OpenRaiser/Intern-Atlas huggingface dataset:
Large language models streamline automated systematic review: A preliminary study
Venue: arXiv cs.IR 2025
Year: 2025

Large Language Models (LLMs) have shown promise in natural language processing tasks, with the potential to automate systematic reviews. This study evaluates the performance of three state-of-the-art LLMs in conducting systematic review tasks. We assessed GPT-4, Claude-3, and Mistral 8x7B across four systematic review tasks: study design formulation, search strategy development, literature screening, and data extraction. Sourced from a previously published systematic review, we provided reference standard including standard PICO (Population, Intervention, Comparison, Outcome) design, standard eligibility criteria, and data from 20 reference literature. Three investigators evaluated the quality of study design and eligibility criteria using 5-point Liker Scale in terms of accuracy, integrity, relevance, consistency and overall performance. For other tasks, the output is defined as accurate if it is the same as the reference standard. Search strategy performance was evaluated through accuracy and retrieval efficacy. Screening accuracy was assessed for both abstracts screening and full texts screening. Data extraction accuracy was evaluated across 1,120 data points comprising 3,360 individual fields. Claude-3 demonstrated superior overall performance in PICO design. In search strategy formulation, GPT-4 and Claude-3 achieved comparable accuracy, outperforming Mistral. For abstract screening, GPT-4 achieved the highest accuracy, followed by Mistral and Claude-3. In data extraction, GPT-4 significantly outperformed other models. LLMs demonstrate potential for automating systematic review tasks, with GPT-4 showing superior performance in search strategy formulation, literature screening and data extraction. These capabilities make them promising assistive tools for researchers and warrant further development and validation in this field.
Improving Residential Safety by Multiple Sensors on Multiple Nodes for Joint Emergency Detection
Venue: arXiv cs.NI 2025
Year: 2025

Recent advances in low-cost microcontrollers have enabled innovative smart home applications. However, existing systems typically consist of single-purpose devices that only report sensed data to a controller. Given the potential for residential emergencies, we propose to integrate emergency detection systems into smart home environments. We present an ad-hoc distributed sensor network (DSN) designed to detect five common residential emergencies: fires, gas and water leakages, earthquakes, and intrusions. Our novel approach combines diverse sensors with a voting-based consensus algorithm among multiple nodes, improving accuracy and reliability over traditional alert systems. The consensus algorithm employs a majority rule with weighted votes, allowing adjustments for various scenarios. An experimental evaluation confirms our approach's effectiveness in accurately detecting emergencies while demonstrating reliability in mitigating node failures, ensuring system longevity, and maintaining robust communication. Additionally, our approach significantly reduces power consumption compared to alternatives.
Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers
Venue: arXiv cs.IR 2025
Year: 2025

Accessing accurate medication insights is vital for enhancing patient safety, minimizing errors, and supporting clinical decision-making. However, healthcare professionals in Africa often rely on manual and time-consuming processes to retrieve drug information, exacerbated by limited access to pharmacists due to brain drain and healthcare disparities. This paper presents "Drug Insights," an open-source Retrieval-Augmented Generation (RAG) chatbot designed to streamline medication lookup for healthcare workers in Africa. By leveraging a corpus of Nigerian pharmaceutical data and advanced AI technologies, including Pinecone databases and GPT models, the system delivers accurate, context-specific responses with minimal hallucination. The chatbot integrates prompt engineering and S-BERT evaluation to optimize retrieval and response generation. Preliminary tests, including pharmacist feedback, affirm the tool's potential to improve drug information access while highlighting areas for enhancement, such as UI/UX refinement and extended corpus integration.
Town Hall Debate Prompting: Enhancing Logical Reasoning in LLMs through Multi-Persona Interaction
Venue: arXiv cs.CL 2025
Year: 2025

Debate is a commonly used form of human communication catered towards problem-solving because of its efficiency. Debate fundamentally allows multiple viewpoints to be brought up in problem-solving, and for complex problems, each viewpoint opens a new path for problem-solving. In this work, we apply this concept to LLM decision-making by proposing town hall-style debate prompting (THDP), a prompting method that splices a language model into multiple personas that will debate one another to reach a conclusion. Our experimental pipeline varies both the number of personas and the personality types of each persona to find the optimum town hall size and personality for benchmark performance as measured by ZebraLogic bench, a reasoning-intensive benchmark characterized by both multiple-choice and fill-in-the-blank questions. Our experimental results demonstrate that a town hall size of 5 personas with LLM-determined personality types performs optimally on ZebraLogic, achieving a 13\% improvement over one-shot CoT baselines in per-cell accuracy in GPT-4o, 9% puzzle accuracy increase in Claude 3.5 Sonnet, and an improvement in hard puzzle accuracy from 10-15%.
A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications
Venue: arXiv cs.DC 2025
Year: 2025

Advancements in embedded systems and Artificial Intelligence (AI) have enhanced the capabilities of Unmanned Aircraft Vehicles (UAVs) in computer vision. However, the integration of AI techniques o-nboard drones is constrained by their processing capabilities. In this sense, this study evaluates the deployment of object detection models (YOLOv8n and YOLOv8s) on both resource-constrained edge devices and cloud environments. The objective is to carry out a comparative performance analysis using a representative real-time UAV image processing pipeline. Specifically, the NVIDIA Jetson Orin Nano, Orin NX, and Raspberry Pi 5 (RPI5) devices have been tested to measure their detection accuracy, inference speed, and energy consumption, and the effects of post-training quantization (PTQ). The results show that YOLOv8n surpasses YOLOv8s in its inference speed, achieving 52 FPS on the Jetson Orin NX and 65 fps with INT8 quantization. Conversely, the RPI5 failed to satisfy the real-time processing needs in spite of its suitability for low-energy consumption applications. An analysis of both the cloud-based and edge-based end-to-end processing times showed that increased communication latencies hindered real-time applications, revealing trade-offs between edge (low latency) and cloud processing (quick processing). Overall, these findings contribute to providing recommendations and optimization strategies for the deployment of AI models on UAVs.
AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results
Venue: arXiv cs.DC 2025
Year: 2025

The deployment of large language models (LLMs) on extended reality (XR) devices has great potential to advance the field of human-AI interaction. In the case of direct, on-device model inference, selecting the appropriate model and device for specific tasks remains challenging. In this paper, we present AIvaluateXR, a comprehensive evaluation framework for benchmarking LLMs running on XR devices. To demonstrate the framework, we deploy 17 selected LLMs across four XR platforms: Magic Leap 2, Meta Quest 3, Vivo X100s Pro, and Apple Vision Pro, and conduct an extensive evaluation. Our experimental setup measures four key metrics: performance consistency, processing speed, memory usage, and battery consumption. For each of the 68 model-device pairs, we assess performance under varying string lengths, batch sizes, and thread counts, analyzing the trade-offs for real-time XR applications. We propose a unified evaluation method based on the 3D Pareto Optimality theory to select the optimal device-model pairs from quality and speed objectives. Additionally, we compare the efficiency of on-device LLMs with client-server and cloud-based setups, and evaluate their accuracy on two interactive tasks. We believe our findings offer valuable insight to guide future optimization efforts for LLM deployment on XR devices. Our evaluation method can be used as standard groundwork for further research and development in this emerging field. The source code and supplementary materials are available at: www.nanovis.org/AIvaluateXR.html
Asymptotic evaluation of the information processing capacity in reservoir computing
Venue: arXiv cs.IT 2025
Year: 2025

Reservoir computing (RC) is becoming increasingly important because of its short training time. The squared error normalized by the target output is called the information processing capacity (IPC) and is used to evaluate the performance of an RC system. Since RC aims to learn the relationship between input and output time series, we should evaluate the IPC for infinitely long data rather than the IPC for finite-length data. However, a method for estimating it has not been established. We evaluated the IPC for infinitely long data using the asymptotic expansion of the IPC and weighted least-squares fitting. Then, we showed the validity of our method by numerical simulations. This work makes the performance evaluation of RC more evident.
Rotate, Clip, and Partition: Towards W2A4KV4 Quantization by Integrating Rotation and Learnable Non-uniform Quantizer
Venue: arXiv cs.LG 2025
Year: 2025

We propose Rotate, Clip, and Partition (RCP), a quantization-aware training (QAT) approach that first realizes extreme compression of LLMs with W2A4KV4(2-bit weight, 4-bit activation, and 4-bit KV cache) configuration. RCP integrates recent rotation techniques with a novel non-uniform weight quantizer design, by quantitatively analyzing the impact of random rotation on 2-bit weight quantization. Our weight quantizer features Learnable Direct Partitioning (LDP), which introduces learnable parameters to directly learn non-uniform intervals jointly with LLM weights. We also present a specialized GPU kernel that supports GEMV on non-uniform W2A4. Experiments show that RCP can compress LLaMA-2-7B to W2A4KV4 with a loss of only 2.84 WikiText2 ppl and 5.29 times reduced memory footprint. Furthermore, RCP can quantize challenging mobile-targeted LLaMA-3.2 models and domain-specific WizardCoder-7B and MetaMath-7B with no critical problems such as convergence failure and repetition. Code is available at https://github.com/ songsm921/RCP.
A General Error-Theoretical Analysis Framework for Constructing Compression Strategies
Venue: arXiv cs.LG 2025
Year: 2025

The exponential growth in parameter size and computational complexity of deep models poses significant challenges for efficient deployment. The core problem of existing compression methods is that different layers of the model have significant differences in their tolerance to compression levels. For instance, the first layer of a model can typically sustain a higher compression level compared to the last layer without compromising performance. Thus, the key challenge lies in how to allocate compression levels across layers in a way that minimizes performance loss while maximizing parameter reduction. To address this challenge, we propose a Compression Error Theory (CET) framework, designed to determine the optimal compression level for each layer. Taking quantization as an example, CET leverages differential expansion and algebraic geometry to reconstruct the quadratic form of quantization error as ellipsoids and hyperbolic paraboloids, and utilizes their geometric structures to define an error subspace. To identify the error subspace with minimal performance loss, by performing orthogonal decomposition of the geometric space, CET transforms the optimization process of the error subspace into a complementary problem. The final theoretical analysis shows that constructing the quantization subspace along the major axis results in minimal performance degradation. Through experimental verification of the theory, CET can greatly retain performance while compressing. Specifically, on the ResNet-34 model, CET achieves nearly 11$\times$ parameter compression while even surpassing performance comparable to the original model.
Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles
Venue: arXiv cs.LG 2025
Year: 2025

Accurate prediction of shear strength parameters in Municipal Solid Waste (MSW) remains a critical challenge in geotechnical engineering due to the heterogeneous nature of waste materials and their temporal evolution through degradation processes. This paper presents a novel explainable artificial intelligence (XAI) framework for evaluating cohesion and friction angle across diverse MSW compositional profiles. The proposed model integrates a multi-layer perceptron architecture with SHAP (SHapley Additive exPlanations) analysis to provide transparent insights into how specific waste components influence strength characteristics. Training data encompassed large-scale direct shear tests across various waste compositions and degradation states. The model demonstrated superior predictive accuracy compared to traditional gradient boosting methods, achieving mean absolute percentage errors of 7.42% and 14.96% for friction angle and cohesion predictions, respectively. Through SHAP analysis, the study revealed that fibrous materials and particle size distribution were primary drivers of shear strength variation, with food waste and plastics showing significant but non-linear effects. The model's explainability component successfully quantified these relationships, enabling evidence-based recommendations for waste management practices. This research bridges the gap between advanced machine learning and geotechnical engineering practice, offering a reliable tool for rapid assessment of MSW mechanical properties while maintaining interpretability for engineering decision-making.
Challenges of Multi-Modal Coreset Selection for Depth Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Coreset selection methods are effective in accelerating training and reducing memory requirements but remain largely unexplored in applied multimodal settings. We adapt a state-of-the-art (SoTA) coreset selection technique for multimodal data, focusing on the depth prediction task. Our experiments with embedding aggregation and dimensionality reduction approaches reveal the challenges of extending unimodal algorithms to multimodal scenarios, highlighting the need for specialized methods to better capture inter-modal relationships.
Forecasting Frontier Language Model Agent Capabilities
Venue: arXiv cs.CL 2025
Year: 2025

As Language Models (LMs) increasingly operate as autonomous agents, accurately forecasting their capabilities becomes crucial for societal preparedness. We evaluate six forecasting methods that predict downstream capabilities of LM agents. We use "one-step" approaches that predict benchmark scores from input metrics like compute or model release date directly or "two-step" approaches that first predict an intermediate metric like the principal component of cross-benchmark performance (PC-1) and human-evaluated competitive Elo ratings. We evaluate our forecasting methods by backtesting them on a dataset of 38 LMs from the OpenLLM 2 leaderboard. We then use the validated two-step approach (Release Date$\to$Elo$\to$Benchmark) to predict LM agent performance for frontier models on three benchmarks: SWE-Bench Verified (software development), Cybench (cybersecurity assessment), and RE-Bench (ML research engineering). Our forecast predicts that by the beginning of 2026, non-specialized LM agents with low capability elicitation will reach a success rate of 54% on SWE-Bench Verified, while state-of-the-art LM agents will reach an 87% success rate. Our approach does not account for recent advances in inference-compute scaling and might thus be too conservative.
DiffCheck: a Scan-CAD Evaluation Tool for Digital Manufacturing and Assembly Processes in Timber Construction
Venue: arXiv cs.GR 2025
Year: 2025

In digital timber construction, scanning technologies and point cloud data are widely used due to the accessibility of affordable 3D sensors, photogrammetry, and user-friendly CAD tools. While typically not employed for accuracy checks in timber fabrication due to the precision of standard machinery, experimental research and prototyping with joinery and assembly can benefit from precision and accuracy evaluation tools. We introduce diffCheck, a C++/Python software integrated into Grasshopper to address this need. It uses advanced point cloud analysis to compare scans of fabricated timber structures with their respective CAD models, helping to identify discrepancies. Tested on various timber elements and digital fabrication methods like robotic assembly, AR-assisted woodworking, and CNC machining, diffCheck aims to establish a user-friendly benchmark framework for digital fabrication systems using timber components, with the potential to find applications in other materials. Its source code and the analyzed data are openly shared with the digital fabrication community under a permissive license.
Generative AI Framework for 3D Object Generation in Augmented Reality
Venue: arXiv cs.GR 2025
Year: 2025

This thesis presents a framework that integrates state-of-the-art generative AI models for real-time creation of three-dimensional (3D) objects in augmented reality (AR) environments. The primary goal is to convert diverse inputs, such as images and speech, into accurate 3D models, enhancing user interaction and immersion. Key components include advanced object detection algorithms, user-friendly interaction techniques, and robust AI models like Shap-E for 3D generation. Leveraging Vision Language Models (VLMs) and Large Language Models (LLMs), the system captures spatial details from images and processes textual information to generate comprehensive 3D objects, seamlessly integrating virtual objects into real-world environments. The framework demonstrates applications across industries such as gaming, education, retail, and interior design. It allows players to create personalized in-game assets, customers to see products in their environments before purchase, and designers to convert real-world objects into 3D models for real-time visualization. A significant contribution is democratizing 3D model creation, making advanced AI tools accessible to a broader audience, fostering creativity and innovation. The framework addresses challenges like handling multilingual inputs, diverse visual data, and complex environments, improving object detection and model generation accuracy, as well as loading 3D models in AR space in real-time. In conclusion, this thesis integrates generative AI and AR for efficient 3D model generation, enhancing accessibility and paving the way for innovative applications and improved user interactions in AR environments.
Tight Bounds for some Classical Problems Parameterized by Cutwidth
Venue: arXiv cs.DS 2025
Year: 2025

Cutwidth is a widely studied parameter that quantifies how well a graph can be decomposed along small edge-cuts. It complements pathwidth, which captures decomposition by small vertex separators, and it is well-known that cutwidth upper-bounds pathwidth. The SETH-tight parameterized complexity of problems on graphs of bounded pathwidth (and treewidth) has been actively studied over the past decade while for cutwidth the complexity of many classical problems remained open. For Hamiltonian Cycle, it is known that a $(2+\sqrt{2})^{\operatorname{pw}} n^{O(1)}$ algorithm is optimal for pathwidth under SETH~[Cygan et al.\ JACM 2022]. Van Geffen et al.~[J.\ Graph Algorithms Appl.\ 2020] and Bojikian et al.~[STACS 2023] asked which running time is optimal for this problem parameterized by cutwidth. We answer this question with $(1+\sqrt{2})^{\operatorname{ctw}} n^{O(1)}$ by providing matching upper and lower bounds. Second, as our main technical contribution, we close the gap left by van Heck~[2018] for Partition Into Triangles (and Triangle Packing) by improving both upper and lower bound and getting a tight bound of $\sqrt[3]{3}^{\operatorname{ctw}} n^{O(1)}$, which to our knowledge exhibits the only known tight non-integral basis apart from Hamiltonian Cycle. We show that cuts inducing a disjoint union of paths of length three (unions of so-called $Z$-cuts) lie at the core of the complexity of the problem -- usually lower-bound constructions use simpler cuts inducing either a matching or a disjoint union of bicliques. Finally, we determine the optimal running times for Max Cut ($2^{\operatorname{ctw}} n^{O(1)}$) and Induced Matching ($3^{\operatorname{ctw}} n^{O(1)}$) by providing matching lower bounds for the existing algorithms -- the latter result also answers an open question for treewidth by Chaudhary and Zehavi~[WG 2023].
Likable or Intelligent? Comparing Social Robots and Virtual Agents for Long-term Health Monitoring
Venue: arXiv cs.HC 2025
Year: 2025

Using social robots and virtual agents (VAs) as interfaces for health monitoring systems for older adults offers the possibility of more engaging interactions that can support long-term health and well-being. While robots are characterized by their physical presence, software-based VAs are more scalable and flexible. Few comparisons of these interfaces exist in the human-robot and human-agent interaction domains, especially in long-term and real-world studies. In this work, we examined impressions of social robots and VAs at the beginning and end of an eight-week study in which older adults interacted with these systems independently in their homes. Using a between-subjects design, participants could choose which interface to evaluate during the study. While participants perceived the social robot as somewhat more likable, the VA was perceived as more intelligent. Our work provides a basis for further studies investigating factors most relevant for engaging interactions with social interfaces for long-term health monitoring.
IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning
Venue: arXiv cs.RO 2025
Year: 2025

Planning paths that maximize information gain for robotic platforms has wide-ranging applications and significant potential impact. To effectively adapt to real-time data collection, informative path planning must be computed online and be responsive to new observations. In this work, we present IA-TIGRIS, an incremental and adaptive sampling-based informative path planner designed for real-time onboard execution. Our approach leverages past planning efforts through incremental refinement while continuously adapting to updated belief maps. We additionally present detailed implementation and optimization insights to facilitate real-world deployment, along with an array of reward functions tailored to specific missions and behaviors. Extensive simulation results demonstrate IA-TIGRIS generates higher-quality paths compared to baseline methods. We validate our planner on two distinct hardware platforms: a hexarotor UAV and a fixed-wing UAV, each having different motion models and configuration spaces. Our results show up to a 41% improvement in information gain compared to baseline methods, highlighting the planner's potential for deployment in real-world applications. Project website and video: https://ia-tigris.github.io