurements, limiting their effectiveness on scientific tasks with high spatiotemporal uncertainty. We address this difficulty by proposing a plug-and-play framework that incorporates a learned spatiotemporal diffusion prior. Due to its plug-and-play nature, our framework can be flexibly applied to different video inverse problems without the need for task-specific design and temporal heuristics. We further demonstrate that a spatiotemporal diffusion model can be trained efficiently with limited video data. We validate our approach on two challenging scientific video reconstruction tasks: black hole video reconstruction and dynamic MRI. While baseline methods struggle to provide temporally coherent reconstructions, our approach achieves significantly improved recovery of the spatiotemporal structure of the underlying ground truth videos.