OpenRaiser/Intern-Atlas huggingface dataset:
An Exploratory Study on Human-Robot Interaction using Semantics-based Situational Awareness
Venue: arXiv cs.RO 2025
Year: 2025

In this paper, we investigate the impact of high-level semantics (evaluation of the environment) on Human-Robot Teams (HRT) and Human-Robot Interaction (HRI) in the context of mobile robot deployments. Although semantics has been widely researched in AI, how high-level semantics can benefit the HRT paradigm is underexplored, often fuzzy, and intractable. We applied a semantics-based framework that could reveal different indicators of the environment (i.e. how much semantic information exists) in a mock-up disaster response mission. In such missions, semantics are crucial as the HRT should handle complex situations and respond quickly with correct decisions, where humans might have a high workload and stress. Especially when human operators need to shift their attention between robots and other tasks, they will struggle to build Situational Awareness (SA) quickly. The experiment suggests that the presented semantics: 1) alleviate the perceived workload of human operators; 2) increase the operator's trust in the SA; and 3) help to reduce the reaction time in switching the level of autonomy when needed. Additionally, we find that participants with higher trust in the system are encouraged by high-level semantics to use teleoperation mode more.
CAPRI-CT: Causal Analysis and Predictive Reasoning for Image Quality Optimization in Computed Tomography
Venue: arXiv cs.CV 2025
Year: 2025

In computed tomography (CT), achieving high image quality while minimizing radiation exposure remains a key clinical challenge. This paper presents CAPRI-CT, a novel causal-aware deep learning framework for Causal Analysis and Predictive Reasoning for Image Quality Optimization in CT imaging. CAPRI-CT integrates image data with acquisition metadata (such as tube voltage, tube current, and contrast agent types) to model the underlying causal relationships that influence image quality. An ensemble of Variational Autoencoders (VAEs) is employed to extract meaningful features and generate causal representations from observational data, including CT images and associated imaging parameters. These input features are fused to predict the Signal-to-Noise Ratio (SNR) and support counterfactual inference, enabling what-if simulations, such as changes in contrast agents (types and concentrations) or scan parameters. CAPRI-CT is trained and validated using an ensemble learning approach, achieving strong predictive performance. By facilitating both prediction and interpretability, CAPRI-CT provides actionable insights that could help radiologists and technicians design more efficient CT protocols without repeated physical scans. The source code and dataset are publicly available at https://github.com/SnehaGeorge22/capri-ct.
SRMambaV2: Biomimetic Attention for Sparse Point Cloud Upsampling in Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Upsampling LiDAR point clouds in autonomous driving scenarios remains a significant challenge due to the inherent sparsity and complex 3D structures of the data. Recent studies have attempted to address this problem by converting the complex 3D spatial scenes into 2D image super-resolution tasks. However, due to the sparse and blurry feature representation of range images, accurately reconstructing detailed and complex spatial topologies remains a major difficulty. To tackle this, we propose a novel sparse point cloud upsampling method named SRMambaV2, which enhances the upsampling accuracy in long-range sparse regions while preserving the overall geometric reconstruction quality. Specifically, inspired by human driver visual perception, we design a biomimetic 2D selective scanning self-attention (2DSSA) mechanism to model the feature distribution in distant sparse areas. Meanwhile, we introduce a dual-branch network architecture to enhance the representation of sparse features. In addition, we introduce a progressive adaptive loss (PAL) function to further refine the reconstruction of fine-grained details during the upsampling process. Experimental results demonstrate that SRMambaV2 achieves superior performance in both qualitative and quantitative evaluations, highlighting its effectiveness and practical value in automotive sparse point cloud upsampling tasks.
When and Where Localization Fails: An Analysis of the Iterative Closest Point in Evolving Environment
Venue: arXiv cs.RO 2025
Year: 2025

Robust relocalization in dynamic outdoor environments remains a key challenge for autonomous systems relying on 3D lidar. While long-term localization has been widely studied, short-term environmental changes, occurring over days or weeks, remain underexplored despite their practical significance. To address this gap, we present a highresolution, short-term multi-temporal dataset collected weekly from February to April 2025 across natural and semi-urban settings. Each session includes high-density point cloud maps, 360 deg panoramic images, and trajectory data. Projected lidar scans, derived from the point cloud maps and modeled with sensor-accurate occlusions, are used to evaluate alignment accuracy against the ground truth using two Iterative Closest Point (ICP) variants: Point-to-Point and Point-to-Plane. Results show that Point-to-Plane offers significantly more stable and accurate registration, particularly in areas with sparse features or dense vegetation. This study provides a structured dataset for evaluating short-term localization robustness, a reproducible framework for analyzing scan-to-map alignment under noise, and a comparative evaluation of ICP performance in evolving outdoor environments. Our analysis underscores how local geometry and environmental variability affect localization success, offering insights for designing more resilient robotic systems.
Bounds and Equivalence of Skew Polycyclic Codes over Finite Fields
Venue: arXiv cs.IT 2025
Year: 2025

We study skew polycyclic codes over a finite field $\mathbb{F}_q$, associated with a skew polynomial $f(x) \in \mathbb{F}_q[x;σ]$, where $σ$ is an automorphism of $\mathbb{F}_q$. We start by proving the Roos-like bound for both the Hamming and the rank metric for this class of codes. Next, we focus on the Hamming and rank equivalence between two classes of polycyclic codes by introducing an equivalence relation and describing its equivalence classes. Finally, we present examples that illustrate applications of the theory developed in this paper.
Dual-branch Prompting for Multimodal Machine Translation
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Machine Translation (MMT) typically enhances text-only translation by incorporating aligned visual features. Despite the remarkable progress, state-of-the-art MMT approaches often rely on paired image-text inputs at inference and are sensitive to irrelevant visual noise, which limits their robustness and practical applicability. To address these issues, we propose D2P-MMT, a diffusion-based dual-branch prompting framework for robust vision-guided translation. Specifically, D2P-MMT requires only the source text and a reconstructed image generated by a pre-trained diffusion model, which naturally filters out distracting visual details while preserving semantic cues. During training, the model jointly learns from both authentic and reconstructed images using a dual-branch prompting strategy, encouraging rich cross-modal interactions. To bridge the modality gap and mitigate training-inference discrepancies, we introduce a distributional alignment loss that enforces consistency between the output distributions of the two branches. Extensive experiments on the Multi30K dataset demonstrate that D2P-MMT achieves superior translation performance compared to existing state-of-the-art approaches.
PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

While end-to-end autonomous driving models show promising results, their practical deployment is often hindered by large model sizes, a reliance on expensive LiDAR sensors and computationally intensive BEV feature representations. This limits their scalability, especially for mass-market vehicles equipped only with cameras. To address these challenges, we propose PRIX (Plan from Raw Pixels). Our novel and efficient end-to-end driving architecture operates using only camera data, without explicit BEV representation and forgoing the need for LiDAR. PRIX leverages a visual feature extractor coupled with a generative planning head to predict safe trajectories from raw pixel inputs directly. A core component of our architecture is the Context-aware Recalibration Transformer (CaRT), a novel module designed to effectively enhance multi-level visual features for more robust planning. We demonstrate through comprehensive experiments that PRIX achieves state-of-the-art performance on the NavSim and nuScenes benchmarks, matching the capabilities of larger, multimodal diffusion planners while being significantly more efficient in terms of inference speed and model size, making it a practical solution for real-world deployment. Our work is open-source and the code will be at https://maxiuw.github.io/prix.
Human-AI Collaboration and Explainability for 2D/3D Registration Quality Assurance
Venue: arXiv cs.HC 2025
Year: 2025

Purpose: As surgery increasingly integrates advanced imaging, algorithms, and robotics to automate complex tasks, human judgment of system correctness remains a vital safeguard for patient safety. A critical example is 2D/3D registration, where small registration misalignments can lead to surgical errors. Current visualization strategies alone are insufficient to reliably enable humans to detect these misalignments, highlighting the need for enhanced decision-support tools. Methods: We propose the first artificial intelligence (AI) model tailored to 2D/3D registration quality assessment, augmented with explainable AI (XAI) mechanisms to clarify the model's predictions. Using both objective measures (e.g., accuracy, sensitivity, precision, specificity) and subjective evaluations (e.g., workload, trust, and understanding), we systematically compare decision-making across four conditions: AI-only, Human-only, Human+AI, and Human+XAI. Results: The AI-only condition achieved the highest accuracy, whereas collaborative paradigms (Human+AI and Human+XAI) improved sensitivity, precision, and specificity compared to standalone approaches. Participants experienced significantly lower workload in collaborative conditions relative to the Human-only condition. Moreover, participants reported a greater understanding of AI predictions in the Human+XAI condition than in Human+AI, although no significant differences were observed between the two collaborative paradigms in perceived trust or workload. Conclusion: Human-AI collaboration can enhance 2D/3D registration quality assurance, with explainability mechanisms improving user understanding. Future work should refine XAI designs to optimize decision-making performance and efficiency. Extending both the algorithmic design and human-XAI collaboration elements holds promise for more robust quality assurance of 2D/3D registration.
The Early Bird Identifies the Worm: You Can't Beat a Head Start in Long-Term Body Re-ID (ECHO-BID)
Venue: arXiv cs.CV 2025
Year: 2025

A wide range of model-based approaches to long-term person re-identification have been proposed. Whether these models perform more accurately than direct domain transfer learning applied to extensively trained large-scale foundation models is not known. We applied domain transfer learning for long-term person re-id to four vision foundation models (CLIP, DINOv2, AIMv2, and EVA-02). Domain-adapted versions of all four models %CLIP-L, DINOv2-L, AIMv2-L, and EVA-02-L surpassed existing state-of-the-art models by a large margin in highly unconstrained viewing environments. Decision score fusion of the four models improved performance over any individual model. Of the individual models, the EVA-02 foundation model provided the best ``head start'' to long-term re-id, surpassing other models on three of the four performance metrics by substantial margins. Accordingly, we introduce $\textbf{E}$va $\textbf{C}$lothes-Change from $\textbf{H}$idden $\textbf{O}$bjects - $\textbf{B}$ody $\textbf{ID}$entification (ECHO-BID), a class of long-term re-id models built on the object-pretrained EVA-02 Large backbones. Ablation experiments varying backbone size, scale of object classification pretraining, and transfer learning protocol indicated that model size and the use of a smaller, but more challenging transfer learning protocol are critical features in performance. We conclude that foundation models provide a head start to domain transfer learning and support state-of-the-art performance with modest amounts of domain data. The limited availability of long-term re-id data makes this approach advantageous.
TyDi QA-WANA: A Benchmark for Information-Seeking Question Answering in Languages of West Asia and North Africa
Venue: arXiv cs.CL 2025
Year: 2025

We present TyDi QA-WANA, a question-answering dataset consisting of 28K examples divided among 10 language varieties of western Asia and northern Africa. The data collection process was designed to elicit information-seeking questions, where the asker is genuinely curious to know the answer. Each question in paired with an entire article that may or may not contain the answer; the relatively large size of the articles results in a task suitable for evaluating models' abilities to utilize large text contexts in answering questions. Furthermore, the data was collected directly in each language variety, without the use of translation, in order to avoid issues of cultural relevance. We present performance of two baseline models, and release our code and data to facilitate further improvement by the research community.
Co-constructing Explanations for AI Systems using Provenance
Venue: arXiv cs.HC 2025
Year: 2025

Modern AI systems are complex workflows containing multiple components and data sources. Data provenance provides the ability to interrogate and potentially explain the outputs of these systems. However, provenance is often too detailed and not contextualized for the user trying to understand the AI system. In this work, we present our vision for an interactive agent that works together with the user to co-construct an explanation that is simultaneously useful to the user as well as grounded in data provenance. To illustrate this vision, we present: 1) an initial prototype of such an agent; and 2) a scalable evaluation framework based on user simulations and a large language model as a judge approach.
Flexible Vector Integration in Embedded RISC-V SoCs for End to End CNN Inference Acceleration
Venue: arXiv cs.DC 2025
Year: 2025

The emergence of heterogeneity and domain-specific architectures targeting deep learning inference show great potential for enabling the deployment of modern CNNs on resource-constrained embedded platforms. A significant development is the diversification of custom hardware solely targeting the most expensive parts of CNNs. DLAs (deep learning accelerators) and NPUs (neural processing units), among others, can overcome the approaching limits of traditional silicon scaling and provide a solution to the power/performance tradeoff within embedded SoCs. Efficient DSA utilization requires proper system integration and a compilation/execution model for balanced execution in these heterogeneous architectures. There is a critical need for proper system integration and an efficient compilation/execution model for balanced execution in these heterogeneous architectures. This work highlights the hardware integration challenges for efficiently placing these units within the memory hierarchy and correct proximity to other execution blocks. We experimentally verify performance bottlenecks in CNN execution and pre/post-processing at runtime, where previous attention has generally been given to accelerator speedup alone. This work takes advantage of the ratification of the RISC-V Vector 1.0 extension and demonstrates its potential as a flexible target within a well-suited cache hierarchy scheme to reduce pre-processing bottlenecks and CPU fallback processes. Our results show up to a 9x speedup of image pre-processing and YOLOv3 fallback layer execution by up to 3x compared to CPU. We demonstrate RVV-1.0 in exposing a flexible programming model that can enable a balanced computation and memory footprint on accelerator-rich embedded SoCs supporting modern deep-learning dataflows while consuming less power than traditional parallel execution platforms.
GenSelect: A Generative Approach to Best-of-N
Venue: arXiv cs.LG 2025
Year: 2025

Generative reward models with parallel sampling have enabled effective test-time scaling for reasoning tasks. Current approaches employ pointwise scoring of individual solutions or pairwise comparisons. However, pointwise methods underutilize LLMs' comparative abilities, while pairwise methods scale inefficiently with larger sampling budgets. We introduce GenSelect, where the LLM uses long reasoning to select the best solution among N candidates. This leverages LLMs' comparative strengths while scaling efficiently across parallel sampling budgets. For math reasoning, we demonstrate that reasoning models, such as QwQ and DeepSeek-R1-0528, excel at GenSelect, outperforming existing scoring approaches with simple prompting.
Wasserstein GAN-Based Precipitation Downscaling with Optimal Transport for Enhancing Perceptual Realism
Venue: arXiv cs.LG 2025
Year: 2025

High-resolution (HR) precipitation prediction is essential for reducing damage from stationary and localized heavy rainfall; however, HR precipitation forecasts using process-driven numerical weather prediction models remains challenging. This study proposes using Wasserstein Generative Adversarial Network (WGAN) to perform precipitation downscaling with an optimal transport cost. In contrast to a conventional neural network trained with mean squared error, the WGAN generated visually realistic precipitation fields with fine-scale structures even though the WGAN exhibited slightly lower performance on conventional evaluation metrics. The learned critic of WGAN correlated well with human perceptual realism. Case-based analysis revealed that large discrepancies in critic scores can help identify both unrealistic WGAN outputs and potential artifacts in the reference data. These findings suggest that the WGAN framework not only improves perceptual realism in precipitation downscaling but also offers a new perspective for evaluating and quality-controlling precipitation datasets.
Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications
Venue: arXiv cs.NI 2025
Year: 2025

In future AI-native wireless networks, the presence of mismatches between the latent spaces of independently designed and trained deep neural network (DNN) encoders may impede mutual understanding due to the emergence of semantic channel noise. This undermines the receiver's ability to interpret transmitted representations, thereby reducing overall system performance. To address this issue, we propose the Parseval Frame Equalizer (PFE), a zero-shot, frame-based semantic channel equalizer that aligns latent spaces of heterogeneous encoders without requiring system retraining. PFE enables dynamic signal compression and expansion, mitigating semantic noise while preserving performance on downstream tasks. Building on this capability, we introduce a dynamic optimization strategy that coordinates communication, computation, and learning resources to balance energy consumption, end-to-end (E2E) latency, and task performance in multi-agent semantic communication scenarios. Extensive simulations confirm the effectiveness of our approach in maintaining semantic consistency and meeting long-term constraints on latency and accuracy under diverse and time-varying network conditions.
PinchBot: Long-Horizon Deformable Manipulation with Guided Diffusion Policy
Venue: arXiv cs.RO 2025
Year: 2025

Pottery creation is a complicated art form that requires dexterous, precise and delicate actions to slowly morph a block of clay to a meaningful, and often useful 3D goal shape. In this work, we aim to create a robotic system that can create simple pottery goals with only pinch-based actions. This pinch pottery task allows us to explore the challenges of a highly multi-modal and long-horizon deformable manipulation task. To this end, we present PinchBot, a goal-conditioned diffusion policy model that when combined with pre-trained 3D point cloud embeddings, task progress prediction and collision-constrained action projection, is able to successfully create a variety of simple pottery goals. For experimental videos and access to the demonstration dataset, please visit our project website: https://sites.google.com/andrew.cmu.edu/pinchbot/home.
Performance Evaluation and Threat Mitigation in Large-scale 5G Core Deployment
Venue: arXiv cs.CR 2025
Year: 2025

The deployment of large-scale software-based 5G core functions presents significant challenges due to their reliance on optimized and intelligent resource provisioning for their services. Many studies have focused on analyzing the impact of resource allocation for complex deployments using mathematical models, queue theories, or even Artificial Intelligence (AI). This paper elucidates the effects of chaotic workloads, generated by Distributed Denial of Service (DDoS) on different Network Functions (NFs) on User Equipment registration performance. Our findings highlight the necessity of diverse resource profiles to ensure Service-Level Agreement (SLA) compliance in large-scale 5G core deployments. Additionally, our analysis of packet capture approaches demonstrates the potential of kernel-based monitoring for scalable security threat defense. Finally, our empirical evaluation provides insights into the effective deployment of 5G NFs in complex scenarios.
Talk with the Things: Integrating LLMs into IoT Networks
Venue: arXiv cs.NI 2025
Year: 2025

The convergence of Large Language Models (LLMs) and Internet of Things (IoT) networks open new opportunities for building intelligent, responsive, and user-friendly systems. This work presents an edge-centric framework that integrates LLMs into IoT architectures to enable natural language-based control, context-aware decision-making, and enhanced automation. The proposed modular and lightweight Retrieval Augmented Generation (RAG)-based LLMs are deployed on edge computing devices connected to IoT gateways, enabling local processing of user commands and sensor data for reduced latency, improved privacy, and enhanced inference quality. We validate the framework through a smart home prototype using LLaMA 3 and Gemma 2B models for controlling smart devices. Experimental results highlight the trade-offs between model accuracy and inference time with respect to models size. At last, we also discuss the potential applications that can use LLM-based IoT systems, and a few key challenges associated with such systems.