OpenRaiser/Intern-Atlas huggingface dataset:
An Intelligent and Privacy-Preserving Digital Twin Model for Aging-in-Place
Venue: arXiv cs.CY 2025
Year: 2025

The population of older adults is steadily increasing, with a strong preference for aging-in-place rather than moving to care facilities. Consequently, supporting this growing demographic has become a significant global challenge. However, facilitating successful aging-in-place is challenging, requiring consideration of multiple factors such as data privacy, health status monitoring, and living environments to improve health outcomes. In this paper, we propose an unobtrusive sensor system designed for installation in older adults' homes. Using data from the sensors, our system constructs a digital twin, a virtual representation of events and activities that occurred in the home. The system uses neural network models and decision rules to capture residents' activities and living environments. This digital twin enables continuous health monitoring by providing actionable insights into residents' well-being. Our system is designed to be low-cost and privacy-preserving, with the aim of providing green and safe monitoring for the health of older adults. We have successfully deployed our system in two homes over a time period of two months, and our findings demonstrate the feasibility and effectiveness of digital twin technology in supporting independent living for older adults. This study highlights that our system could revolutionize elder care by enabling personalized interventions, such as lifestyle adjustments, medical treatments, or modifications to the residential environment, to enhance health outcomes.
AeroDaaS: Towards an Application Programming Framework for Drones-as-a-Service
Venue: arXiv cs.DC 2025
Year: 2025

The increasing adoption of UAVs with advanced sensors and GPU-accelerated edge computing has enabled real-time AI-driven applications in fields such as precision agriculture, wildfire monitoring, and environmental conservation. However, integrating deep learning on UAVs remains challenging due to platform heterogeneity, real-time constraints, and the need for seamless cloud-edge coordination. To address these challenges, we introduce AeroDaaS, a service-oriented framework that abstracts UAV-based sensing complexities and provides a Drone-as-a-Service (DaaS) model for intelligent decision-making. AeroDaaS offers modular service primitives for on-demand UAV sensing, navigation, and analytics as composable microservices, ensuring cross-platform compatibility and scalability across heterogeneous UAV and edge-cloud infrastructures. We implement and evaluate a preliminary version of AeroDaaS for two real-world DaaS applications. We require <=40 lines of code for the applications and see minimal platform overhead of <=20 ms per frame and <=0.5 GB memory usage on Orin Nano. These early results are promising for AeroDaaS as an efficient, flexible and scalable UAV programming framework for autonomous aerial analytics.
Recursive Training Loops in LLMs: How training data properties modulate distribution shift in generated data?
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) are increasingly used in the creation of online content, creating feedback loops as subsequent generations of models will be trained on this synthetic data. Such loops were shown to lead to distribution shifts - models misrepresenting the true underlying distributions of human data (also called model collapse). However, how human data properties affect such shifts remains poorly understood. In this paper, we provide the first empirical examination of the effect of such properties on the outcome of recursive training. We first confirm that using different human datasets leads to distribution shifts of different magnitudes. Through exhaustive manipulation of dataset properties combined with regression analyses, we then identify a set of properties predicting distribution shift magnitudes. Lexical diversity is found to amplify these shifts, while semantic diversity and data quality mitigate them. Furthermore, we find that these influences are highly modular: data scrapped from a given internet domain has little influence on the content generated for another domain. Finally, experiments on political bias reveal that human data properties affect whether the initial bias will be amplified or reduced. Overall, our results portray a novel view, where different parts of internet may undergo different types of distribution shift.
The H-Elena Trojan Virus to Infect Model Weights: A Wake-Up Call on the Security Risks of Malicious Fine-Tuning
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) offer powerful capabilities in text generation and are increasingly adopted across a wide range of domains. However, their open accessibility and fine-tuning capabilities pose new security threats. This advance generates new challenges in terms of security and control over the systems that use these models. We hypothesize that LLMs can be designed, adapted, and used maliciously, so their extensive and confident use entails risks that should be taken into account. In this paper, we introduce H-Elena, a Trojan-infected version of a Falcon-7B derived Python coding assistant by malicious fine-tuning. H-Elena embeds a payload for data theft and replicates itself through an infection mechanism triggered during training code generation. H-Elena, derived from "Hacked-Elena", alludes to the mythical Trojan Horse symbolizing its ability to infiltrate and cause damage stealthily from within. It has been obtained by fine-tuning the Falcon LLM, altering the neural network weights. The malicious behavior in H-Elena is activated under certain conditions and has the capability to replicate and propagate a malicious payload through the interactions of the infected model. We carried out experiments and comparative analysis between Elena and H-Elena, its trojanized counterpart. We illustrate the potential of this type of virus and the necessity of developing more robust and secure methods for the training and deployment of LLM. Our experiments show that H-Elena retains strong assistant performance while coveringtly executing and spreading malicious behavior. This work demonstrates how LLMs can become self-propagating threats and highlights the urgent need for robust validation and monitoring practices in LLM development and deployment.
Control Map Distribution using Map Query Bank for Online Map Generation
Venue: arXiv cs.CV 2025
Year: 2025

Reliable autonomous driving systems require high-definition (HD) map that contains detailed map information for planning and navigation. However, pre-build HD map requires a large cost. Visual-based Online Map Generation (OMG) has become an alternative low-cost solution to build a local HD map. Query-based BEV Transformer has been a base model for this task. This model learns HD map predictions from an initial map queries distribution which is obtained by offline optimization on training set. Besides the quality of BEV feature, the performance of this model also highly relies on the capacity of initial map query distribution. However, this distribution is limited because the limited query number. To make map predictions optimal on each test sample, it is essential to generate a suitable initial distribution for each specific scenario. This paper proposes to decompose the whole HD map distribution into a set of point representations, namely map query bank (MQBank). To build specific map query initial distributions of different scenarios, low-cost standard definition map (SD map) data is introduced as a kind of prior knowledge. Moreover, each layer of map decoder network learns instance-level map query features, which will lose detailed information of each point. However, BEV feature map is a point-level dense feature. It is important to keep point-level information in map queries when interacting with BEV feature map. This can also be solved with map query bank method. Final experiments show a new insight on SD map prior and a new record on OpenLaneV2 benchmark with 40.5%, 45.7% mAP on vehicle lane and pedestrian area.
Concept-based Rubrics Improve LLM Formative Assessment and Data Synthesis
Venue: arXiv cs.LG 2025
Year: 2025

Formative assessment in STEM topics aims to promote student learning by identifying students' current understanding, thus targeting how to promote further learning. Previous studies suggest that the assessment performance of current generative large language models (LLMs) on constructed responses to open-ended questions is significantly lower than that of supervised classifiers trained on high-quality labeled data. However, we demonstrate that concept-based rubrics can significantly enhance LLM performance, which narrows the gap between LLMs as off-the shelf assessment tools, and smaller supervised models, which need large amounts of training data. For datasets where concept-based rubrics allow LLMs to achieve strong performance, we show that the concept-based rubrics help the same LLMs generate high quality synthetic data for training lightweight, high-performance supervised models. Our experiments span diverse STEM student response datasets with labels of varying quality, including a new real-world dataset that contains some AI-assisted responses, which introduces additional considerations.
Improving Front-end Performance through Modular Rendering and Adaptive Hydration (MRAH) in React Applications
Venue: arXiv cs.SE 2025
Year: 2025

Modern web applications increasingly leverage server-side rendering (SSR) to improve initial load times and search engine optimization. However, the subsequent hydration process-where client-side JavaScript attaches interactivity to SSR-delivered HTML-can introduce performance bottlenecks. We propose a novel architectural pattern combining a modular rendering pipeline with an adaptive hydration strategy to optimize frontend performance in React and Next.js applications. The approach breaks the interface into distinct modules that can be rendered and hydrated independently (inspired by the "islands" paradigm), and it adaptively prioritizes or defers hydration of these modules based on device capabilities, network conditions, and component importance. We integrate techniques such as code-splitting with dynamic import(), conditional hydration triggers (e.g., on visibility or idle time) using libraries like react-lazy-hydration, and adaptive loading hooks to tailor the hydration process to the user's context. By reducing the amount of JavaScript executed on page load and by scheduling hydration work intelligently, this architecture aims to improve key performance metrics-including First Input Delay (FID) and Time to Interactive (TTI)-without sacrificing rich interactivity. We describe the architecture and implementation in a Next.js environment, discuss how components can be conditionally hydrated or entirely skipped when not needed, and compare our approach to related work in progressive hydration, partial hydration, and React Server Components. Evaluation of the approach is left for future work. This pattern offers a pathway to building highly interactive yet performant React applications through careful orchestration of rendering and hydration.
RF-BayesPhysNet: A Bayesian rPPG Uncertainty Estimation Method for Complex Scenarios
Venue: arXiv cs.LG 2025
Year: 2025

Remote photoplethysmography (rPPG) technology infers heart rate by capturing subtle color changes in facial skin using a camera, demonstrating great potential in non-contact heart rate measurement. However, measurement accuracy significantly decreases in complex scenarios such as lighting changes and head movements compared to ideal laboratory conditions. Existing deep learning models often neglect the quantification of measurement uncertainty, limiting their credibility in dynamic scenes. To address the issue of insufficient rPPG measurement reliability in complex scenarios, this paper introduces Bayesian neural networks to the rPPG field for the first time, proposing the Robust Fusion Bayesian Physiological Network (RF-BayesPhysNet), which can model both aleatoric and epistemic uncertainty. It leverages variational inference to balance accuracy and computational efficiency. Due to the current lack of uncertainty estimation metrics in the rPPG field, this paper also proposes a new set of methods, using Spearman correlation coefficient, prediction interval coverage, and confidence interval width, to measure the effectiveness of uncertainty estimation methods under different noise conditions. Experiments show that the model, with only double the parameters compared to traditional network models, achieves a MAE of 2.56 on the UBFC-RPPG dataset, surpassing most models. It demonstrates good uncertainty estimation capability in no-noise and low-noise conditions, providing prediction confidence and significantly enhancing robustness in real-world applications. We have open-sourced the code at https://github.com/AIDC-rPPG/RF-Net
Distillation and Refinement of Reasoning in Small Language Models for Document Re-ranking
Venue: arXiv cs.IR 2025
Year: 2025

We present a novel approach for training small language models for reasoning-intensive document ranking that combines knowledge distillation with reinforcement learning optimization. While existing methods often rely on expensive human annotations or large black-box language models, our methodology leverages web data and a teacher LLM to automatically generate high-quality training examples with relevance explanations. By framing document ranking as a reinforcement learning problem and incentivizing explicit reasoning capabilities, we train a compact 3B parameter language model that achieves state-of-the-art performance on the BRIGHT benchmark. Our model ranks third on the leaderboard while using substantially fewer parameters than other approaches, outperforming models that are over 20 times larger. Through extensive experiments, we demonstrate that generating explanations during inference, rather than directly predicting relevance scores, enables more effective reasoning with smaller language models. The self-supervised nature of our method offers a scalable and interpretable solution for modern information retrieval systems.
GREATERPROMPT: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization
Venue: arXiv cs.LG 2025
Year: 2025

LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significantly dependent on the quality and structure of the input prompts, making prompt design a critical factor for their performance. Recent advancements in automated prompt optimization have introduced diverse techniques that automatically enhance prompts to better align model outputs with user expectations. However, these methods often suffer from the lack of standardization and compatibility across different techniques, limited flexibility in customization, inconsistent performance across model scales, and they often exclusively rely on expensive proprietary LLM APIs. To fill in this gap, we introduce GREATERPROMPT, a novel framework that democratizes prompt optimization by unifying diverse methods under a unified, customizable API while delivering highly effective prompts for different tasks. Our framework flexibly accommodates various model scales by leveraging both text feedback-based optimization for larger LLMs and internal gradient-based optimization for smaller models to achieve powerful and precise prompt improvements. Moreover, we provide a user-friendly Web UI that ensures accessibility for non-expert users, enabling broader adoption and enhanced performance across various user groups and application scenarios. GREATERPROMPT is available at https://github.com/psunlpgroup/GreaterPrompt via GitHub, PyPI, and web user interfaces.
V-CEM: Bridging Performance and Intervenability in Concept-based Models
Venue: arXiv cs.LG 2025
Year: 2025

Concept-based eXplainable AI (C-XAI) is a rapidly growing research field that enhances AI model interpretability by leveraging intermediate, human-understandable concepts. This approach not only enhances model transparency but also enables human intervention, allowing users to interact with these concepts to refine and improve the model's performance. Concept Bottleneck Models (CBMs) explicitly predict concepts before making final decisions, enabling interventions to correct misclassified concepts. While CBMs remain effective in Out-Of-Distribution (OOD) settings with intervention, they struggle to match the performance of black-box models. Concept Embedding Models (CEMs) address this by learning concept embeddings from both concept predictions and input data, enhancing In-Distribution (ID) accuracy but reducing the effectiveness of interventions, especially in OOD scenarios. In this work, we propose the Variational Concept Embedding Model (V-CEM), which leverages variational inference to improve intervention responsiveness in CEMs. We evaluated our model on various textual and visual datasets in terms of ID performance, intervention responsiveness in both ID and OOD settings, and Concept Representation Cohesiveness (CRC), a metric we propose to assess the quality of the concept embedding representations. The results demonstrate that V-CEM retains CEM-level ID performance while achieving intervention effectiveness similar to CBM in OOD settings, effectively reducing the gap between interpretability (intervention) and generalization (performance).
Virtual Reality Lensing for Surface Approximation in Feature-driven DVR
Venue: arXiv cs.GR 2025
Year: 2025

We present a novel lens technique to support the identification of heterogeneous features in direct volume rendering (DVR) visualizations. In contrast to data-centric transfer function (TF) design, our image-driven approach enables users to specify target features directly within the visualization using deformable quadric surfaces. The lens leverages quadrics for their expressive yet simple parametrization, enabling users to sculpt feature approximations by composing multiple quadric lenses. By doing so, the lens offers greater versatility than traditional rigid-shape lenses for selecting and bringing into focus features with irregular geometry. We discuss the lens visualization and interaction design, advocating for bimanual spatial virtual reality (VR) input for reducing cognitive and physical strain. We also report findings from a pilot qualitative evaluation with a domain specialist using a public asteroid impact dataset. These insights not only shed light on the benefits and pitfalls of using deformable lenses but also suggest directions for future research.
I Can Hear You Coming: RF Sensing for Uncooperative Satellite Evasion
Venue: arXiv cs.RO 2025
Year: 2025

This work presents a novel method for leveraging intercepted Radio Frequency (RF) signals to inform a constrained Reinforcement Learning (RL) policy for robust control of a satellite operating in contested environments. Uncooperative satellite engagements with nation-state actors prompts the need for enhanced maneuverability and agility on-orbit. However, robust, autonomous and rapid adversary avoidance capabilities for the space environment is seldom studied. Further, the capability constrained nature of many space vehicles does not afford robust space situational awareness capabilities that can be used for well informed maneuvering. We present a "Cat & Mouse" system for training optimal adversary avoidance algorithms using RL. We propose the novel approach of utilizing intercepted radio frequency communication and dynamic spacecraft state as multi-modal input that could inform paths for a mouse to outmaneuver the cat satellite. Given the current ubiquitous use of RF communications, our proposed system can be applicable to a diverse array of satellites. In addition to providing a comprehensive framework for training and implementing a constrained RL policy capable of providing control for robust adversary avoidance, we also explore several optimization based methods for adversarial avoidance. These methods were then tested on real-world data obtained from the Space Surveillance Network (SSN) to analyze the benefits and limitations of different avoidance methods.
Intrinsic Verification of Parsers and Formal Grammar Theory in Dependent Lambek Calculus (Extended Version)
Venue: arXiv cs.PL 2025
Year: 2025

We present Dependent Lambek Calculus, a domain-specific dependent type theory for verified parsing and formal grammar theory. In $\textrm{Lambek}^D$, linear types are used as a syntax for formal grammars,and parsers can be written as linear terms. The linear typing restriction provides a form of intrinsic verification that a parser yields only valid parse trees for the input string. We demonstrate the expressivity of this system by showing that the combination of inductive linear types and dependency on non-linear data can be used to encode commonly used grammar formalisms such as regular and context-free grammars as well as traces of various types of automata. Using these encodings, we define parsers for regular expressions using deterministic automata, as well as examples of verified parsers of context-free grammars. We present a denotational semantics of our type theory that interprets the linear types as functions from strings to sets of abstract parse trees and terms as parse transformers. Based on this denotational semantics, we have made a prototype implementation of $\textrm{Lambek}^D$ using a shallow embedding in the Agda proof assistant. All of our examples parsers have been implemented in this prototype implementation.
Detection-Friendly Nonuniformity Correction: A Union Framework for Infrared UAVTarget Detection
Venue: arXiv cs.CV 2025
Year: 2025

Infrared unmanned aerial vehicle (UAV) images captured using thermal detectors are often affected by temperature dependent low-frequency nonuniformity, which significantly reduces the contrast of the images. Detecting UAV targets under nonuniform conditions is crucial in UAV surveillance applications. Existing methods typically treat infrared nonuniformity correction (NUC) as a preprocessing step for detection, which leads to suboptimal performance. Balancing the two tasks while enhancing detection beneficial information remains challenging. In this paper, we present a detection-friendly union framework, termed UniCD, that simultaneously addresses both infrared NUC and UAV target detection tasks in an end-to-end manner. We first model NUC as a small number of parameter estimation problem jointly driven by priors and data to generate detection-conducive images. Then, we incorporate a new auxiliary loss with target mask supervision into the backbone of the infrared UAV target detection network to strengthen target features while suppressing the background. To better balance correction and detection, we introduce a detection-guided self-supervised loss to reduce feature discrepancies between the two tasks, thereby enhancing detection robustness to varying nonuniformity levels. Additionally, we construct a new benchmark composed of 50,000 infrared images in various nonuniformity types, multi-scale UAV targets and rich backgrounds with target annotations, called IRBFD. Extensive experiments on IRBFD demonstrate that our UniCD is a robust union framework for NUC and UAV target detection while achieving real-time processing capabilities. Dataset can be available at https://github.com/IVPLaboratory/UniCD.