OpenRaiser/Intern-Atlas huggingface dataset:
Societal AI Research Has Become Less Interdisciplinary
Venue: arXiv cs.CL 2025
Year: 2025

As artificial intelligence (AI) systems become deeply embedded in everyday life, calls to align AI development with ethical and societal values have intensified. Interdisciplinary collaboration is often championed as a key pathway for fostering such engagement. Yet it remains unclear whether interdisciplinary research teams are actually leading this shift in practice. This study analyzes over 100,000 AI-related papers published on ArXiv between 2014 and 2024 to examine how ethical values and societal concerns are integrated into technical AI research. We develop a classifier to identify societal content and measure the extent to which research papers express these considerations. We find a striking shift: while interdisciplinary teams remain more likely to produce societally-oriented research, computer science-only teams now account for a growing share of the field's overall societal output. These teams are increasingly integrating societal concerns into their papers and tackling a wide range of domains - from fairness and safety to healthcare and misinformation. These findings challenge common assumptions about the drivers of societal AI and raise important questions. First, what are the implications for emerging understandings of AI safety and governance if most societally-oriented research is being undertaken by exclusively technical teams? Second, for scholars in the social sciences and humanities: in a technical field increasingly responsive to societal demands, what distinctive perspectives can we still offer to help shape the future of AI?
Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning
Venue: arXiv cs.RO 2025
Year: 2025

Limb loss affects millions globally, impairing physical function and reducing quality of life. Most traditional surface electromyographic (sEMG) and semi-autonomous methods require users to generate myoelectric signals for each control, imposing physically and mentally taxing demands. This study aims to develop a fully autonomous control system that enables a prosthetic hand to automatically grasp and release objects of various shapes using only a camera attached to the wrist. By placing the hand near an object, the system will automatically execute grasping actions with a proper grip force in response to the hand's movements and the environment. To release the object being grasped, just naturally place the object close to the table and the system will automatically open the hand. Such a system would provide individuals with limb loss with a very easy-to-use prosthetic control interface and greatly reduce mental effort while using. To achieve this goal, we developed a teleoperation system to collect human demonstration data for training the prosthetic hand control model using imitation learning, which mimics the prosthetic hand actions from human. Through training the model using only a few objects' data from one single participant, we have shown that the imitation learning algorithm can achieve high success rates, generalizing to more individuals and unseen objects with a variation of weights. The demonstrations are available at \href{https://sites.google.com/view/autonomous-prosthetic-hand}{https://sites.google.com/view/autonomous-prosthetic-hand}
Communicating Through Avatars in Industry 5.0: A Focus Group Study on Human-Robot Collaboration
Venue: arXiv cs.HC 2025
Year: 2025

The integration of collaborative robots (cobots) in industrial settings raises concerns about worker well-being, particularly due to reduced social interactions. Avatars - designed to facilitate worker interactions and engagement - are promising solutions to enhance the human-robot collaboration (HRC) experience. However, real-world perspectives on avatar-supported HRC remain unexplored. To address this gap, we conducted a focus group study with employees from a German manufacturing company that uses cobots. Before the discussion, participants engaged with a scripted, industry-like HRC demo in a lab setting. This qualitative approach provided valuable insights into the avatar's potential roles, improvements to its behavior, and practical considerations for deploying them in industrial workcells. Our findings also emphasize the importance of personalized communication and task assistance. Although our study's limitations restrict its generalizability, it serves as an initial step in recognizing the potential of adaptive, context-aware avatar interactions in real-world industrial environments.
MoRE: Mixture of Residual Experts for Humanoid Lifelike Gaits Learning on Complex Terrains
Venue: arXiv cs.RO 2025
Year: 2025

Humanoid robots have demonstrated robust locomotion capabilities using Reinforcement Learning (RL)-based approaches. Further, to obtain human-like behaviors, existing methods integrate human motion-tracking or motion prior in the RL framework. However, these methods are limited in flat terrains with proprioception only, restricting their abilities to traverse challenging terrains with human-like gaits. In this work, we propose a novel framework using a mixture of latent residual experts with multi-discriminators to train an RL policy, which is capable of traversing complex terrains in controllable lifelike gaits with exteroception. Our two-stage training pipeline first teaches the policy to traverse complex terrains using a depth camera, and then enables gait-commanded switching between human-like gait patterns. We also design gait rewards to adjust human-like behaviors like robot base height. Simulation and real-world experiments demonstrate that our framework exhibits exceptional performance in traversing complex terrains, and achieves seamless transitions between multiple human-like gait patterns.
MOMAV: A highly symmetrical fully-actuated multirotor drone using optimizing control allocation
Venue: arXiv cs.RO 2025
Year: 2025

MOMAV (Marco's Omnidirectional Micro Aerial Vehicle) is a multirotor drone that is fully actuated, meaning it can control its orientation independently of its position. MOMAV is also highly symmetrical, making its flight efficiency largely unaffected by its current orientation. These characteristics are achieved by a novel drone design where six rotor arms align with the vertices of an octahedron, and where each arm can actively rotate along its long axis. Various standout features of MOMAV are presented: The high flight efficiency compared to arm configuration of other fully-actuated drones, the design of an original rotating arm assembly featuring slip-rings used to enable continuous arm rotation, and a novel control allocation algorithm based on sequential quadratic programming (SQP) used to calculate throttle and arm-angle setpoints in flight. Flight tests have shown that MOMAV is able to achieve remarkably low mean position/orientation errors of 6.6mm, 2.1° (σ: 3.0mm, 1.0°) when sweeping position setpoints, and 11.8mm, 3.3° (σ: 8.6mm, 2.0°) when sweeping orientation setpoints.
Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU
Venue: arXiv cs.HC 2025
Year: 2025

This paper presents a keyword spotting (KWS) system implemented on the NXP MCXN947 microcontroller with an integrated Neural Processing Unit (NPU), enabling real-time voice interaction on resource-constrained devices. The system combines MFCC feature extraction with a CNN classifier, optimized using Quantization Aware Training to reduce model size with minimal accuracy drop. Experimental results demonstrate a 59x speedup in inference time when leveraging the NPU compared to CPU-only execution, achieving 97.06% accuracy with a model size of 30.58 KB, demonstrating the feasibility of efficient, low-power voice interfaces on embedded platforms.
Inherently Faithful Attention Maps for Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

We introduce an attention-based method that uses learned binary attention masks to ensure that only attended image regions influence the prediction. Context can strongly affect object perception, sometimes leading to biased representations, particularly when objects appear in out-of-distribution backgrounds. At the same time, many image-level object-centric tasks require identifying relevant regions, often requiring context. To address this conundrum, we propose a two-stage framework: stage 1 processes the full image to discover object parts and identify task-relevant regions, while stage 2 leverages input attention masking to restrict its receptive field to these regions, enabling a focused analysis while filtering out potentially spurious information. Both stages are trained jointly, allowing stage 2 to refine stage 1. Extensive experiments across diverse benchmarks demonstrate that our approach significantly improves robustness against spurious correlations and out-of-distribution backgrounds. Code: https://github.com/ananthu-aniraj/ifam
Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL)
Venue: arXiv cs.LG 2025
Year: 2025

Agent-based modeling (ABM) is a powerful tool for understanding self-organizing biological systems, but it is computationally intensive and often not analytically tractable. Equation learning (EQL) methods can derive continuum models from ABM data, but they typically require extensive simulations for each parameter set, raising concerns about generalizability. In this work, we extend EQL to Multi-experiment equation learning (ME-EQL) by introducing two methods: one-at-a-time ME-EQL (OAT ME-EQL), which learns individual models for each parameter set and connects them via interpolation, and embedded structure ME-EQL (ES ME-EQL), which builds a unified model library across parameters. We demonstrate these methods using a birth--death mean-field model and an on-lattice agent-based model of birth, death, and migration with spatial structure. Our results show that both methods significantly reduce the relative error in recovering parameters from agent-based simulations, with OAT ME-EQL offering better generalizability across parameter space. Our findings highlight the potential of equation learning from multiple experiments to enhance the generalizability and interpretability of learned models for complex biological systems.
Striking Back At Cobalt: Using Network Traffic Metadata To Detect Cobalt Strike Masquerading Command and Control Channels
Venue: arXiv cs.CR 2025
Year: 2025

Off-the-shelf software for Command and Control is often used by attackers and legitimate pentesters looking for discretion. Among other functionalities, these tools facilitate the customization of their network traffic so it can mimic popular websites, thereby increasing their secrecy. Cobalt Strike is one of the most famous solutions in this category, used by known advanced attacker groups such as "Mustang Panda" or "Nobelium". In response to these threats, Security Operation Centers and other defense actors struggle to detect Command and Control traffic, which often use encryption protocols such as TLS. Network traffic metadata-based machine learning approaches have been proposed to detect encrypted malware communications or fingerprint websites over Tor network. This paper presents a machine learning-based method to detect Cobalt Strike Command and Control activity based only on widely used network traffic metadata. The proposed method is, to the best of our knowledge, the first of its kind that is able to adapt the model it uses to the observed traffic to optimize its performance. This specificity permits our method to performs equally or better than the state of the art while using standard features. Our method is thus easier to use in a production environment and more explainable.
Mycelium: A Transformation-Embedded LSM-Tree
Venue: arXiv cs.DC 2025
Year: 2025

Compaction is a necessary, but often costly background process in write-optimized data structures like LSM-trees that reorganizes incoming data that is sequentially appended to logs. In this paper, we introduce Transformation-Embedded LSM-trees (TE-LSM), a novel approach that transparently embeds a variety of data transformations into the compaction process. While many others have sought to reduce the high cost of compaction, TE-LSMs leverage the opportunity to embed other useful work to amortize IO costs and amplification. We illustrate the use of a TE-LSM in Mycelium, our prototype built on top of RocksDB that extends the compaction process through a cross-column-family merging mechanism. Mycelium enables seamless integration of a transformer interface and aims to better prepare data for future accesses based on access patterns. We use Mycelium to explore three types of transformations: splitting column groups, converting data formats, and index building. In addition to providing a cost model analysis, we evaluate Mycelium's write and read performance using YCSB workloads. Our results show that Mycelium incurs a 20% write throughput overhead - significantly lower than the 35% to 60% overhead observed in naive approaches that perform data transformations outside of compaction-while achieving up to 425% improvements in read latency compared to RocksDB baseline.
Can A Gamer Train A Mathematical Reasoning Model?
Venue: arXiv cs.CL 2025
Year: 2025

While large language models (LLMs) have achieved remarkable performance in various tasks including mathematical reasoning, their development typically demands prohibitive computational resources. Recent advancements have reduced costs for training capable models, yet even these approaches rely on high-end hardware clusters. In this paper, we demonstrate that a single average gaming GPU can train a solid mathematical reasoning model, by integrating reinforcement learning and memory optimization techniques. Specifically, we train a 1.5B parameter mathematical reasoning model on RTX 3080 Ti of 16GB memory that achieves comparable or better performance on mathematical reasoning benchmarks than models several times larger, in resource-constrained environments. Our results challenge the paradigm that state-of-the-art mathematical reasoning necessitates massive infrastructure, democratizing access to high-performance AI research. https://github.com/shinandrew/YouronMath.
IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections
Venue: arXiv cs.AI 2025
Year: 2025

Traffic simulators are widely used to study the operational efficiency of road infrastructure, but their rule-based approach limits their ability to mimic real-world driving behavior. Traffic intersections are critical components of the road infrastructure, both in terms of safety risk (nearly 28% of fatal crashes and 58% of nonfatal crashes happen at intersections) as well as the operational efficiency of a road corridor. This raises an important question: can we create a data-driven simulator that can mimic the macro- and micro-statistics of the driving behavior at a traffic intersection? Deep Generative Modeling-based trajectory prediction models provide a good starting point to model the complex dynamics of vehicles at an intersection. But they are not tested in a "live" micro-simulation scenario and are not evaluated on traffic engineering-related metrics. In this study, we propose traffic engineering-related metrics to evaluate generative trajectory prediction models and provide a simulation-in-the-loop pipeline to do so. We also provide a multi-headed self-attention-based trajectory prediction model that incorporates the signal information, which outperforms our previous models on the evaluation metrics.
Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data
Venue: arXiv cs.LG 2025
Year: 2025

Developments in Deep Learning have significantly improved time series forecasting by enabling more accurate modeling of complex temporal dependencies inherent in sequential data. The effectiveness of such models is often demonstrated on limited sets of specific real-world data. Although this allows for comparative analysis, it still does not demonstrate how specific data characteristics align with the architectural strengths of individual models. Our research aims at uncovering clear connections between time series characteristics and particular models. We introduce a novel dataset generated using Gaussian Processes, specifically designed to display distinct, known characteristics for targeted evaluations of model adaptability to them. Furthermore, we present TimeFlex, a new model that incorporates a modular architecture tailored to handle diverse temporal dynamics, including trends and periodic patterns. This model is compared to current state-of-the-art models, offering a deeper understanding of how models perform under varied time series conditions.
UD-KSL Treebank v1.3: A semi-automated framework for aligning XPOS-extracted units with UPOS tags
Venue: arXiv cs.CL 2025
Year: 2025

The present study extends recent work on Universal Dependencies annotations for second-language (L2) Korean by introducing a semi-automated framework that identifies morphosyntactic constructions from XPOS sequences and aligns those constructions with corresponding UPOS categories. We also broaden the existing L2-Korean corpus by annotating 2,998 new sentences from argumentative essays. To evaluate the impact of XPOS-UPOS alignments, we fine-tune L2-Korean morphosyntactic analysis models on datasets both with and without these alignments, using two NLP toolkits. Our results indicate that the aligned dataset not only improves consistency across annotation layers but also enhances morphosyntactic tagging and dependency-parsing accuracy, particularly in cases of limited annotated data.
Deep Reinforcement Learning-Based RAN Slicing with Efficient Inter-Slice Isolation in Tactical Wireless Networks
Venue: arXiv cs.NI 2025
Year: 2025

The next generation of tactical networks (TNs) is poised to further leverage the key enablers of 5G and beyond 5G (B5G) technology, such as radio access network (RAN) slicing and the open RAN (O-RAN) paradigm, to unlock multiple architectural options and opportunities for a wide range of innovative applications. RAN slicing and the O-RAN paradigm are considered game changers in TNs, where the former makes it possible to tailor user services to users requirements, and the latter brings openness and intelligence to the management of the RAN. In TNs, bandwidth scarcity requires a dynamic bandwidth slicing strategy. Although this type of strategy ensures efficient bandwidth utilization, it compromises RAN slicing isolation in terms of quality of service (QoS) performance. To deal with this challenge, we propose a deep reinforcement learning (DRL)-based RAN slicing mechanism that achieves a trade-off between efficient RAN bandwidth sharing and appropriate inter- and intra-slice isolation. The proposed mechanism performs bandwidth allocation in two stages. In the first stage, the bandwidth is allocated to the RAN slices. In the second stage, each slice partitions its bandwidth among its associated users. In both stages, the slicing operation is constrained by several considerations related to improving the QoS of slices and users that in turn foster inter- and intra-slice isolation. The proposed RAN slicing mechanism is based on DRL algorithms to perform the bandwidth sharing operation in each stage. We propose to deploy the mechanism in an O-RAN architecture and describe the O-RAN functional blocks and the main DRL model lifecycle management phases involved. We also develop three different implementations of the proposed mechanism, each based on a different DRL algorithm, and evaluate their performance against multiple baselines across various parameters.
Agentic Neural Networks: Self-Evolving Multi-Agent Systems via Textual Backpropagation
Venue: arXiv cs.LG 2025
Year: 2025

Leveraging multiple Large Language Models(LLMs) has proven effective for addressing complex, high-dimensional tasks, but current approaches often rely on static, manually engineered multi-agent configurations. To overcome these constraints, we present the Agentic Neural Network(ANN), a framework that conceptualizes multi-agent collaboration as a layered neural network architecture. In this design, each agent operates as a node, and each layer forms a cooperative "team" focused on a specific subtask. Agentic Neural Network follows a two-phase optimization strategy: (1) Forward Phase-Drawing inspiration from neural network forward passes, tasks are dynamically decomposed into subtasks, and cooperative agent teams with suitable aggregation methods are constructed layer by layer. (2) Backward Phase-Mirroring backpropagation, we refine both global and local collaboration through iterative feedback, allowing agents to self-evolve their roles, prompts, and coordination. This neuro-symbolic approach enables ANN to create new or specialized agent teams post-training, delivering notable gains in accuracy and adaptability. Across four benchmark datasets, ANN surpasses leading multi-agent baselines under the same configurations, showing consistent performance improvements. Our findings indicate that ANN provides a scalable, data-driven framework for multi-agent systems, combining the collaborative capabilities of LLMs with the efficiency and flexibility of neural network principles. We plan to open-source the entire framework.
Segment Any Architectural Facades (SAAF):An automatic segmentation model for building facades, walls and windows based on multimodal semantics guidance
Venue: arXiv cs.CV 2025
Year: 2025

In the context of the digital development of architecture, the automatic segmentation of walls and windows is a key step in improving the efficiency of building information models and computer-aided design. This study proposes an automatic segmentation model for building facade walls and windows based on multimodal semantic guidance, called Segment Any Architectural Facades (SAAF). First, SAAF has a multimodal semantic collaborative feature extraction mechanism. By combining natural language processing technology, it can fuse the semantic information in text descriptions with image features, enhancing the semantic understanding of building facade components. Second, we developed an end-to-end training framework that enables the model to autonomously learn the mapping relationship from text descriptions to image segmentation, reducing the influence of manual intervention on the segmentation results and improving the automation and robustness of the model. Finally, we conducted extensive experiments on multiple facade datasets. The segmentation results of SAAF outperformed existing methods in the mIoU metric, indicating that the SAAF model can maintain high-precision segmentation ability when faced with diverse datasets. Our model has made certain progress in improving the accuracy and generalization ability of the wall and window segmentation task. It is expected to provide a reference for the development of architectural computer vision technology and also explore new ideas and technical paths for the application of multimodal learning in the architectural field.
Understanding and Improving Data Repurposing
Venue: arXiv cs.CY 2025
Year: 2025

We live in an age of unprecedented opportunities to use existing data for tasks not anticipated when those data were collected, resulting in widespread data repurposing. This commentary defines and maps the scope of data repurposing to highlight its importance for organizations and society and the need to study data repurposing as a frontier of data management. We explain how repurposing differs from original data use and data reuse and then develop a framework for data repurposing consisting of concepts and activities for adapting existing data to new tasks. The framework and its implications are illustrated using two examples of repurposing, one in healthcare and one in citizen science. We conclude by suggesting opportunities for research to better understand data repurposing and enable more effective data repurposing practices.