OpenRaiser/Intern-Atlas huggingface dataset:
Improving mmWave based Hand Hygiene Monitoring through Beam Steering and Combining Techniques
Venue: arXiv cs.HC 2025
Year: 2025

We introduce BeaMsteerX (BMX), a novel mmWave hand hygiene gesture recognition technique that improves accuracy in longer ranges (1.5m). BMX steers a mmWave beam towards multiple directions around the subject, generating multiple views of the gesture that are then intelligently combined using deep learning to enhance gesture classification. We evaluated BMX using off-the-shelf mmWave radars and collected a total of 7,200 hand hygiene gesture data from 10 subjects performing a six-step hand-rubbing procedure, as recommended by the World Health Organization, using sanitizer, at 1.5m -- over five times longer than in prior works. BMX outperforms state-of-the-art approaches by 31--43% and achieves 91% accuracy at boresight by combining only two beams, demonstrating superior gesture classification in low SNR scenarios. BMX maintained its effectiveness even when the subject was positioned 30 degrees away from the boresight, exhibiting a modest 5% drop in accuracy.
Displacement-Actuated Continuum Robots: A Joint Space Abstraction
Venue: arXiv cs.RO 2025
Year: 2025

The displacement-actuated continuum robot as an abstraction has been shown as a key abstraction to significantly simplify and improve approaches due to its relation to the Clarke transform. To highlight further potentials, we revisit and extend this abstraction that features an increasingly popular length extension and an underutilized twisting. For each extension, the corresponding mapping from the joint values to the local coordinates of the manifold embedded in the joint spaces is provided. Each mapping is characterized by its compactness and linearity.
BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Applying imitation learning (IL) is challenging to nonprehensile manipulation tasks of invisible objects with partial observations, such as excavating buried rocks. The demonstrator must make such complex action decisions as exploring to find the object and task-oriented actions to complete the task while estimating its hidden state, perhaps causing inconsistent action demonstration and high cognitive load problems. For these problems, work in human cognitive science suggests that promoting the use of pre-designed, simple exploration rules for the demonstrator may alleviate the problems of action inconsistency and high cognitive load. Therefore, when performing imitation learning from demonstrations using such exploration rules, it is important to accurately imitate not only the demonstrator's task-oriented behavior but also his/her mode-switching behavior (exploratory or task-oriented behavior) under partial observation. Based on the above considerations, this paper proposes a novel imitation learning framework called Belief Exploration-Action Cloning (BEAC), which has a switching policy structure between a pre-designed exploration policy and a task-oriented action policy trained on the estimated belief states based on past history. In simulation and real robot experiments, we confirmed that our proposed method achieved the best task performance, higher mode and action prediction accuracies, while reducing the cognitive load in the demonstration indicated by a user study.
The Model Hears You: Audio Language Model Deployments Should Consider the Principle of Least Privilege
Venue: arXiv cs.SD 2025
Year: 2025

The latest Audio Language Models (Audio LMs) process speech directly instead of relying on a separate transcription step. This shift preserves detailed information, such as intonation or the presence of multiple speakers, that would otherwise be lost in transcription. However, it also introduces new safety risks, including the potential misuse of speaker identity cues and other sensitive vocal attributes, which could have legal implications. In this paper, we urge a closer examination of how these models are built and deployed. Our experiments show that end-to-end modeling, compared with cascaded pipelines, creates socio-technical safety risks such as identity inference, biased decision-making, and emotion detection. This raises concerns about whether Audio LMs store voiceprints and function in ways that create uncertainty under existing legal regimes. We then argue that the Principle of Least Privilege should be considered to guide the development and deployment of these models. Specifically, evaluations should assess (1) the privacy and safety risks associated with end-to-end modeling; and (2) the appropriate scope of information access. Finally, we highlight related gaps in current audio LM benchmarks and identify key open research questions, both technical and policy-related, that must be addressed to enable the responsible deployment of end-to-end Audio LMs.
Betweenness Centrality Based Dynamic Source Routing for Flying Ad Hoc Networks in Marching Formation
Venue: arXiv cs.NI 2025
Year: 2025

Designing high-performance routing protocols for flying ad hoc networks (FANETs) is challenging due to the diversity of applications and the dynamics of network topology. The existing general-purpose routing protocols for ad hoc networks often oversimplify mobility patterns and disregard the unequal importance of nodes, resulting in suboptimal routing decisions that are unsuitable for task-oriented FANETs. To break the bottleneck, in this paper we propose a betweenness centrality based dynamic source routing (BC-DSR) protocol for a flying ad hoc network (FANET) in marching formation. Firstly, we introduce a Gauss-Markov group (GMG) mobility model based on the leader-follower pattern, which accurately captures the temporal and spatial correlations of node movements in the realistic marching formation. Besides, we exploit the concept of BC defined in graph theory to measure the structural unequal importance of relay nodes, i.e., to determine link weights, in the particular marching formation topology. The path of least cost is calculated relying on a weighted directed graph constructed. The ns-3 based simulation results demonstrate that our BCDSR protocol achieves higher packet-delivery ratio and lower average end-to-end latency and routing overhead ratio than representative benchmark protocols used in FANETs, while maintaining a reasonably small network jitter.
Preferential Multi-Objective Bayesian Optimization for Drug Discovery
Venue: arXiv cs.LG 2025
Year: 2025

Despite decades of advancements in automated ligand screening, large-scale drug discovery remains resource-intensive and requires post-processing hit selection, a step where chemists manually select a few promising molecules based on their chemical intuition. This creates a major bottleneck in the virtual screening process for drug discovery, demanding experts to repeatedly balance complex trade-offs among drug properties across a vast pool of candidates. To improve the efficiency and reliability of this process, we propose a novel human-centered framework named CheapVS that allows chemists to guide the ligand selection process by providing preferences regarding the trade-offs between drug properties via pairwise comparison. Our framework combines preferential multi-objective Bayesian optimization with a docking model for measuring binding affinity to capture human chemical intuition for improving hit identification. Specifically, on a library of 100K chemical candidates targeting EGFR and DRD2, CheapVS outperforms state-of-the-art screening methods in identifying drugs within a limited computational budget. Notably, our method can recover up to 16/37 EGFR and 37/58 DRD2 known drugs while screening only 6% of the library, showcasing its potential to significantly advance drug discovery.
Physics-Informed Neural Network Surrogate Models for River Stage Prediction
Venue: arXiv cs.LG 2025
Year: 2025

This work investigates the feasibility of using Physics-Informed Neural Networks (PINNs) as surrogate models for river stage prediction, aiming to reduce computational cost while maintaining predictive accuracy. Our primary contribution demonstrates that PINNs can successfully approximate HEC-RAS numerical solutions when trained on a single river, achieving strong predictive accuracy with generally low relative errors, though some river segments exhibit higher deviations. By integrating the governing Saint-Venant equations into the learning process, the proposed PINN-based surrogate model enforces physical consistency and significantly improves computational efficiency compared to HEC-RAS. We evaluate the model's performance in terms of accuracy and computational speed, demonstrating that it closely approximates HEC-RAS predictions while enabling real-time inference. These results highlight the potential of PINNs as effective surrogate models for single-river hydrodynamics, offering a promising alternative for computationally efficient river stage forecasting. Future work will explore techniques to enhance PINN training stability and robustness across a more generalized multi-river model.
PRIOT: Pruning-Based Integer-Only Transfer Learning for Embedded Systems
Venue: arXiv cs.LG 2025
Year: 2025

On-device transfer learning is crucial for adapting a common backbone model to the unique environment of each edge device. Tiny microcontrollers, such as the Raspberry Pi Pico, are key targets for on-device learning but often lack floating-point units, necessitating integer-only training. Dynamic computation of quantization scale factors, which is adopted in former studies, incurs high computational costs. Therefore, this study focuses on integer-only training with static scale factors, which is challenging with existing training methods. We propose a new training method named PRIOT, which optimizes the network by pruning selected edges rather than updating weights, allowing effective training with static scale factors. The pruning pattern is determined by the edge-popup algorithm, which trains a parameter named score assigned to each edge instead of the original parameters and prunes the edges with low scores before inference. Additionally, we introduce a memory-efficient variant, PRIOT-S, which only assigns scores to a small fraction of edges. We implement PRIOT and PRIOT-S on the Raspberry Pi Pico and evaluate their accuracy and computational costs using a tiny CNN model on the rotated MNIST dataset and the VGG11 model on the rotated CIFAR-10 dataset. Our results demonstrate that PRIOT improves accuracy by 8.08 to 33.75 percentage points over existing methods, while PRIOT-S reduces memory footprint with minimal accuracy loss.
Joint Extraction Matters: Prompt-Based Visual Question Answering for Multi-Field Document Information Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Visual question answering (VQA) has emerged as a flexible approach for extracting specific pieces of information from document images. However, existing work typically queries each field in isolation, overlooking potential dependencies across multiple items. This paper investigates the merits of extracting multiple fields jointly versus separately. Through experiments on multiple large vision language models and datasets, we show that jointly extracting fields often improves accuracy, especially when the fields share strong numeric or contextual dependencies. We further analyze how performance scales with the number of requested items and use a regression based metric to quantify inter field relationships. Our results suggest that multi field prompts can mitigate confusion arising from similar surface forms and related numeric values, providing practical methods for designing robust VQA systems in document information extraction tasks.
Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Knowledge distillation can be a cost-effective technique to distill knowledge in Large Language Models, if the teacher output logits can be pre-computed and cached. However, successfully applying this to pre-training remains largely unexplored. In this work, we prove that naive approaches for sparse knowledge distillation such as caching Top-K probabilities, while intuitive, provide biased estimates of teacher probability distribution to the student, resulting in suboptimal performance and calibration. We propose an importance-sampling-based method `Random Sampling Knowledge Distillation', which provides unbiased estimates, preserves the gradient in expectation, and requires storing significantly sparser logits. Our method enables faster training of student models with marginal overhead (<10%) compared to cross-entropy based training, while maintaining competitive performance compared to full distillation, across a range of model sizes from 300M to 3B.
"Playing the robot's advocate": Bystanders' descriptions of a robot's conduct in public settings
Venue: arXiv cs.HC 2025
Year: 2025

Relying on a large corpus of natural interactions between visitors and a robot in a museum setting, we study a recurrent practice through which humans "worked" to maintain the robot as a competent participant: the description by bystanders, in a way that was made accessible to the main speaker, of the social action that the robot was taken to be accomplishing. Doing so, bystanders maintained the robot's (sometimes incongruous) behaviour as relevant to the activity at hand and preserved the robot itself as a competent participant. Relying on these data, we argue that ex ante definitions of a robot as "social" (i.e. before any interaction occurred) run the risk of naturalizing as self-evident the observable result from micro-sociological processes: namely, the interactional work of co-present humans through which the robot's conduct is reconfigured as contextually relevant.
Indoor Localization Based on MSC Map
Venue: arXiv cs.NI 2025
Year: 2025

In this short paper, we propose a technique for AI-based identification of modulation and coding schemes (MCS) in surrounding cellular signals. Based on the created MCS map, we evaluate the performance of indoor localization techniques.
Follow-Your-Color: Multi-Instance Sketch Colorization
Venue: arXiv cs.CV 2025
Year: 2025

We present Follow-Your-Color, a diffusion-based framework for multi-instance sketch colorization. The production of multi-instance 2D line art colorization adheres to an industry-standard workflow, which consists of three crucial stages: the design of line art characters, the coloring of individual objects, and the refinement process. The artists are required to repeat the process of coloring each instance one by one, which is inaccurate and inefficient. Meanwhile, current generative methods fail to solve this task due to the challenge of multi-instance pair data collection. To tackle these challenges, we incorporate three technical designs to ensure precise character detail transcription and achieve multi-instance sketch colorization in a single forward pass. Specifically, we first propose the self-play training strategy to address the lack of training data. Then we introduce an instance guider to feed the color of the instance. To achieve accurate color matching, we present fine-grained color matching with edge loss to enhance visual quality. Equipped with the proposed modules, Follow-Your-Color enables automatically transforming sketches into vividly-colored images with accurate consistency and multi-instance control. Experiments on our collected datasets show that our model outperforms existing methods regarding chromatic precision. Specifically, our model critically automates the colorization process with zero manual adjustments, so novice users can produce stylistically consistent artwork by providing reference instances and the original line art. Our code and additional details are available at https://yinhan-zhang.github.io/color.
Evolving the Computational Notebook: A Two-Dimensional Canvas for Enhanced Human-AI Interaction
Venue: arXiv cs.SE 2025
Year: 2025

Computational notebooks, while essential for data science, are limited by their one-dimensional interface, which poorly aligns with non-linear developer workflows and complicates collaboration and human-AI interaction. In this work, we focus on features of Computational Canvas, a novel two-dimensional interface that evolves notebooks to enhance data analysis and AI-assisted development within integrated development environments (IDEs). We present vital features, including freely arrangeable code cells, separate environments, and improved output management. These features are designed to facilitate intuitive organization, visual exploration, and natural collaboration with other users and AI agents. We also show the implementation of Computational Canvas with designed features as a Visual Studio Code plugin. By shifting from linear to two-dimensional spatial interfaces, we aim to significantly boost developers' productivity in data exploration, experimentation, and AI-assisted development, addressing the current limitations of traditional notebooks and fostering more flexible, collaborative data science workflows.
Extending Behavior Trees for Robotic Missions with Quality Requirements
Venue: arXiv cs.SE 2025
Year: 2025

Context and motivation: In recent years, behavior trees have gained growing interest within the robotics community as a specification and control switching mechanism for the different tasks that form a robotics mission. Problem: Given the rising complexity and prevalence of robotic systems, it is increasingly challenging and important for practitioners to design high-quality missions that meet certain qualities, for instance, to consider potential failures or mitigate safety risks. In software requirements engineering, quality or non-functional requirements have long been recognized as a key factor in system success. Currently, qualities are not represented in behavior tree models, which capture a robotic mission, making it difficult to assess the extent to which different mission components comply with those qualities. Principal ideas: In this paper, we propose an extension for behavior trees to have qualities and quality requirements explicitly represented in robotics missions. We provide a meta-model for the extension, develop a domain-specific language (DSL), and describe how we integrated our DSL in one of the most used languages in robotics for developing behavior trees, BehaviorTree.CPP. A preliminary evaluation of the implemented DSL shows promising results for the feasibility of our approach and the need for similar DSLs. Contribution: Our approach paves the way for incorporating qualities into the behavior model of robotics missions. This promotes early expression of qualities in robotics missions, and a better overview of missions components and their contribution to the satisfaction of quality concerns.
Nofl: A Precise Immix
Venue: arXiv cs.PL 2025
Year: 2025

Can a memory manager be built with fast bump-pointer allocation, single-pass heap tracing, and a low upper bound on memory overhead? The Immix collector answered in the affirmative for the first two, but the granularity at which it reclaims memory means that in the worst case a tiny object can keep two 128-byte lines of memory from being re-used for allocation. This paper takes Immix to an extreme of precision, allowing all free space between objects to be reclaimed, down to the limit of the allocator's minimum alignment. We present the design of this Nofl layout, build a collector library around it, and build a new Scheme-to-C compiler as a workbench. We make a first evaluation of the Nofl-based mostly-marking collector when compared to standard copying and mark-sweep collectors and run against a limited set of microbenchmarks, finding that Nofl outperforms the others for tight-to-adequate heap sizes.
ARFlow: Human Action-Reaction Flow Matching with Physical Guidance
Venue: arXiv cs.CV 2025
Year: 2025

Human action-reaction synthesis, a fundamental challenge in modeling causal human interactions, plays a critical role in applications ranging from virtual reality to social robotics. While diffusion-based models have demonstrated promising performance, they exhibit two key limitations for interaction synthesis: reliance on complex noise-to-reaction generators with intricate conditional mechanisms, and frequent physical violations in generated motions. To address these issues, we propose Action-Reaction Flow Matching (ARFlow), a novel framework that establishes direct action-to-reaction mappings, eliminating the need for complex conditional mechanisms. Our approach introduces a physical guidance mechanism specifically designed for Flow Matching (FM) that effectively prevents body penetration artifacts during sampling. Moreover, we discover the bias of traditional flow matching sampling algorithm and employ a reprojection method to revise the sampling direction of FM. To further enhance the reaction diversity, we incorporate randomness into the sampling process. Extensive experiments on NTU120, Chi3D and InterHuman datasets demonstrate that ARFlow not only outperforms existing methods in terms of Fréchet Inception Distance and motion diversity but also significantly reduces body collisions, as measured by our new Intersection Volume and Intersection Frequency metrics.
Enabling Versatile Controls for Video Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Despite substantial progress in text-to-video generation, achieving precise and flexible control over fine-grained spatiotemporal attributes remains a significant unresolved challenge in video generation research. To address these limitations, we introduce VCtrl (also termed PP-VCtrl), a novel framework designed to enable fine-grained control over pre-trained video diffusion models in a unified manner. VCtrl integrates diverse user-specified control signals-such as Canny edges, segmentation masks, and human keypoints-into pretrained video diffusion models via a generalizable conditional module capable of uniformly encoding multiple types of auxiliary signals without modifying the underlying generator. Additionally, we design a unified control signal encoding pipeline and a sparse residual connection mechanism to efficiently incorporate control representations. Comprehensive experiments and human evaluations demonstrate that VCtrl effectively enhances controllability and generation quality. The source code and pre-trained models are publicly available and implemented using the PaddlePaddle framework at http://github.com/PaddlePaddle/PaddleMIX/tree/develop/ppdiffusers/examples/ppvctrl.
Developing Critical Thinking in Second Language Learners: Exploring Generative AI like ChatGPT as a Tool for Argumentative Essay Writing
Venue: arXiv cs.HC 2025
Year: 2025

This study employs the Paul-Elder Critical Thinking Model and Tan's argumentative writing framework to create a structured methodology. This methodology, ChatGPT Guideline for Critical Argumentative Writing (CGCAW) framework, integrates the models with ChatGPT's capabilities to guide L2 learners in utilizing ChatGPT to enhance their critical thinking skills. A quantitative experiment was conducted with 10 participants from a state university, divided into experimental and control groups. The experimental group utilized the CGCAW framework, while the control group used ChatGPT without specific guidelines. Participants wrote an argumentative essay within a 40-minute timeframe, and essays were evaluated by three assessors: ChatGPT, Grammarly, and a course instructor. Results indicated that the experimental group showed improvements in clarity, logical coherence, and use of evidence, demonstrating ChatGPT's potential to enhance specific aspects of argumentative writing. However, the control group performed better in overall language mechanics and articulation of main arguments, indicating areas where the CGCAW framework could be further refined. This study highlights the need for further research to optimize the use of AI tools like ChatGPT in L2 learning environments to enhance critical thinking and writing skills.