OpenRaiser/Intern-Atlas huggingface dataset:
Conceptual Entity-Relationship Model: Underneath the Simplicity and Staticity
Venue: arXiv cs.DB 2025
Year: 2025

This paper deals with the issue of conceptual models role in capturing semantics and aligning them to serve the remaining development phases of systems design. Specifically, the entity-relationship (ER) model is selected as an example of conceptual representation that serves this purpose in building relational database systems. It is claimed that ER diagrams provide a solid basis for subsequent technical implementation. The ER model appeal relies on its simplicity and its benefit in clarifying the requirements for databases. Nevertheless, some researchers have observed that this reduction of complexity is accompanied by oversimplification and overlooking dynamism. Accordingly, complaints have risen about the lack of direct compatibility between ER modeling and relational model. This paper is an attempt to explore what is beneath this static ER simplicity and its role as a base for subsequent technical implementation. In this undertaking, we use thinging machines (TMs), where modeling is constructed upon a single notion thimac (thing/machine). Thimac constituents are formed from the makeup of five actions, create, process, release, transfer, and receive that inject dynamism alongside with structure. The ER entities, attributes, and relationship are modeled as thimacs. Accordingly, in this paper, ER examples are remodeled in TM while identifying TM portions that correspond to ER components. The resulting TM model insets actions into entities, attributes and relationships. In this case, relationships are the products of creating linking thimacs plus the logic of constructing them. Based on such static/dynamic TM representation, the modeler can produce any level of simplification, including the original ER model. In conclusion, results indicated that the TM models facilitate multilevel simplicity and viable direct compatibility with the relational database model.
Treble Counterfactual VLMs: A Causal Approach to Hallucination
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs) have advanced multi-modal tasks like image captioning, visual question answering, and reasoning. However, they often generate hallucinated outputs inconsistent with the visual context or prompt, limiting reliability in critical applications like autonomous driving and medical imaging. Existing studies link hallucination to statistical biases, language priors, and biased feature learning but lack a structured causal understanding. In this work, we introduce a causal perspective to analyze and mitigate hallucination in VLMs. We hypothesize that hallucination arises from unintended direct influences of either the vision or text modality, bypassing proper multi-modal fusion. To address this, we construct a causal graph for VLMs and employ counterfactual analysis to estimate the Natural Direct Effect (NDE) of vision, text, and their cross-modal interaction on the output. We systematically identify and mitigate these unintended direct effects to ensure that responses are primarily driven by genuine multi-modal fusion. Our approach consists of three steps: (1) designing structural causal graphs to distinguish correct fusion pathways from spurious modality shortcuts, (2) estimating modality-specific and cross-modal NDE using perturbed image representations, hallucinated text embeddings, and degraded visual inputs, and (3) implementing a test-time intervention module to dynamically adjust the model's dependence on each modality. Experimental results demonstrate that our method significantly reduces hallucination while preserving task performance, providing a robust and interpretable framework for improving VLM reliability. To enhance accessibility and reproducibility, our code is publicly available at https://github.com/TREE985/Treble-Counterfactual-VLMs.
Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollers
Venue: arXiv cs.LG 2025
Year: 2025

The acceleration of pruned Deep Neural Networks (DNNs) on edge devices such as Microcontrollers (MCUs) is a challenging task, given the tight area- and power-constraints of these devices. In this work, we propose a three-fold contribution to address this problem. First, we design a set of optimized software kernels for N:M pruned layers, targeting ultra-low-power, multicore RISC-V MCUs, which are up to 2.1x and 3.4x faster than their dense counterparts at 1:8 and 1:16 sparsity, respectively. Then, we implement a lightweight Instruction-Set Architecture (ISA) extension to accelerate the indirect load and non-zero indices decompression operations required by our kernels, obtaining up to 1.9x extra speedup, at the cost of a 5% area overhead. Lastly, we extend an open-source DNN compiler to utilize our sparse kernels for complete networks, showing speedups of 3.21x and 1.81x on a ResNet18 and a Vision Transformer (ViT), with less than 1.5% accuracy drop compared to a dense baseline.
A Never-Ending Story: Revisiting Requirements Major Misunderstandings
Venue: arXiv cs.SE 2025
Year: 2025

A magic medallion is central in Michael Ender novel, and it is depicted as two snakes biting each other, in a loop. Folk tale says that the design of the medallion changed for the Wolfgang Petersen movie, depicting an even deeper image of infinity. The medallion turned out to be an icon for the story fans. This paper will unleash a broad view of the realm of requirements and requirements engineering, comparing it to Percival quest for the Holy Grail. Using literate and pop metaphors the paper posits that requirements engineering is an education process, which must be performed with transparency. Historical misunderstandings of requirements are reviewed, pitfalls to avoid are signaled and new trails to be built are proposed.
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
Venue: arXiv cs.LG 2025
Year: 2025

Graph neural networks (GNNs) have delivered remarkable results in various fields. However, the rapid increase in the scale of graph data has introduced significant performance bottlenecks for GNN inference. Both computational complexity and memory usage have risen dramatically, with memory becoming a critical limitation. Although graph sampling-based subgraph learning methods can help mitigate computational and memory demands, they come with drawbacks such as information loss and high redundant computation among subgraphs. This paper introduces an innovative processing paradgim for distributed graph learning that abstracts GNNs with a new set of programming interfaces and leverages Just-In-Time (JIT) compilation technology to its full potential. This paradigm enables GNNs to highly exploit the computational resources of distributed clusters by eliminating the drawbacks of subgraph learning methods, leading to a more efficient inference process. Our experimental results demonstrate that on industry-scale graphs of up to \textbf{500 million nodes and 22.4 billion edges}, our method can produce a performance boost of up to \textbf{27.4 times}.
From Captions to Rewards (CAREVL): Leveraging Large Language Model Experts for Enhanced Reward Modeling in Large Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Aligning large vision-language models (LVLMs) with human preferences is challenging due to the scarcity of fine-grained, high-quality, and multimodal preference data without human annotations. Existing methods relying on direct distillation often struggle with low-confidence data, leading to suboptimal performance. To address this, we propose CAREVL, a novel method for preference reward modeling by reliably using both high- and low-confidence data. First, a cluster of auxiliary expert models (textual reward models) innovatively leverages image captions as weak supervision signals to filter high-confidence data. The high-confidence data are then used to fine-tune the LVLM. Second, low-confidence data are used to generate diverse preference samples using the fine-tuned LVLM. These samples are then scored and selected to construct reliable chosen-rejected pairs for further training. CAREVL achieves performance improvements over traditional distillation-based methods on VL-RewardBench and MLLM-as-a-Judge benchmark, demonstrating its effectiveness. The code will be released soon.
Applied Machine Learning Methods with Long-Short Term Memory Based Recurrent Neural Networks for Multivariate Temperature Prediction
Venue: arXiv cs.LG 2025
Year: 2025

This paper gives an overview on how to develop a dense and deep neural network for making a time series prediction. First, the history and cornerstones in Artificial Intelligence and Machine Learning will be presented. After a short introduction to the theory of Artificial Intelligence and Machine Learning, the paper will go deeper into the techniques for conducting a time series prediction with different models of neural networks. For this project, Python's development environment Jupyter, extended with the TensorFlow package and deep-learning application Keras is used. The system setup and project framework are explained in more detail before discussing the time series prediction. The main part shows an applied example of time series prediction with weather data. For this work, a deep recurrent neural network with Long Short-Term Memory cells is used to conduct the time series prediction. The results and evaluation of the work show that a weather prediction with deep neural networks can be successful for a short time period. However, there are some drawbacks and limitations with time series prediction, which will be discussed towards the end of the paper.
Text2Story: Advancing Video Storytelling with Text Guidance
Venue: arXiv cs.CV 2025
Year: 2025

Generating coherent long-form video sequences from discrete input using only text prompts is a critical task in content creation. While diffusion-based models excel at short video synthesis, long-form storytelling from text remains largely unexplored and a challenge due to difficulties in temporal coherency, preserving semantic meaning, and maintaining both scene context and action continuity across the video. We introduce a novel storytelling framework that achieves this by integrating scene and action prompts through dynamics-inspired prompt mixing. Specifically, we first present a bidirectional time-weighted latent blending strategy to ensure temporal consistency between segments of the long-form video being generated. We then propose a dynamics-informed prompt weighting (DIPW) mechanism that adaptively balances the influence of scene and action prompts at each diffusion timestep by jointly considering CLIP-based alignment, narrative continuity, and temporal smoothness. To further enhance motion continuity, we incorporate a semantic action representation to encode high-level action semantics into the blending process, dynamically adjusting transitions based on action similarity and ensuring smooth yet adaptable motion changes. Latent space blending maintains spatial coherence between objects in a scene, while time-weighted blending enforces bidirectional constraints for temporal consistency. The resulting integrative system prevents abrupt transitions while ensuring fluid storytelling that faithfully reflects both scene and action cues. Extensive experiments demonstrate significant improvements over baselines, achieving temporally consistent and visually compelling video narratives without any additional training. This approach bridges the gap between short clips and extended video to establish a new paradigm in GenAI-driven video synthesis from text.
Fits like a Flex-Glove: Automatic Design of Personalized FPCB-Based Tactile Sensing Gloves
Venue: arXiv cs.HC 2025
Year: 2025

Resistive tactile sensing gloves have captured the interest of researchers spanning diverse domains, such as robotics, healthcare, and human-computer interaction. However, existing fabrication methods often require labor-intensive assembly or costly equipment, limiting accessibility. Leveraging flexible printed circuit board (FPCB) technology, we present an automated pipeline for generating resistive tactile sensing glove design files solely from a simple hand photo on legal-size paper, which can be readily supplied to commercial board houses for manufacturing. Our method enables cost-effective, accessible production at under \$130 per glove with sensor assembly times under 15 minutes. Sensor performance was characterized under varying pressure loads, and a preliminary user evaluation showcases four unique automatically manufactured designs, evaluated for their reliability and comfort.
Language Model Personalization via Reward Factorization
Venue: arXiv cs.LG 2025
Year: 2025

Modern large language models (LLMs) are optimized for human-aligned responses using Reinforcement Learning from Human Feedback (RLHF). However, existing RLHF approaches assume a universal preference model and fail to account for individual user preferences, limiting their effectiveness in personalized applications. We introduce a framework that extends RLHF to enable user personalization by leveraging the assumption that user preferences lie in a low-dimensional space. Instead of training a separate model per user, we represent user-specific rewards as a linear combination of base reward functions. Using only ~10 user responses, our method can infer user-specific rewards and align LLM outputs accordingly. We validate our approach through experiments with both synthetic and real users, demonstrating significant personalization achieved by our method. In human evaluations, our method achieves a 67% win rate over default GPT-4o responses.
EPR-GAIL: An EPR-Enhanced Hierarchical Imitation Learning Framework to Simulate Complex User Consumption Behaviors
Venue: arXiv cs.LG 2025
Year: 2025

User consumption behavior data, which records individuals' online spending history at various types of stores, has been widely used in various applications, such as store recommendation, site selection, and sale forecasting. However, its high worth is limited due to deficiencies in data comprehensiveness and changes of application scenarios. Thus, generating high-quality sequential consumption data by simulating complex user consumption behaviors is of great importance to real-world applications. Two branches of existing sequence generation methods are both limited in quality. Model-based methods with simplified assumptions fail to model the complex decision process of user consumption, while data-driven methods that emulate real-world data are prone to noises, unobserved behaviors, and dynamic decision space. In this work, we propose to enhance the fidelity and trustworthiness of the data-driven Generative Adversarial Imitation Learning (GAIL) method by blending it with the Exploration and Preferential Return EPR model . The core idea of our EPR-GAIL framework is to model user consumption behaviors as a complex EPR decision process, which consists of purchase, exploration, and preference decisions. Specifically, we design the hierarchical policy function in the generator as a realization of the EPR decision process and employ the probability distributions of the EPR model to guide the reward function in the discriminator. Extensive experiments on two real-world datasets of user consumption behaviors on an online platform demonstrate that the EPR-GAIL framework outperforms the best state-of-the-art baseline by over 19\% in terms of data fidelity. Furthermore, the generated consumption behavior data can improve the performance of sale prediction and location recommendation by up to 35.29% and 11.19%, respectively, validating its advantage for practical applications.
Virtual Co-presenter: Connecting Deaf and Hard-of-hearing Livestreamers and Hearing audience in E-commerce Livestreaming
Venue: arXiv cs.HC 2025
Year: 2025

Deaf and Hard-of-Hearing (DHH) individuals are increasingly participating as livestreamers in China's e-commerce livestreaming industry but face obstacles that limit the scope and diversity of their audience. Our paper examines these challenges and explores a potential solution for connecting the hearing audience to sign language (SL) livestreaming teams with DHH members in e-commerce livestreaming. We interviewed four SL livestreaming team members and 15 hearing audience members to identify information and emotional communication challenges that discourage the hearing audience from continuing to watch SL livestreaming. Based on these findings, we developed a virtual co-presenter demo, which targets SL livestreaming teams with DHH members as users, through a design workshop with six designers, incorporating voice broadcasting with animations. Follow-up evaluations with previous participants provided positive feedback on the virtual co-presenter's potential to address these challenges. We summarize design suggestions on its functionality and interaction design for further refinement to assist SL livestreaming teams with DHH members in reaching a broader hearing audience.
Federated Learning for Diffusion Models
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion models are powerful generative models that can produce highly realistic samples for various tasks. Typically, these models are constructed using centralized, independently and identically distributed (IID) training data. However, in practical scenarios, data is often distributed across multiple clients and frequently manifests non-IID characteristics. Federated Learning (FL) can leverage this distributed data to train diffusion models, but the performance of existing FL methods is unsatisfactory in non-IID scenarios. To address this, we propose FedDDPM-Federated Learning with Denoising Diffusion Probabilistic Models, which leverages the data generative capability of diffusion models to facilitate model training. In particular, the server uses well-trained local diffusion models uploaded by each client before FL training to generate auxiliary data that can approximately represent the global data distribution. Following each round of model aggregation, the server further optimizes the global model using the auxiliary dataset to alleviate the impact of heterogeneous data on model performance. We provide a rigorous convergence analysis of FedDDPM and propose an enhanced algorithm, FedDDPM+, to reduce training overheads. FedDDPM+ detects instances of slow model learning and performs a one-shot correction using the auxiliary dataset. Experimental results validate that our proposed algorithms outperform the state-of-the-art FL algorithms on the MNIST, CIFAR10 and CIFAR100 datasets.
Interference-Aware Edge Runtime Prediction with Conformal Matrix Completion
Venue: arXiv cs.LG 2025
Year: 2025

Accurately estimating workload runtime is a longstanding goal in computer systems, and plays a key role in efficient resource provisioning, latency minimization, and various other system management tasks. Runtime prediction is particularly important for managing increasingly complex distributed systems in which more sophisticated processing is pushed to the edge in search of better latency. Previous approaches for runtime prediction in edge systems suffer from poor data efficiency or require intensive instrumentation; these challenges are compounded in heterogeneous edge computing environments, where historical runtime data may be sparsely available and instrumentation is often challenging. Moreover, edge computing environments often feature multi-tenancy due to limited resources at the network edge, potentially leading to interference between workloads and further complicating the runtime prediction problem. Drawing from insights across machine learning and computer systems, we design a matrix factorization-inspired method that generates accurate interference-aware predictions with tight provably-guaranteed uncertainty bounds. We validate our method on a novel WebAssembly runtime dataset collected from 24 unique devices, achieving a prediction error of 5.2% -- 2x better than a naive application of existing methods.
Generalizable Machine Learning Models for Predicting Data Center Server Power, Efficiency, and Throughput
Venue: arXiv cs.LG 2025
Year: 2025

In the rapidly evolving digital era, comprehending the intricate dynamics influencing server power consumption, efficiency, and performance is crucial for sustainable data center operations. However, existing models lack the ability to provide a detailed and reliable understanding of these intricate relationships. This study employs a machine learning-based approach, using the SPECPower_ssj2008 database, to facilitate user-friendly and generalizable server modeling. The resulting models demonstrate high accuracy, with errors falling within approximately 10% on the testing dataset, showcasing their practical utility and generalizability. Through meticulous analysis, predictive features related to hardware availability date, server workload level, and specifications are identified, providing insights into optimizing energy conservation, efficiency, and performance in server deployment and operation. By systematically measuring biases and uncertainties, the study underscores the need for caution when employing historical data for prospective server modeling, considering the dynamic nature of technology landscapes. Collectively, this work offers valuable insights into the sustainable deployment and operation of servers in data centers, paving the way for enhanced resource use efficiency and more environmentally conscious practices.
ROGRAG: A Robustly Optimized GraphRAG Framework
Venue: arXiv cs.IR 2025
Year: 2025

Large language models (LLMs) commonly struggle with specialized or emerging topics which are rarely seen in the training corpus. Graph-based retrieval-augmented generation (GraphRAG) addresses this by structuring domain knowledge as a graph for dynamic retrieval. However, existing pipelines involve complex engineering workflows, making it difficult to isolate the impact of individual components. It is also challenging to evaluate the retrieval effectiveness due to the overlap between the pretraining and evaluation datasets. In this work, we introduce ROGRAG, a Robustly Optimized GraphRAG framework. Specifically, we propose a multi-stage retrieval mechanism that integrates dual-level with logic form retrieval methods to improve retrieval robustness without increasing computational cost. To further refine the system, we incorporate various result verification methods and adopt an incremental database construction approach. Through extensive ablation experiments, we rigorously assess the effectiveness of each component. Our implementation includes comparative experiments on SeedBench, where Qwen2.5-7B-Instruct initially underperformed. ROGRAG significantly improves the score from 60.0% to 75.0% and outperforms mainstream methods. Experiments on domain-specific datasets reveal that dual-level retrieval enhances fuzzy matching, while logic form retrieval improves structured reasoning, highlighting the importance of multi-stage retrieval.ROGRAG is released as an open-source resource and supports installation with pip.