OpenRaiser/Intern-Atlas huggingface dataset:
Snail Homing and Mating Search Algorithm for Weight Optimization of Stepped-Transmission Shaft
Venue: arXiv cs.NE 2025
Year: 2025

In this paper, the steeped-transmission shaft design problem is proposed for weight optimization. The bio-inspired search-based Snail Homing and Mating Search (SHMS) algorithm is utilized to solve the problem. It is inspired by the social behaviour of snails and their inherent nature of finding better homes, and mate. The proposed steeped-transmission shaft design problem is modelled considering the fatigue loading, combined bending, torsion loads, and the principle of Modified Goodman criteria. The forces diagram and the bending moment diagrams are obtained using the MDSOLIDS software. The forces and bending moment are then used to mathematical model the objective function and constraints. The SHMS algorithm has yielded the desired solution with reasonable computational cost. The constraints are handled using a static penalty function approach. The statistical results obtained using SHMS algorithm are further used for generating CAD model. The analysis is carried out in ANSYS Workbench. Further, the deflection obtained from SHMS algorithm and ANSYS Workbench are compared and results are discussed in details.
Perspectives and potential issues in using artificial intelligence for computer science education
Venue: arXiv cs.CY 2025
Year: 2025

Since its launch in late 2022, ChatGPT has ignited widespread interest in Large Language Models (LLMs) and broader Artificial Intelligence (AI) solutions. As this new wave of AI permeates various sectors of society, we are continually uncovering both the potential and the limitations of existing AI tools. The need for adjustment is particularly significant in Computer Science Education (CSEd), as LLMs have evolved into core coding tools themselves, blurring the line between programming aids and intelligent systems, and reinforcing CSEd's role as a nexus of technology and pedagogy. The findings of our survey indicate that while AI technologies hold potential for enhancing learning experiences, such as through personalized learning paths, intelligent tutoring systems, and automated assessments, there are also emerging concerns. These include the risk of over-reliance on technology, the potential erosion of fundamental cognitive skills, and the challenge of maintaining equitable access to such innovations. Recent advancements represent a paradigm shift, transforming not only the content we teach but also the methods by which teaching and learning take place. Rather than placing the burden of adapting to AI technologies on students, educational institutions must take a proactive role in verifying, integrating, and applying new pedagogical approaches. Such efforts can help ensure that both educators and learners are equipped with the skills needed to navigate the evolving educational landscape shaped by these technological innovations.
Implementing a Logical Inference System for Japanese Comparatives
Venue: arXiv cs.CL 2025
Year: 2025

Natural Language Inference (NLI) involving comparatives is challenging because it requires understanding quantities and comparative relations expressed by sentences. While some approaches leverage Large Language Models (LLMs), we focus on logic-based approaches grounded in compositional semantics, which are promising for robust handling of numerical and logical expressions. Previous studies along these lines have proposed logical inference systems for English comparatives. However, it has been pointed out that there are several morphological and semantic differences between Japanese and English comparatives. These differences make it difficult to apply such systems directly to Japanese comparatives. To address this gap, this study proposes ccg-jcomp, a logical inference system for Japanese comparatives based on compositional semantics. We evaluate the proposed system on a Japanese NLI dataset containing comparative expressions. We demonstrate the effectiveness of our system by comparing its accuracy with that of existing LLMs.
Dynamic Adaptive Legged Locomotion Policy via Decoupling Reaction Force Control and Gait Control
Venue: arXiv cs.RO 2025
Year: 2025

While Reinforcement Learning (RL) has achieved remarkable progress in legged locomotion control, it often suffers from performance degradation in out-of-distribution (OOD) conditions and discrepancies between the simulation and the real environments. Instead of mainly relying on domain randomization (DR) to best cover the real environments and thereby close the sim-to-real gap and enhance robustness, this work proposes an emerging decoupled framework that acquires fast online adaptation ability and mitigates the sim-to-real problems in unfamiliar environments by isolating stance-leg control and swing-leg control. Various simulation and real-world experiments demonstrate its effectiveness against horizontal force disturbances, uneven terrains, heavy and biased payloads, and sim-to-real gap.
Controllable-Continuous Color Editing in Diffusion Model via Color Mapping
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, text-driven image editing has made significant progress. However, due to the inherent ambiguity and discreteness of natural language, color editing still faces challenges such as insufficient precision and difficulty in achieving continuous control. Although linearly interpolating the embedding vectors of different textual descriptions can guide the model to generate a sequence of images with varying colors, this approach lacks precise control over the range of color changes in the output images. Moreover, the relationship between the interpolation coefficient and the resulting image color is unknown and uncontrollable. To address these issues, we introduce a color mapping module that explicitly models the correspondence between the text embedding space and image RGB values. This module predicts the corresponding embedding vector based on a given RGB value, enabling precise color control of the generated images while maintaining semantic consistency. Users can specify a target RGB range to generate images with continuous color variations within the desired range, thereby achieving finer-grained, continuous, and controllable color editing. Experimental results demonstrate that our method performs well in terms of color continuity and controllability.
AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

While reasoning technology like Chain of Thought (CoT) has been widely adopted in Vision Language Action (VLA) models, it demonstrates promising capabilities in end to end autonomous driving. However, recent efforts to integrate CoT reasoning often fall short in simple scenarios, introducing unnecessary computational overhead without improving decision quality. To address this, we propose AdaThinkDrive, a novel VLA framework with a dual mode reasoning mechanism inspired by fast and slow thinking. First, our framework is pretrained on large scale autonomous driving (AD) scenarios using both question answering (QA) and trajectory datasets to acquire world knowledge and driving commonsense. During supervised fine tuning (SFT), we introduce a two mode dataset, fast answering (w/o CoT) and slow thinking (with CoT), enabling the model to distinguish between scenarios that require reasoning. Furthermore, an Adaptive Think Reward strategy is proposed in conjunction with the Group Relative Policy Optimization (GRPO), which rewards the model for selectively applying CoT by comparing trajectory quality across different reasoning modes. Extensive experiments on the Navsim benchmark show that AdaThinkDrive achieves a PDMS of 90.3, surpassing the best vision only baseline by 1.7 points. Moreover, ablations show that AdaThinkDrive surpasses both the never Think and always Think baselines, improving PDMS by 2.0 and 1.4, respectively. It also reduces inference time by 14% compared to the always Think baseline, demonstrating its ability to balance accuracy and efficiency through adaptive reasoning.
Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Model Powered Multi-Agent Systems (MASs) are increasingly employed to automate complex real-world problems, such as programming and scientific discovery. Despite their promising, MASs are not without their flaws. However, failure attribution in MASs - pinpointing the specific agent actions responsible for failures - remains underexplored and labor-intensive, posing significant challenges for debugging and system improvement. To bridge this gap, we propose FAMAS, the first spectrum-based failure attribution approach for MASs, which operates through systematic trajectory replay and abstraction, followed by spectrum analysis.The core idea of FAMAS is to estimate, from variations across repeated MAS executions, the likelihood that each agent action is responsible for the failure. In particular, we propose a novel suspiciousness formula tailored to MASs, which integrates two key factor groups, namely the agent behavior group and the action behavior group, to account for the agent activation patterns and the action activation patterns within the execution trajectories of MASs. Through expensive evaluations against 12 baselines on the Who and When benchmark, FAMAS demonstrates superior performance by outperforming all the methods in comparison.
A Survey and Evaluation Framework for Secure DNS Resolution
Venue: arXiv cs.CR 2025
Year: 2025

Since security was not among the original design goals of the Domain Name System (herein called Vanilla DNS), many secure DNS schemes have been proposed to enhance the security and privacy of the DNS resolution process. Some proposed schemes aim to replace the existing DNS infrastructure entirely, but none have succeeded in doing so. In parallel, numerous schemes focus on improving DNS security without modifying its fundamental two-stage structure. These efforts highlight the feasibility of addressing DNS security as two distinct but compatible stages. We survey DNS resolution process attacks and threats and develop a comprehensive threat model and attack taxonomy for their systematic categorization. This analysis results in the formulation of 14 desirable security, privacy, and availability properties to mitigate the identified threats. Using these properties, we develop an objective evaluation framework and apply it to comparatively analyze 12 secure DNS schemes surveyed in this work that aim to augment the properties of the DNS resolution process. Our evaluation reveals that no single scheme provides ideal protection across the entire resolution path. Instead, the schemes tend to address a subset of properties specific to individual stages. Since these schemes targeting different stages of DNS resolution are complementary and can operate together, combining compatible schemes offers a practical and effective approach to achieving comprehensive security in the DNS resolution process.
Masked Feature Modeling Enhances Adaptive Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Unsupervised domain adaptation (UDA) for semantic segmentation aims to transfer models from a labeled source domain to an unlabeled target domain. While auxiliary self-supervised tasks-particularly contrastive learning-have improved feature discriminability, masked modeling approaches remain underexplored in this setting, largely due to architectural incompatibility and misaligned optimization objectives. We propose Masked Feature Modeling (MFM), a novel auxiliary task that performs feature masking and reconstruction directly in the feature space. Unlike existing masked modeling methods that reconstruct low-level inputs or perceptual features (e.g., HOG or visual tokens), MFM aligns its learning target with the main segmentation task, ensuring compatibility with standard architectures like DeepLab and DAFormer without modifying the inference pipeline. To facilitate effective reconstruction, we introduce a lightweight auxiliary module, Rebuilder, which is trained jointly but discarded during inference, adding zero computational overhead at test time. Crucially, MFM leverages the segmentation decoder to classify the reconstructed features, tightly coupling the auxiliary objective with the pixel-wise prediction task to avoid interference with the primary task. Extensive experiments across various architectures and UDA benchmarks demonstrate that MFM consistently enhances segmentation performance, offering a simple, efficient, and generalizable strategy for unsupervised domain-adaptive semantic segmentation.
Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET
Venue: arXiv cs.CV 2025
Year: 2025

The classification of pixel spectra of hyperspectral images, i.e. spectral classification, is used in many fields ranging from agricultural, over medical to remote sensing applications and is currently also expanding to areas such as autonomous driving. Even though for full hyperspectral images the best-performing methods exploit spatial-spectral information, performing classification solely on spectral information has its own advantages, e.g. smaller model size and thus less data required for training. Moreover, spectral information is complementary to spatial information and improvements on either part can be used to improve spatial-spectral approaches in the future. Recently, 1D-Justo-LiuNet was proposed as a particularly efficient model with very few parameters, which currently defines the state of the art in spectral classification. However, we show that with limited training data the model performance deteriorates. Therefore, we investigate MiniROCKET and HDC-MiniROCKET for spectral classification to mitigate that problem. The model extracts well-engineered features without trainable parameters in the feature extraction part and is therefore less vulnerable to limited training data. We show that even though MiniROCKET has more parameters it outperforms 1D-Justo-LiuNet in limited data scenarios and is mostly on par with it in the general case
Using Petri Nets for Context-Adaptive Robot Explanations
Venue: arXiv cs.RO 2025
Year: 2025

In human-robot interaction, robots must communicate in a natural and transparent manner to foster trust, which requires adapting their communication to the context. In this paper, we propose using Petri nets (PNs) to model contextual information for adaptive robot explanations. PNs provide a formal, graphical method for representing concurrent actions, causal dependencies, and system states, making them suitable for analyzing dynamic interactions between humans and robots. We demonstrate this approach through a scenario involving a robot that provides explanations based on contextual cues such as user attention and presence. Model analysis confirms key properties, including deadlock-freeness, context-sensitive reachability, boundedness, and liveness, showing the robustness and flexibility of PNs for designing and verifying context-adaptive explanations in human-robot interactions.
An Exhaustive DPLL Approach to Model Counting over Integer Linear Constraints with Simplification Techniques
Venue: arXiv cs.AI 2025
Year: 2025

Linear constraints are one of the most fundamental constraints in fields such as computer science, operations research and optimization. Many applications reduce to the task of model counting over integer linear constraints (MCILC). In this paper, we design an exact approach to MCILC based on an exhaustive DPLL architecture. To improve the efficiency, we integrate several effective simplification techniques from mixed integer programming into the architecture. We compare our approach to state-of-the-art MCILC counters and propositional model counters on 2840 random and 4131 application benchmarks. Experimental results show that our approach significantly outperforms all exact methods in random benchmarks solving 1718 instances while the state-of-the-art approach only computes 1470 instances. In addition, our approach is the only approach to solve all 4131 application instances.
Repulsive Trajectory Modification and Conflict Resolution for Efficient Multi-Manipulator Motion Planning
Venue: arXiv cs.RO 2025
Year: 2025

We propose an efficient motion planning method designed to efficiently find collision-free trajectories for multiple manipulators. While multi-manipulator systems offer significant advantages, coordinating their motions is computationally challenging owing to the high dimensionality of their composite configuration space. Conflict-Based Search (CBS) addresses this by decoupling motion planning, but suffers from subsequent conflicts incurred by resolving existing conflicts, leading to an exponentially growing constraint tree of CBS. Our proposed method is based on repulsive trajectory modification within the two-level structure of CBS. Unlike conventional CBS variants, the low-level planner applies a gradient descent approach using an Artificial Potential Field. This field generates repulsive forces that guide the trajectory of the conflicting manipulator away from those of other robots. As a result, subsequent conflicts are less likely to occur. Additionally, we develop a strategy that, under a specific condition, directly attempts to find a conflict-free solution in a single step without growing the constraint tree. Through extensive tests including physical robot experiments, we demonstrate that our method consistently reduces the number of expanded nodes in the constraint tree, achieves a higher success rate, and finds a solution faster compared to Enhanced CBS and other state-of-the-art algorithms.
Performance Evaluation of Intent-Based Networking Scenarios: A GitOps and Nephio Approach
Venue: arXiv cs.NI 2025
Year: 2025

GitOps has emerged as a foundational paradigm for managing cloud-native infrastructures by enabling declarative configuration, version-controlled state, and automated reconciliation between intents and runtime deployments. Despite its widespread adoption, the performance and scalability of GitOps tools in Intent-Based Networking (IBN) scenarios are insufficiently evaluated. This paper presents a reproducible, metric-driven benchmarking, assessing the latency and resource overheads of three widely used GitOps operators: Argo CD, Flux CD, and ConfigSync. We conduct controlled experiments under both single- and multi-intent scenarios, capturing key performance indicators such as latency and resource consumption. Our results highlight trade-offs between the tools in terms of determinism, resource efficiency, and responsiveness. We further investigate a realistic orchestration scenario, using Nephio as our orchestrator, to quantify the processing latency and overhead in declarative end-to-end deployment pipelines. Our findings can offer valuable insights for tool selection and optimisation in future autonomous network orchestration systems.
Plug-and-Play PDE Optimization for 3D Gaussian Splatting: Toward High-Quality Rendering and Reconstruction
Venue: arXiv cs.GR 2025
Year: 2025

3D Gaussian Splatting (3DGS) has revolutionized radiance field reconstruction by achieving high-quality novel view synthesis with fast rendering speed, introducing 3D Gaussian primitives to represent the scene. However, 3DGS encounters blurring and floaters when applied to complex scenes, caused by the reconstruction of redundant and ambiguous geometric structures. We attribute this issue to the unstable optimization of the Gaussians. To address this limitation, we present a plug-and-play PDE-based optimization method that overcomes the optimization constraints of 3DGS-based approaches in various tasks, such as novel view synthesis and surface reconstruction. Firstly, we theoretically derive that the 3DGS optimization procedure can be modeled as a PDE, and introduce a viscous term to ensure stable optimization. Secondly, we use the Material Point Method (MPM) to obtain a stable numerical solution of the PDE, which enhances both global and local constraints. Additionally, an effective Gaussian densification strategy and particle constraints are introduced to ensure fine-grained details. Extensive qualitative and quantitative experiments confirm that our method achieves state-of-the-art rendering and reconstruction quality.
Low-cost Highly-interoperable Multiplatform Campus Network: Experience of YARSI University
Venue: arXiv cs.NI 2025
Year: 2025

To some organizations, building campus network is sometimes considered to be very expensive; and this has made the project uneasy to perform. Moreover, if the organization without sufficient IT knowledge does not have capable IT engineers, leaving this project to third parties without supervision would lead to unexpected larger expenses. For this reason, in the year of 2003, YARSI University formed CMIS (Center for Management Infor-mation System) to perform tasks in designing, operations and maintenance of campus network and its services. By combining Open Source operating system run on a local assembled personal computer as gateway and router, and switching technology from Cisco, we designed a low-cost UTP-based campus network which covering rooms and buildings in YARSI environment. Meanwhile the internet access through several broadband connections and dedicated wireless was shared to more than 100 simultaneous users by a captive portal system. With this strategy, we can significantly reduce cost for purchasing, maintenance and operations of network infrastructure and internet access. Our model in designing low-cost campus network and internet connections could be adopted by rural community or organizations that have limited budget to have internet access.
Exploring Major Transitions in the Evolution of Biological Cognition With Artificial Neural Networks
Venue: arXiv cs.AI 2025
Year: 2025

Transitional accounts of evolution emphasise a few changes that shape what is evolvable, with dramatic consequences for derived lineages. More recently it has been proposed that cognition might also have evolved via a series of major transitions that manipulate the structure of biological neural networks, fundamentally changing the flow of information. We used idealised models of information flow, artificial neural networks (ANNs), to evaluate whether changes in information flow in a network can yield a transitional change in cognitive performance. We compared networks with feed-forward, recurrent and laminated topologies, and tested their performance learning artificial grammars that differed in complexity, controlling for network size and resources. We documented a qualitative expansion in the types of input that recurrent networks can process compared to feed-forward networks, and a related qualitative increase in performance for learning the most complex grammars. We also noted how the difficulty in training recurrent networks poses a form of transition barrier and contingent irreversibility -- other key features of evolutionary transitions. Not all changes in network topology confer a performance advantage in this task set. Laminated networks did not outperform non-laminated networks in grammar learning. Overall, our findings show how some changes in information flow can yield transitions in cognitive performance.