OpenRaiser/Intern-Atlas huggingface dataset:
Evolution of Programmers' Trust in Generative AI Programming Assistants
Venue: arXiv cs.HC 2025
Year: 2025

Motivation. Trust in generative AI programming assistants is a vital attitude that impacts how programmers use those programming assistants. Programmers that are over-trusting may be too reliant on their tools, leading to incorrect or vulnerable code; programmers that are under-trusting may avoid using tools that can improve their productivity and well-being. Methods. Since trust is a dynamic attitude that may change over time, this study aims to understand programmers' evolution of trust after immediate (one hour) and extended (10 days) use of GitHub Copilot. We collected survey data from 71 upper-division computer science students working on a legacy code base, representing a population that is about to enter the workforce. In this study, we quantitatively measure student trust levels and qualitatively uncover why student trust changes. Findings. Student trust, on average, increased over time. After completing a project with Copilot, however, students felt that Copilot requires a competent programmer to complete some tasks manually. Students mentioned that seeing Copilot's correctness, understanding how Copilot uses context from the code base, and learning some basics of natural language processing contributed to their elevated trust. Implications. Our study helps instructors and industry managers understand the factors that influence how students calibrate their trust with AI assistants. We make four pedagogical recommendations, which are that CS educators should 1) provide opportunities for students to work with Copilot on challenging software engineering tasks to calibrate their trust, 2) teach traditional skills of comprehending, debugging, and testing so students can verify output, 3) teach students about the basics of natural language processing, and 4) explicitly introduce and demonstrate the range of features available in Copilot.
RepIt: Steering Language Models with Concept-Specific Refusal Vectors
Venue: arXiv cs.AI 2025
Year: 2025

While activation steering in large language models (LLMs) is a growing area of research, methods can often incur broader effects than desired. This motivates isolation of purer concept vectors to enable targeted interventions and understand LLM behavior at a more granular level. We present RepIt, a simple and data-efficient framework for isolating concept-specific representations. Across five frontier LLMs, RepIt enables precise interventions: it selectively suppresses refusal on targeted concepts while preserving refusal elsewhere, producing models that answer WMD-related questions while still scoring as safe on standard benchmarks. We further show that the corrective signal localizes to just 100-200 neurons and that robust target representations can be extracted from as few as a dozen examples on a single A6000. This efficiency raises a dual concern: manipulations can be performed with modest compute and data to extend to underrepresented data-scarce topics while evading existing benchmarks. By disentangling refusal vectors with RepIt, this work demonstrates that targeted interventions can counteract overgeneralization, laying the foundation for more granular control of model behavior.
Contrastive timbre representations for musical instrument and synthesizer retrieval
Venue: arXiv cs.SD 2025
Year: 2025

Efficiently retrieving specific instrument timbres from audio mixtures remains a challenge in digital music production. This paper introduces a contrastive learning framework for musical instrument retrieval, enabling direct querying of instrument databases using a single model for both single- and multi-instrument sounds. We propose techniques to generate realistic positive/negative pairs of sounds for virtual musical instruments, such as samplers and synthesizers, addressing limitations in common audio data augmentation methods. The first experiment focuses on instrument retrieval from a dataset of 3,884 instruments, using single-instrument audio as input. Contrastive approaches are competitive with previous works based on classification pre-training. The second experiment considers multi-instrument retrieval with a mixture of instruments as audio input. In this case, the proposed contrastive framework outperforms related works, achieving 81.7\% top-1 and 95.7\% top-5 accuracies for three-instrument mixtures.
StyleSculptor: Zero-Shot Style-Controllable 3D Asset Generation with Texture-Geometry Dual Guidance
Venue: arXiv cs.CV 2025
Year: 2025

Creating 3D assets that follow the texture and geometry style of existing ones is often desirable or even inevitable in practical applications like video gaming and virtual reality. While impressive progress has been made in generating 3D objects from text or images, creating style-controllable 3D assets remains a complex and challenging problem. In this work, we propose StyleSculptor, a novel training-free approach for generating style-guided 3D assets from a content image and one or more style images. Unlike previous works, StyleSculptor achieves style-guided 3D generation in a zero-shot manner, enabling fine-grained 3D style control that captures the texture, geometry, or both styles of user-provided style images. At the core of StyleSculptor is a novel Style Disentangled Attention (SD-Attn) module, which establishes a dynamic interaction between the input content image and style image for style-guided 3D asset generation via a cross-3D attention mechanism, enabling stable feature fusion and effective style-guided generation. To alleviate semantic content leakage, we also introduce a style-disentangled feature selection strategy within the SD-Attn module, which leverages the variance of 3D feature patches to disentangle style- and content-significant channels, allowing selective feature injection within the attention framework. With SD-Attn, the network can dynamically compute texture-, geometry-, or both-guided features to steer the 3D generation process. Built upon this, we further propose the Style Guided Control (SGC) mechanism, which enables exclusive geometry- or texture-only stylization, as well as adjustable style intensity control. Extensive experiments demonstrate that StyleSculptor outperforms existing baseline methods in producing high-fidelity 3D assets.
WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in deep-research systems have demonstrated the potential for AI agents to autonomously discover and synthesize knowledge from external sources. In this paper, we introduce WebResearcher, a novel framework for building such agents through two key components: (1) WebResearcher, an iterative deep-research paradigm that reformulates deep research as a Markov Decision Process, where agents periodically consolidate findings into evolving reports while maintaining focused workspaces, overcoming the context suffocation and noise contamination that plague existing mono-contextual approaches; and (2) WebFrontier, a scalable data synthesis engine that generates high-quality training data through tool-augmented complexity escalation, enabling systematic creation of research tasks that bridge the gap between passive knowledge recall and active knowledge construction. Notably, we find that the training data from our paradigm significantly enhances tool-use capabilities even for traditional mono-contextual methods. Furthermore, our paradigm naturally scales through parallel thinking, enabling concurrent multi-agent exploration for more comprehensive conclusions. Extensive experiments across 6 challenging benchmarks demonstrate that WebResearcher achieves state-of-the-art performance, even surpassing frontier proprietary systems.
Do Natural Language Descriptions of Model Activations Convey Privileged Information?
Venue: arXiv cs.CL 2025
Year: 2025

Recent interpretability methods have proposed to translate LLM internal representations into natural language descriptions using a second verbalizer LLM. This is intended to illuminate how the target model represents and operates on inputs. But do such activation verbalization approaches actually provide privileged knowledge about the internal workings of the target model, or do they merely convey information about its inputs? We critically evaluate popular verbalization methods across datasets used in prior work and find that they can succeed at benchmarks without any access to target model internals, suggesting that these datasets may not be ideal for evaluating verbalization methods. We then run controlled experiments which reveal that verbalizations often reflect the parametric knowledge of the verbalizer LLM which generated them, rather than the knowledge of the target LLM whose activations are decoded. Taken together, our results indicate a need for targeted benchmarks and experimental controls to rigorously assess whether verbalization methods provide meaningful insights into the operations of LLMs.
LLM Chatbot-Creation Approaches
Venue: arXiv cs.HC 2025
Year: 2025

This full research-to-practice paper explores approaches for developing course chatbots by comparing low-code platforms and custom-coded solutions in educational contexts. With the rise of Large Language Models (LLMs) like GPT-4 and LLaMA, LLM-based chatbots are being integrated into teaching workflows to automate tasks, provide assistance, and offer scalable support. However, selecting the optimal development strategy requires balancing ease of use, customization, data privacy, and scalability. This study compares two development approaches: low-code platforms like AnythingLLM and Botpress, with custom-coded solutions using LangChain, FAISS, and FastAPI. The research uses Prompt engineering, Retrieval-augmented generation (RAG), and personalization to evaluate chatbot prototypes across technical performance, scalability, and user experience. Findings indicate that while low-code platforms enable rapid prototyping, they face limitations in customization and scaling, while custom-coded systems offer more control but require significant technical expertise. Both approaches successfully implement key research principles such as adaptive feedback loops and conversational continuity. The study provides a framework for selecting the appropriate development strategy based on institutional goals and resources. Future work will focus on hybrid solutions that combine low-code accessibility with modular customization and incorporate multimodal input for intelligent tutoring systems.
Behind India's ChatGPT Conversations: A Retrospective Analysis of 238 Unedited User Prompts
Venue: arXiv cs.CY 2025
Year: 2025

Understanding how users authentically interact with Large Language Models (LLMs) remains a significant challenge in human-computer interaction research. Most existing studies rely on self-reported usage patterns or controlled experimental conditions, potentially missing genuine behavioral adaptations. This study presents a behavioral analysis of the use of English-speaking urban professional ChatGPT in India based on 238 authentic, unedited user prompts from 40 participants in 15+ Indian cities, collected using retrospective survey methodology in August 2025. Using authentic retrospective prompt collection via anonymous social media survey to minimize real-time observer effects, we analyzed genuine usage patterns. Key findings include: (1) 85\% daily usage rate (34/40 users) indicating mature adoption beyond experimental use, (2) evidence of cross-domain integration spanning professional, personal, health and creative contexts among the majority of users, (3) 42.5\% (17/40) primarily use ChatGPT for professional workflows with evidence of real-time problem solving integration, and (4) cultural context navigation strategies with users incorporating Indian cultural specifications in their prompts. Users develop sophisticated adaptation techniques and the formation of advisory relationships for personal guidance. The study reveals the progression from experimental to essential workflow dependency, with users treating ChatGPT as an integrated life assistant rather than a specialized tool. However, the findings are limited to urban professionals in English recruited through social media networks and require a larger demographic validation. This work contributes a novel methodology to capture authentic AI usage patterns and provides evidence-based insights into cultural adaptation strategies among this specific demographic of users.
The Provenance Problem: LLMs and the Breakdown of Citation Norms
Venue: arXiv cs.CY 2025
Year: 2025

The increasing use of generative AI in scientific writing raises urgent questions about attribution and intellectual credit. When a researcher employs ChatGPT to draft a manuscript, the resulting text may echo ideas from sources the author has never encountered. If an AI system reproduces insights from, for example, an obscure 1975 paper without citation, does this constitute plagiarism? We argue that such cases exemplify the 'provenance problem': a systematic breakdown in the chain of scholarly credit. Unlike conventional plagiarism, this phenomenon does not involve intent to deceive (researchers may disclose AI use and act in good faith) yet still benefit from the uncredited intellectual contributions of others. This dynamic creates a novel category of attributional harm that current ethical and professional frameworks fail to address. As generative AI becomes embedded across disciplines, the risk that significant ideas will circulate without recognition threatens both the reputational economy of science and the demands of epistemic justice. This Perspective analyzes how AI challenges established norms of authorship, introduces conceptual tools for understanding the provenance problem, and proposes strategies to preserve integrity and fairness in scholarly communication.
Parking Space Ground Truth Test Automation by Artificial Intelligence Using Convolutional Neural Networks
Venue: arXiv cs.CV 2025
Year: 2025

This research is part of a study of a real-time, cloud-based on-street parking service using crowd-sourced in-vehicle fleet data. The service provides real-time information about available parking spots by classifying crowd-sourced detections observed via ultrasonic sensors. The goal of this research is to optimize the current parking service quality by analyzing the automation of the existing test process for ground truth tests. Therefore, methods from the field of machine learning, especially image pattern recognition, are applied to enrich the database and substitute human engineering work in major areas of the analysis process. After an introduction into the related areas of machine learning, this paper explains the methods and implementations made to achieve a high level of automation, applying convolutional neural networks. Finally, predefined metrics present the performance level achieved, showing a time reduction of human resources up to 99.58 %. The overall improvements are discussed, summarized, and followed by an outlook for future development and potential application of the analysis automation tool.
Curvature as a tool for evaluating dimensionality reduction and estimating intrinsic dimension
Venue: arXiv cs.CV 2025
Year: 2025

Utilizing recently developed abstract notions of sectional curvature, we introduce a method for constructing a curvature-based geometric profile of discrete metric spaces. The curvature concept that we use here captures the metric relations between triples of points and other points. More significantly, based on this curvature profile, we introduce a quantitative measure to evaluate the effectiveness of data representations, such as those produced by dimensionality reduction techniques. Furthermore, Our experiments demonstrate that this curvature-based analysis can be employed to estimate the intrinsic dimensionality of datasets. We use this to explore the large-scale geometry of empirical networks and to evaluate the effectiveness of dimensionality reduction techniques.
A Domain Knowledge Informed Approach for Anomaly Detection of Electric Vehicle Interior Sounds
Venue: arXiv cs.SD 2025
Year: 2025

The detection of anomalies in automotive cabin sounds is critical for ensuring vehicle quality and maintaining passenger comfort. In many real-world settings, this task is more appropriately framed as an unsupervised learning problem rather than the supervised case due to the scarcity or complete absence of labeled faulty data. In such an unsupervised setting, the model is trained exclusively on healthy samples and detects anomalies as deviations from normal behavior. However, in the absence of labeled faulty samples for validation and the limited reliability of commonly used metrics, such as validation reconstruction error, effective model selection remains a significant challenge. To overcome these limitations, a domain-knowledge-informed approach for model selection is proposed, in which proxy-anomalies engineered through structured perturbations of healthy spectrograms are used in the validation set to support model selection. The proposed methodology is evaluated on a high-fidelity electric vehicle dataset comprising healthy and faulty cabin sounds across five representative fault types viz., Imbalance, Modulation, Whine, Wind, and Pulse Width Modulation. This dataset, generated using advanced sound synthesis techniques, and validated via expert jury assessments, has been made publicly available to facilitate further research. Experimental evaluations on the five fault cases demonstrate the selection of optimal models using proxy-anomalies, significantly outperform conventional model selection strategies.
The threat of analytic flexibility in using large language models to simulate human data: A call to attention
Venue: arXiv cs.CY 2025
Year: 2025

Social scientists are now using large language models to create "silicon samples" - synthetic datasets intended to stand in for human respondents, aimed at revolutionising human subjects research. However, there are many analytic choices which must be made to produce these samples. Though many of these choices are defensible, their impact on sample quality is poorly understood. I map out these analytic choices and demonstrate how a very small number of decisions can dramatically change the correspondence between silicon samples and human data. Configurations (N = 252) varied substantially in their capacity to estimate (i) rank ordering of participants, (ii) response distributions, and (iii) between-scale correlations. Most critically, configurations were not consistent in quality: those that performed well on one dimension often performed poorly on another, implying that there is no "one-size-fits-all" configuration that optimises the accuracy of these samples. I call for greater attention to the threat of analytic flexibility in using silicon samples.
A Convex Formulation of Compliant Contact between Filaments and Rigid Bodies
Venue: arXiv cs.RO 2025
Year: 2025

We present a computational framework for simulating filaments interacting with rigid bodies through contact. Filaments are challenging to simulate due to their codimensionality, i.e., they are one-dimensional structures embedded in three-dimensional space. Existing methods often assume that filaments remain permanently attached to rigid bodies. Our framework unifies discrete elastic rod (DER) modeling, a pressure field patch contact model, and a convex contact formulation to accurately simulate frictional interactions between slender filaments and rigid bodies - capabilities not previously achievable. Owing to the convex formulation of contact, each time step can be solved to global optimality, guaranteeing complementarity between contact velocity and impulse. We validate the framework by assessing the accuracy of frictional forces and comparing its physical fidelity against baseline methods. Finally, we demonstrate its applicability in both soft robotics, such as a stochastic filament-based gripper, and deformable object manipulation, such as shoelace tying, providing a versatile simulator for systems involving complex filament-filament and filament-rigid body interactions.
Is Research Software Science a Metascience?
Venue: arXiv cs.SE 2025
Year: 2025

As research increasingly relies on computational methods, the reliability of scientific results depends on the quality, reproducibility, and transparency of research software. Ensuring these qualities is critical for scientific integrity and discovery. This paper asks whether Research Software Science (RSS)--the empirical study of how research software is developed and used--should be considered a form of metascience, the science of science. Classification matters because it could affect recognition, funding, and integration of RSS into research improvement. We define metascience and RSS, compare their principles and objectives, and examine their overlaps. Arguments for classification highlight shared commitments to reproducibility, transparency, and empirical study of research processes. Arguments against portraying RSS as a specialized domain focused on a tool rather than the broader scientific enterprise. Our analysis finds RSS advances core goals of metascience, especially in computational reproducibility, and bridges technical, social, and cognitive aspects of research. Its classification depends on whether one adopts a broad definition of metascience--any empirical effort to improve science--or a narrow one focused on systemic and epistemological structures. We argue RSS is best understood as a distinct interdisciplinary domain that aligns with, and in some definitions fits within, metascience. Recognizing it as such can strengthen its role in improving reliability, justify funding, and elevate software development in research institutions. Regardless of classification, applying scientific rigor to research software ensures the tools of discovery meet the standards of the discoveries themselves.
Outperforming Dijkstra on Sparse Graphs: The Lightning Network Use Case
Venue: arXiv cs.PF 2025
Year: 2025

Efficient routing is critical for payment channel networks (PCNs) such as the Lightning Network (LN), where most clients currently rely on Dijkstra-based algorithms for payment pathfinding. While Dijkstra's algorithm has long been regarded as optimal on sparse graphs, recent theoretical work challenges this view. The new Bounded Multi-Source Shortest Path (BMSSP) algorithm by Duan et al. theoretically achieves $O(m~log^{2/3}~n)$ runtime, which is asymptotically faster than Dijkstra's $O(m + n~log~n)$ on sparse directed graphs. In this paper, we implement BMSSP on Rust and compare its performance against Dijkstra's using real LN topology data. Our evaluation, based on multiple randomized trials and statistical tests, shows that current implementations of BMSSP do not significantly outperform Dijkstra's in practice, and speedups are smaller than what theory predicts, possibly due to implementation and constant factor overheads. These results provide the first empirical evidence of BMSSP's potential to accelerate LN routing and inform future optimizations of PCN pathfinding algorithms.