OpenRaiser/Intern-Atlas huggingface dataset:
Retrieval-Augmented Code Review Comment Generation
Venue: arXiv cs.SE 2025
Year: 2025

Automated code review comment generation (RCG) aims to assist developers by automatically producing natural language feedback for code changes. Existing approaches are primarily either generation-based, using pretrained language models, or information retrieval-based (IR), reusing comments from similar past examples. While generation-based methods leverage code-specific pretraining on large code-natural language corpora to learn semantic relationships between code and natural language, they often struggle to generate low-frequency but semantically important tokens due to their probabilistic nature. In contrast, IR-based methods excel at recovering such rare tokens by copying from existing examples but lack flexibility in adapting to new code contexts-for example, when input code contains identifiers or structures not found in the retrieval database. To bridge the gap between generation-based and IR-based methods, this work proposes to leverage retrieval-augmented generation (RAG) for RCG by conditioning pretrained language models on retrieved code-review exemplars. By providing relevant examples that illustrate how similar code has been previously reviewed, the model is better guided to generate accurate review comments. Our evaluation on the Tufano et al. benchmark shows that RAG-based RCG outperforms both generation-based and IR-based RCG. It achieves up to +1.67% higher exact match and +4.25% higher BLEU scores compared to generation-based RCG. It also improves the generation of low-frequency ground-truth tokens by up to 24.01%. We additionally find that performance improves as the number of retrieved exemplars increases.
Further Evidence on a Controversial Topic about Human-Based Experiments: Professionals vs. Students
Venue: arXiv cs.SE 2025
Year: 2025

Most Software Engineering (SE) human-based controlled experiments rely on students as participants, raising concerns about their external validity. Specifically, the realism of results obtained from students and their applicability to the software industry remains in question. In this short paper, we bring further evidence on this controversial point. To do so, we compare 62 students and 42 software professionals on a bug-fixing task on the same Java program. The students were enrolled in a Bachelor's program in Computer Science, while the professionals were employed by two multinational companies (for one of them, the professionals were from two offices). Some variations in the experimental settings of the two groups (students and professionals) were present. For instance, the experimental environment of the experiment with professionals was more realistic; i.e., they faced some stress factors such as interruptions during the bug-fixing task. Considering the differences between the two groups of participants, the gathered data show that the students outperformed the professionals in fixing bugs. This diverges to some extent from past empirical evidence. Rather than presenting definitive conclusions, our results aim to catalyze the discussion on the use of students in experiments and pave the way for future investigations. Specifically, our results encourage us to examine the complex factors influencing SE tasks, making experiments as more realistic as possible.
Accelerating Delta Debugging through Probabilistic Monotonicity Assessment
Venue: arXiv cs.SE 2025
Year: 2025

Delta debugging assumes search space monotonicity: if a program causes a failure, any supersets of that program will also induce the same failure, permitting the exclusion of subsets of non-failure-inducing programs. However, this assumption does not always hold in practice. This paper introduces Probabilistic Monotonicity Assessment (PMA), enhancing the efficiency of DDMIN-style algorithms without sacrificing effectiveness. PMA dynamically models and assesses the search space's monotonicity based on prior tests tried during the debugging process and uses a confidence function to quantify monotonicity, thereby enabling the probabilistic exclusion of subsets of non-failure-inducing programs. Our approach significantly reduces redundant tests that would otherwise be performed, without compromising the quality of the reduction. We evaluated PMA against two leading DDMIN-style tools, CHISEL and ProbDD. Our findings indicate that PMA cuts processing time by 59.2% compared to CHISEL, accelerates the reduction process (i.e., the number of tokens deleted per second) by 3.32x, and decreases the sizes of the final reduced programs by 6.7%. Against ProbDD, PMA reduces processing time by 22.0%, achieves a 1.34x speedup in the reduction process, and further decreases the sizes of the final reduced programs by 3.0%. These findings affirm PMA's role in significantly improving delta debugging's efficiency while maintaining or enhancing its effectiveness.
Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression
Venue: arXiv cs.CV 2025
Year: 2025

Fairness is a critical component of Trustworthy AI. In this paper, we focus on Machine Learning (ML) and the performance of model predictions when dealing with skin color. Unlike other sensitive attributes, the nature of skin color differs significantly. In computer vision, skin color is represented as tensor data rather than categorical values or single numerical points. However, much of the research on fairness across sensitive groups has focused on categorical features such as gender and race. This paper introduces a new technique for evaluating fairness in ML for image classification tasks, specifically without the use of annotation. To address the limitations of prior work, we handle tensor data, like skin color, without classifying it rigidly. Instead, we convert it into probability distributions and apply statistical distance measures. This novel approach allows us to capture fine-grained nuances in fairness both within and across what would traditionally be considered distinct groups. Additionally, we propose an innovative training method to mitigate the latent biases present in conventional skin tone categorization. This method leverages color distance estimates calculated through Bayesian regression with polynomial functions, ensuring a more nuanced and equitable treatment of skin color in ML models.
DTHA: A Digital Twin-Assisted Handover Authentication Scheme for 5G and Beyond
Venue: arXiv cs.CR 2025
Year: 2025

With the rapid development and extensive deployment of the fifth-generation wireless system (5G), it has achieved ubiquitous high-speed connectivity and improved overall communication performance. Additionally, as one of the promising technologies for integration beyond 5G, digital twin in cyberspace can interact with the core network, transmit essential information, and further enhance the wireless communication quality of the corresponding mobile device (MD). However, the utilization of millimeter-wave, terahertz band, and ultra-dense network technologies presents urgent challenges for MD in 5G and beyond, particularly in terms of frequent handover authentication with target base stations during faster mobility, which can cause connection interruption and incur malicious attacks. To address such challenges in 5G and beyond, in this paper, we propose a secure and efficient handover authentication scheme by utilizing digital twin. Acting as an intelligent intermediate, the authorized digital twin can handle computations and assist the corresponding MD in performing secure mutual authentication and key negotiation in advance before attaching the target base stations in both intra-domain and inter-domain scenarios. In addition, we provide the formal verification based on BAN logic, RoR model, and ProVerif, and informal analysis to demonstrate that the proposed scheme can offer diverse security functionality. Performance evaluation shows that the proposed scheme outperforms most related schemes in terms of signaling, computation, and communication overheads.
Configurable Preference Tuning with Rubric-Guided Synthetic Data
Venue: arXiv cs.CL 2025
Year: 2025

Models of human feedback for AI alignment, such as those underpinning Direct Preference Optimization (DPO), often bake in a singular, static set of preferences, limiting adaptability. This paper challenges the assumption of monolithic preferences by introducing Configurable Preference Tuning (CPT), a novel framework for endowing language models with the ability to dynamically adjust their behavior based on explicit, human-interpretable directives. CPT leverages synthetically generated preference data, conditioned on system prompts derived from structured, fine-grained rubrics that define desired attributes like writing style. By fine-tuning with these rubric-guided preferences, the LLM learns to modulate its outputs at inference time in response to the system prompt, without retraining. This approach not only offers fine-grained control but also provides a mechanism for modeling more nuanced and context-dependent human feedback. Several experimental artifacts, such as training code, generated datasets and fine-tuned models are released at https://github.com/vicgalle/configurable-preference-tuning
Dynamic Collaborative Material Distribution System for Intelligent Robots In Smart Manufacturing
Venue: arXiv cs.RO 2025
Year: 2025

The collaboration and interaction of multiple robots have become integral aspects of smart manufacturing. Effective planning and management play a crucial role in achieving energy savings and minimising overall costs. This paper addresses the real-time Dynamic Multiple Sources to Single Destination (DMS-SD) navigation problem, particularly with a material distribution case for multiple intelligent robots in smart manufacturing. Enumerated solutions, such as in \cite{xiao2022efficient}, tackle the problem by generating as many optimal or near-optimal solutions as possible but do not learn patterns from the previous experience, whereas the method in \cite{xiao2023collaborative} only uses limited information from the earlier trajectories. Consequently, these methods may take a considerable amount of time to compute results on large maps, rendering real-time operations impractical. To overcome this challenge, we propose a lightweight Deep Reinforcement Learning (DRL) method to address the DMS-SD problem. The proposed DRL method can be efficiently trained and rapidly converges to the optimal solution using the designed target-guided reward function. A well-trained DRL model significantly reduces the computation time for the next movement to a millisecond level, which improves the time up to 100 times in our experiments compared to the enumerated solutions. Moreover, the trained DRL model can be easily deployed on lightweight devices in smart manufacturing, such as Internet of Things devices and mobile phones, which only require limited computational resources.
ExoStart: Efficient learning for dexterous manipulation with sensorized exoskeleton demonstrations
Venue: arXiv cs.RO 2025
Year: 2025

Recent advancements in teleoperation systems have enabled high-quality data collection for robotic manipulators, showing impressive results in learning manipulation at scale. This progress suggests that extending these capabilities to robotic hands could unlock an even broader range of manipulation skills, especially if we could achieve the same level of dexterity that human hands exhibit. However, teleoperating robotic hands is far from a solved problem, as it presents a significant challenge due to the high degrees of freedom of robotic hands and the complex dynamics occurring during contact-rich settings. In this work, we present ExoStart, a general and scalable learning framework that leverages human dexterity to improve robotic hand control. In particular, we obtain high-quality data by collecting direct demonstrations without a robot in the loop using a sensorized low-cost wearable exoskeleton, capturing the rich behaviors that humans can demonstrate with their own hands. We also propose a simulation-based dynamics filter that generates dynamically feasible trajectories from the collected demonstrations and use the generated trajectories to bootstrap an auto-curriculum reinforcement learning method that relies only on simple sparse rewards. The ExoStart pipeline is generalizable and yields robust policies that transfer zero-shot to the real robot. Our results demonstrate that ExoStart can generate dexterous real-world hand skills, achieving a success rate above 50% on a wide range of complex tasks such as opening an AirPods case or inserting and turning a key in a lock. More details and videos can be found in https://sites.google.com/view/exostart.
GeoPandas-AI: A Smart Class Bringing LLM as Stateful AI Code Assistant
Venue: arXiv cs.HC 2025
Year: 2025

Geospatial data analysis plays a crucial role in tackling intricate societal challenges such as urban planning and climate modeling. However, employing tools like GeoPandas, a prominent Python library for geospatial data manipulation, necessitates expertise in complex domain-specific syntax and workflows. GeoPandas-AI addresses this gap by integrating LLMs directly into the GeoPandas workflow, transforming the GeoDataFrame class into an intelligent, stateful class for both data analysis and geospatial code development. This paper formalizes the design of such a smart class and provides an open-source implementation of GeoPandas-AI in PyPI package manager. Through its innovative combination of conversational interfaces and stateful exploitation of LLMs for code generation and data analysis, GeoPandas-AI introduces a new paradigm for code-copilots and instantiates it for geospatial development.
Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation
Venue: arXiv cs.LG 2025
Year: 2025

Evaluating feature attribution methods represents a critical challenge in explainable AI (XAI), as researchers typically rely on perturbation-based metrics when ground truth is unavailable. However, recent work reveals that these evaluation metrics can show different performance across predicted classes within the same dataset. These "class-dependent evaluation effects" raise questions about whether perturbation analysis reliably measures attribution quality, with direct implications for XAI method development and evaluation trustworthiness. We investigate under which conditions these class-dependent effects arise by conducting controlled experiments with synthetic time series data where ground truth feature locations are known. We systematically vary feature types and class contrasts across binary classification tasks, then compare perturbation-based degradation scores with ground truth-based precision-recall metrics using multiple attribution methods. Our experiments demonstrate that class-dependent effects emerge with both evaluation approaches, even in simple scenarios with temporally localized features, triggered by basic variations in feature amplitude or temporal extent between classes. Most critically, we find that perturbation-based and ground truth metrics frequently yield contradictory assessments of attribution quality across classes, with weak correlations between evaluation approaches. These findings suggest that researchers should interpret perturbation-based metrics with care, as they may not always align with whether attributions correctly identify discriminating features. By showing this disconnect, our work points toward reconsidering what attribution evaluation actually measures and developing more rigorous evaluation methods that capture multiple dimensions of attribution quality.
Are Multimodal Large Language Models Pragmatically Competent Listeners in Simple Reference Resolution Tasks?
Venue: arXiv cs.CL 2025
Year: 2025

We investigate the linguistic abilities of multimodal large language models in reference resolution tasks featuring simple yet abstract visual stimuli, such as color patches and color grids. Although the task may not seem challenging for today's language models, being straightforward for human dyads, we consider it to be a highly relevant probe of the pragmatic capabilities of MLLMs. Our results and analyses indeed suggest that basic pragmatic capabilities, such as context-dependent interpretation of color descriptions, still constitute major challenges for state-of-the-art MLLMs.
Your Ride, Your Rules: Psychology and Cognition Enabled Automated Driving Systems
Venue: arXiv cs.RO 2025
Year: 2025

Despite rapid advances in autonomous driving technology, current autonomous vehicles (AVs) lack effective bidirectional human-machine communication, limiting their ability to personalize the riding experience and recover from uncertain or immobilized states. This limitation undermines occupant comfort and trust, potentially hindering the adoption of AV technologies. We propose PACE-ADS (Psychology and Cognition Enabled Automated Driving Systems), a human-centered autonomy framework enabling AVs to sense, interpret, and respond to both external traffic conditions and internal occupant states. PACE-ADS uses an agentic workflow where three foundation model agents collaborate: the Driver Agent interprets the external environment; the Psychologist Agent decodes passive psychological signals (e.g., EEG, heart rate, facial expressions) and active cognitive inputs (e.g., verbal commands); and the Coordinator Agent synthesizes these inputs to generate high-level decisions that enhance responsiveness and personalize the ride. PACE-ADS complements, rather than replaces, conventional AV modules. It operates at the semantic planning layer, while delegating low-level control to native systems. The framework activates only when changes in the rider's psychological state are detected or when occupant instructions are issued. It integrates into existing AV platforms with minimal adjustments, positioning PACE-ADS as a scalable enhancement. We evaluate it in closed-loop simulations across diverse traffic scenarios, including intersections, pedestrian interactions, work zones, and car-following. Results show improved ride comfort, dynamic behavioral adjustment, and safe recovery from edge-case scenarios via autonomous reasoning or rider input. PACE-ADS bridges the gap between technical autonomy and human-centered mobility.
Measurement-Aligned Sampling for Inverse Problem
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion models provide a powerful way to incorporate complex prior information for solving inverse problems. However, existing methods struggle to correctly incorporate guidance from conflicting signals in the prior and measurement, and often failed to maximizing the consistency to the measurement, especially in the challenging setting of non-Gaussian or unknown noise. To address these issues, we propose Measurement-Aligned Sampling (MAS), a novel framework for linear inverse problem solving that flexibly balances prior and measurement information. MAS unifies and extends existing approaches such as DDNM, TMPD, while generalizing to handle both known Gaussian noise and unknown or non-Gaussian noise types. Extensive experiments demonstrate that MAS consistently outperforms state-of-the-art methods across a variety of tasks, while maintaining relatively low computational cost.
Real-World Deployment of a Lane Change Prediction Architecture Based on Knowledge Graph Embeddings and Bayesian Inference
Venue: arXiv cs.AR 2025
Year: 2025

Research on lane change prediction has gained a lot of momentum in the last couple of years. However, most research is confined to simulation or results obtained from datasets, leaving a gap between algorithmic advances and on-road deployment. This work closes that gap by demonstrating, on real hardware, a lane-change prediction system based on Knowledge Graph Embeddings (KGEs) and Bayesian inference. Moreover, the ego-vehicle employs a longitudinal braking action to ensure the safety of both itself and the surrounding vehicles. Our architecture consists of two modules: (i) a perception module that senses the environment, derives input numerical features, and converts them into linguistic categories; and communicates them to the prediction module; (ii) a pretrained prediction module that executes a KGE and Bayesian inference model to anticipate the target vehicle's maneuver and transforms the prediction into longitudinal braking action. Real-world hardware experimental validation demonstrates that our prediction system anticipates the target vehicle's lane change three to four seconds in advance, providing the ego vehicle sufficient time to react and allowing the target vehicle to make the lane change safely.
Subjective Experience in AI Systems: What Do AI Researchers and the Public Believe?
Venue: arXiv cs.CY 2025
Year: 2025

We surveyed 582 AI researchers who have published in leading AI venues and 838 nationally representative US participants about their views on the potential development of AI systems with subjective experience and how such systems should be treated and governed. When asked to estimate the chances that such systems will exist on specific dates, the median responses were 1% (AI researchers) and 5% (public) by 2024, 25% and 30% by 2034, and 70% and 60% by 2100, respectively. The median member of the public thought there was a higher chance that AI systems with subjective experience would never exist (25%) than the median AI researcher did (10%). Both groups perceived a need for multidisciplinary expertise to assess AI subjective experience. Although support for welfare protections for such AI systems exceeded opposition, it remained far lower than support for protections for animals or the environment. Attitudes toward moral and governance issues were divided in both groups, especially regarding whether such systems should be created and what rights or protections they should receive. Yet a majority of respondents in both groups agreed that safeguards against the potential risks from AI systems with subjective experience should be implemented by AI developers now, and if created, AI systems with subjective experience should treat others well, behave ethically, and be held accountable. Overall, these results suggest that both AI researchers and the public regard the emergence of AI systems with subjective experience as a possibility this century, though substantial uncertainty and disagreement remain about the timeline and appropriate response.
Secure API-Driven Research Automation to Accelerate Scientific Discovery
Venue: arXiv cs.DC 2025
Year: 2025

The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M revolutionizes programmatic access to high performance computing (HPC) while maintaining uncompromising security. This framework allows intelligent agents and experimental facilities to dynamically provision resources and execute complex workflows, accelerating experimental lifecycles, and unlocking the full potential of AI-augmented autonomous science. S3M signals a new era in scientific computing infrastructure that eliminates traditional barriers between researchers, computational resources, and experimental facilities.