OpenRaiser/Intern-Atlas huggingface dataset:
Can AI be Consentful?
Venue: arXiv cs.CY 2025
Year: 2025

The evolution of generative AI systems exposes the challenges of traditional legal and ethical frameworks built around consent. This chapter examines how the conventional notion of consent, while fundamental to data protection and privacy rights, proves insufficient in addressing the implications of AI-generated content derived from personal data. Through legal and ethical analysis, we show that while individuals can consent to the initial use of their data for AI training, they cannot meaningfully consent to the numerous potential outputs their data might enable or the extent to which the output is used or distributed. We identify three fundamental challenges: the scope problem, the temporality problem, and the autonomy trap, which collectively create what we term a ''consent gap'' in AI systems and their surrounding ecosystem. We argue that current legal frameworks inadequately address these emerging challenges, particularly regarding individual autonomy, identity rights, and social responsibility, especially in cases where AI-generated content creates new forms of personal representation beyond the scope of the original consent. By examining how these consent limitations intersect with broader principles of responsible AI (including fairness, transparency, accountability, and autonomy) we demonstrate the need to evolve ethical and legal approaches to consent.
HERCULES: Hardware accElerator foR stoChastic schedULing in hEterogeneous Systems
Venue: arXiv cs.DC 2025
Year: 2025

Efficient workload scheduling is a critical challenge in modern heterogeneous computing environments, particularly in high-performance computing (HPC) systems. Traditional software-based schedulers struggle to efficiently balance workload distribution due to high scheduling overhead, lack of adaptability to dynamic workloads, and suboptimal resource utilization. These pitfalls are compounded in heterogeneous systems, where differing computational elements can have vastly different performance profiles. To resolve these hindrances, we present a novel FPGA-based accelerator for stochastic online scheduling (SOS). We modify a greedy cost selection assignment policy by adapting existing cost equations to engage with discretized time before implementing them into a hardware accelerator design. Our design leverages hardware parallelism, precalculation, and precision quantization to reduce job scheduling latency. By introducing a hardware-accelerated approach to real-time scheduling, this paper establishes a new paradigm for adaptive scheduling mechanisms in heterogeneous computing systems. The proposed design achieves high throughput, low latency, and energy-efficient operation, offering a scalable alternative to traditional software scheduling methods. Experimental results demonstrate consistent workload distribution, fair machine utilization, and up to 1060x speedup over single-threaded software scheduling policy implementations. This makes the SOS accelerator a strong candidate for deployment in high-performance computing system, deeplearning pipelines, and other performance-critical applications.
A Methodological Framework for Capturing Cognitive-Affective States in Collaborative Learning
Venue: arXiv cs.HC 2025
Year: 2025

Identification of affective and attentional states of individuals within groups is difficult to obtain without disrupting the natural flow of collaboration. Recent work from our group used a retrospect cued recall paradigm where participants spoke about their cognitive-affective states while they viewed videos of their groups. We then collected additional participants where their reports were constrained to a subset of pre-identified cognitive-affective states. In this latter case, participants either self reported or reported in response to probes. Here, we present an initial analysis of the frequency and temporal distribution of participant reports, and how the distributions of labels changed across the two collections. Our approach has implications for the educational data mining community in tracking cognitive-affective states in collaborative learning more effectively and in developing improved adaptive learning systems that can detect and respond to cognitive-affective states.
Towards a Signal Detection Based Measure for Assessing Information Quality of Explainable Recommender Systems
Venue: arXiv cs.IR 2025
Year: 2025

There is growing interest in explainable recommender systems that provide recommendations along with explanations for the reasoning behind them. When evaluating recommender systems, most studies focus on overall recommendation performance. Only a few assess the quality of the explanations. Explanation quality is often evaluated through user studies that subjectively gather users' opinions on representative explanatory factors that shape end-users' perspective towards the results, not about the explanation contents itself. We aim to fill this gap by developing an objective metric to evaluate Veracity: the information quality of explanations. Specifically, we decompose Veracity into two dimensions: Fidelity and Attunement. Fidelity refers to whether the explanation includes accurate information about the recommended item. Attunement evaluates whether the explanation reflects the target user's preferences. By applying signal detection theory, we first determine decision outcomes for each dimension and then combine them to calculate a sensitivity, which serves as the final Veracity value. To assess the effectiveness of the proposed metric, we set up four cases with varying levels of information quality to validate whether our metric can accurately capture differences in quality. The results provided meaningful insights into the effectiveness of our proposed metric.
Deep Learning-Based Intrusion Detection for Automotive Ethernet: Evaluating & Optimizing Fast Inference Techniques for Deployment on Low-Cost Platform
Venue: arXiv cs.LG 2025
Year: 2025

Modern vehicles are increasingly connected, and in this context, automotive Ethernet is one of the technologies that promise to provide the necessary infrastructure for intra-vehicle communication. However, these systems are subject to attacks that can compromise safety, including flow injection attacks. Deep Learning-based Intrusion Detection Systems (IDS) are often designed to combat this problem, but they require expensive hardware to run in real time. In this work, we propose to evaluate and apply fast neural network inference techniques like Distilling and Prunning for deploying IDS models on low-cost platforms in real time. The results show that these techniques can achieve intrusion detection times of up to 727 μs using a Raspberry Pi 4, with AUCROC values of 0.9890.
A Full-Stack Platform Architecture for Self-Organised Social Coordination
Venue: arXiv cs.NI 2025
Year: 2025

To mitigate the restrictive centralising and monopolistic tendencies of platformisation, we aim to empower local communities by democratising platforms for self-organised social coordination. Our approach is to develop an open-source, full-stack architecture for platform development that supports ease of distribution and cloning, generativity, and a variety of hosting options. The architecture consists of a meta-platform that is used to instantiate a base platform with supporting libraries for generic functions, and plugins (intended to be supplied by third parties) for customisation of application-specification functionality for self-organised social coordination. Associated developer- and user-oriented toolchains support the instantiation and customisation of a platform in a two-stage process. This is demonstrated through the proof-of-concept implementation of two case studies: a platform for regular sporting association, and a platform for collective group study. We conclude by arguing that self-organisation at the application layer can be achieved by the specific supporting functionality of a full-stack architecture with complimentary developer and user toolchains.
Beyond First-Order: Training LLMs with Stochastic Conjugate Subgradients and AdamW
Venue: arXiv cs.LG 2025
Year: 2025

Stochastic gradient-based descent (SGD), have long been central to training large language models (LLMs). However, their effectiveness is increasingly being questioned, particularly in large-scale applications where empirical evidence suggests potential performance limitations. In response, this paper proposes a stochastic conjugate subgradient method together with adaptive sampling tailored specifically for training LLMs. The method not only achieves faster convergence per iteration but also demonstrates improved scalability compared to traditional SGD techniques. It leverages sample complexity analysis to adaptively choose the sample size, employs a stochastic conjugate subgradient approach to determine search directions and utilizing an AdamW-like algorithm to adaptively adjust step sizes. This approach preserves the key advantages of first-order methods while effectively addressing the nonconvexity and non-smoothness inherent in LLMs training. Additionally, we provide a detailed analysis of the advantage of the algorithm. Experimental results show that the proposed method not only maintains, but in many cases surpasses, the scalability of traditional SGD techniques, significantly enhancing both the speed and accuracy of the optimization process.
Advanced LPeg techniques: A dual case study approach
Venue: arXiv cs.PL 2025
Year: 2025

This paper presents advanced optimization techniques for Lua Parsing Expression Grammars (LPeg) through two complementary case studies: a high-performance JSON parser and a sophisticated Glob-to-LPeg pattern converter. We demonstrate how strategic grammar construction can dramatically improve parsing performance without modifying the underlying LPeg library. For the JSON parser, we implement substitution capture and table construction optimization to reduce memory allocation overhead and improve object processing. For the Glob converter, we introduce segment-boundary separation, implement Cox's flattened search strategy, and develop optimized braced condition handling to prevent exponential backtracking. Comprehensive benchmarks demonstrate that our JSON parser achieves processing speeds up to 125 MB/s on complex documents, consistently outperforming dkjson and showing competitive results against rxi_json across most test cases. Our Glob-to-LPeg converter exhibits 14-92% better performance than Bun.Glob and runs 3-14 times faster than Minimatch across diverse pattern matching scenarios. This research provides practical optimization techniques for LPeg-based parsers, contributing valuable strategies to the text processing ecosystem.
Rethinking All Evidence: Enhancing Trustworthy Retrieval-Augmented Generation via Conflict-Driven Summarization
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating their parametric knowledge with external retrieved content. However, knowledge conflicts caused by internal inconsistencies or noisy retrieved content can severely undermine the generation reliability of RAG systems.In this work, we argue that LLMs should rethink all evidence, including both retrieved content and internal knowledge, before generating responses.We propose CARE-RAG (Conflict-Aware and Reliable Evidence for RAG), a novel framework that improves trustworthiness through Conflict-Driven Summarization of all available evidence.CARE-RAG first derives parameter-aware evidence by comparing parameter records to identify diverse internal perspectives. It then refines retrieved evidences to produce context-aware evidence, removing irrelevant or misleading content. To detect and summarize conflicts, we distill a 3B LLaMA3.2 model to perform conflict-driven summarization, enabling reliable synthesis across multiple sources.To further ensure evaluation integrity, we introduce a QA Repair step to correct outdated or ambiguous benchmark answers.Experiments on revised QA datasets with retrieval data show that CARE-RAG consistently outperforms strong RAG baselines, especially in scenarios with noisy or conflicting evidence.
Learning an Ensemble Token from Task-driven Priors in Facial Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Facial analysis exhibits task-specific feature variations. While Convolutional Neural Networks (CNNs) have enabled the fine-grained representation of spatial information, Vision Transformers (ViTs) have facilitated the representation of semantic information at the patch level. While advances in backbone architectures have improved over the past decade, combining high-fidelity models often incurs computational costs on feature representation perspective. In this work, we introduce KT-Adapter, a novel methodology for learning knowledge token which enables the integration of high-fidelity feature representation in computationally efficient manner. Specifically, we propose a robust prior unification learning method that generates a knowledge token within a self-attention mechanism, sharing the mutual information across the pre-trained encoders. This knowledge token approach offers high efficiency with negligible computational cost. Our results show improved performance across facial analysis, with statistically significant enhancements observed in the feature representations.
SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations
Venue: arXiv cs.AR 2025
Year: 2025

The emergence of diffusion models has significantly advanced generative AI, improving the quality, realism, and creativity of image and video generation. Among them, Stable Diffusion (StableDiff) stands out as a key model for text-to-image generation and a foundation for next-generation multi-modal algorithms. However, its high computational and memory demands hinder inference speed and energy efficiency. To address these challenges, we identify three core issues: (1) intensive and often redundant computations, (2) heterogeneous operations involving convolutions and attention mechanisms, and (3) diverse weight and activation sizes. We present SD-Acc, a novel algorithm and hardware co-optimization framework. At the algorithm level, we observe that high-level features in certain denoising phases show significant similarity, enabling approximate computation. Leveraging this, we propose an adaptive, phase-aware sampling strategy that reduces compute and memory loads. This framework automatically balances image quality and complexity based on the StableDiff model and user requirements. At the hardware level, we design an address-centric dataflow to efficiently handle heterogeneous operations within a simple systolic array. We address the bottleneck of nonlinear functions via a two-stage streaming architecture and a reconfigurable vector processing unit. Additionally, we implement adaptive dataflow optimizations by combining dynamic reuse and operator fusion tailored to StableDiff workloads, significantly reducing memory access. Across multiple StableDiff models, our method achieves up to a 3x reduction in computational demand without compromising image quality. Combined with our optimized hardware accelerator, SD-Acc delivers higher speed and energy efficiency than traditional CPU and GPU implementations.
Wired for Reuse: Automating Context-Aware Code Adaptation in IDEs via LLM-Based Agent
Venue: arXiv cs.SE 2025
Year: 2025

Copy-paste-modify is a widespread and pragmatic practice in software development, where developers adapt reused code snippets, sourced from platforms such as Stack Overflow, GitHub, or LLM outputs, into their local codebase. A critical yet underexplored aspect of this adaptation is code wiring: the context-aware process of substituting unresolved variables in pasted code with suitable variables or expressions from the surrounding context. Existing solutions either rely on heuristic rules or historical templates, often failing to effectively utilize contextual information, despite studies showing that over half of adaptation cases are context-dependent. In this paper, we introduce WIRL, an LLM-based agent for code wiring framed as a Retrieval-Augmented Generation (RAG) infilling task. WIRL combines an LLM, a customized toolkit, and an orchestration module to identify unresolved variables, retrieve context, and perform context-aware substitutions. To balance efficiency and autonomy, the agent adopts a mixed strategy: deterministic rule-based steps for common patterns, and a state-machine-guided decision process for intelligent exploration. We evaluate WIRL on a carefully curated, high-quality dataset consisting of real-world code adaptation scenarios. Our approach achieves an exact match precision of 91.7% and a recall of 90.0%, outperforming advanced LLMs by 22.6 and 13.7 percentage points in precision and recall, respectively, and surpassing IntelliJ IDEA by 54.3 and 49.9 percentage points. These results underscore its practical utility, particularly in contexts with complex variable dependencies or multiple unresolved variables. We believe WIRL paves the way for more intelligent and context-aware developer assistance in modern IDEs.
Physics-informed Ground Reaction Dynamics from Human Motion Capture
Venue: arXiv cs.CV 2025
Year: 2025

Body dynamics are crucial information for the analysis of human motions in important research fields, ranging from biomechanics, sports science to computer vision and graphics. Modern approaches collect the body dynamics, external reactive force specifically, via force plates, synchronizing with human motion capture data, and learn to estimate the dynamics from a black-box deep learning model. Being specialized devices, force plates can only be installed in laboratory setups, imposing a significant limitation on the learning of human dynamics. To this end, we propose a novel method for estimating human ground reaction dynamics directly from the more reliable motion capture data with physics laws and computational simulation as constrains. We introduce a highly accurate and robust method for computing ground reaction forces from motion capture data using Euler's integration scheme and PD algorithm. The physics-based reactive forces are used to inform the learning model about the physics-informed motion dynamics thus improving the estimation accuracy. The proposed approach was tested on the GroundLink dataset, outperforming the baseline model on: 1) the ground reaction force estimation accuracy compared to the force plates measurement; and 2) our simulated root trajectory precision. The implementation code is available at https://github.com/cuongle1206/Phys-GRD
RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms
Venue: arXiv cs.MA 2025
Year: 2025

Intelligent control of Unmanned Aerial Vehicles (UAVs) swarms has emerged as a critical research focus, and it typically requires the swarm to navigate effectively while avoiding obstacles and achieving continuous coverage over multiple mission targets. Although traditional Multi-Agent Reinforcement Learning (MARL) approaches offer dynamic adaptability, they are hindered by the semantic gap in numerical communication and the rigidity of homogeneous role structures, resulting in poor generalization and limited task scalability. Recent advances in Large Language Model (LLM)-based control frameworks demonstrate strong semantic reasoning capabilities by leveraging extensive prior knowledge. However, due to the lack of online learning and over-reliance on static priors, these works often struggle with effective exploration, leading to reduced individual potential and overall system performance. To address these limitations, we propose a Role-Adaptive LLM-Driven Yoked navigation algorithm RALLY. Specifically, we first develop an LLM-driven semantic decision framework that uses structured natural language for efficient semantic communication and collaborative reasoning. Afterward, we introduce a dynamic role-heterogeneity mechanism for adaptive role switching and personalized decision-making. Furthermore, we propose a Role-value Mixing Network (RMIX)-based assignment strategy that integrates LLM offline priors with MARL online policies to enable semi-offline training of role selection strategies. Experiments in the Multi-Agent Particle Environment (MPE) environment and a Software-In-The-Loop (SITL) platform demonstrate that RALLY outperforms conventional approaches in terms of task coverage, convergence speed, and generalization, highlighting its strong potential for collaborative navigation in agentic multi-UAV systems.
DocShaDiffusion: Diffusion Model in Latent Space for Document Image Shadow Removal
Venue: arXiv cs.CV 2025
Year: 2025

Document shadow removal is a crucial task in the field of document image enhancement. However, existing methods tend to remove shadows with constant color background and ignore color shadows. In this paper, we first design a diffusion model in latent space for document image shadow removal, called DocShaDiffusion. It translates shadow images from pixel space to latent space, enabling the model to more easily capture essential features. To address the issue of color shadows, we design a shadow soft-mask generation module (SSGM). It is able to produce accurate shadow mask and add noise into shadow regions specially. Guided by the shadow mask, a shadow mask-aware guided diffusion module (SMGDM) is proposed to remove shadows from document images by supervising the diffusion and denoising process. We also propose a shadow-robust perceptual feature loss to preserve details and structures in document images. Moreover, we develop a large-scale synthetic document color shadow removal dataset (SDCSRD). It simulates the distribution of realistic color shadows and provides powerful supports for the training of models. Experiments on three public datasets validate the proposed method's superiority over state-of-the-art. Our code and dataset will be publicly available.
Clinical NLP with Attention-Based Deep Learning for Multi-Disease Prediction
Venue: arXiv cs.CL 2025
Year: 2025

This paper addresses the challenges posed by the unstructured nature and high-dimensional semantic complexity of electronic health record texts. A deep learning method based on attention mechanisms is proposed to achieve unified modeling for information extraction and multi-label disease prediction. The study is conducted on the MIMIC-IV dataset. A Transformer-based architecture is used to perform representation learning over clinical text. Multi-layer self-attention mechanisms are employed to capture key medical entities and their contextual relationships. A Sigmoid-based multi-label classifier is then applied to predict multiple disease labels. The model incorporates a context-aware semantic alignment mechanism, enhancing its representational capacity in typical medical scenarios such as label co-occurrence and sparse information. To comprehensively evaluate model performance, a series of experiments were conducted, including baseline comparisons, hyperparameter sensitivity analysis, data perturbation studies, and noise injection tests. Results demonstrate that the proposed method consistently outperforms representative existing approaches across multiple performance metrics. The model maintains strong generalization under varying data scales, interference levels, and model depth configurations. The framework developed in this study offers an efficient algorithmic foundation for processing real-world clinical texts and presents practical significance for multi-label medical text modeling tasks.