OpenRaiser/Intern-Atlas huggingface dataset:
Exploring the Role of Theory of Mind in Human Decision Making: Cognitive, Spatial, and Emotional Influences in the Adversarial Rock-Paper-Scissors Game
Venue: arXiv cs.HC 2025
Year: 2025

Understanding how humans attribute beliefs, goals, and intentions to others, known as theory of mind (ToM), is critical in the context of human-computer interaction. Despite various metrics used to assess ToM, the interplay between cognitive, spatial, and emotional factors in influencing human decision making during adversarial interactions remains underexplored. This paper investigates these relationships using the Rock-Paper-Scissors (RPS) game as a testbed. Through established ToM tests, we analyze how cognitive reasoning, spatial awareness, and emotional perceptiveness affect human performance when interacting with bots and human opponents in repeated RPS settings. Our findings reveal significant correlations among certain ToM metrics and highlight humans' ability to detect patterns in opponents' actions. However, most individual ToM metrics proved insufficient for predicting performance variations, with recursive thinking being the only metric moderately associated with decision effectiveness. Through exploratory factor analysis (EFA) and structural equation modeling (SEM), we identified two latent factors influencing decision effectiveness: Factor 1, characterized by recursive thinking, emotional perceptiveness, and spatial reasoning, positively affects decision-making against dynamic bots and human players, while Factor 2, linked to interpersonal skills and rational ability, has a negative impact. These insights lay the groundwork for further research on ToM metrics and for designing more intuitive, adaptive systems that better anticipate and adapt to human behavior, ultimately enhancing human-machine collaboration.
Adaptive Time Budgets for Safe and Comfortable Vehicle Control Transition in Conditionally Automated Driving
Venue: arXiv cs.HC 2025
Year: 2025

Conditionally automated driving requires drivers to resume vehicle control promptly when automation reaches its operational limits. Ensuring smooth vehicle control transitions is critical for the safety and efficiency of mixed-traffic transportation systems, where complex interactions and variable traffic behaviors pose additional challenges. This study addresses this challenge by introducing an adaptive time budget framework that provides drivers with sufficient time to complete takeovers both safely and comfortably across diverse scenarios. We focus in particular on the takeover buffer, that is, the extra time available after drivers consciously resume control to complete evasive maneuvers. A driving simulator experiment is conducted to evaluate the influence of different takeover buffer lengths on safety-related indicators (minimum time-to-collision, maximum deceleration, and steering wheel angle) and subjective assessments (perceived time sufficiency, perceived risk, and performance satisfaction). Results show that (i) takeover buffers of about 5-6 seconds consistently lead to optimal safety and comfort; and (ii) drivers prefer relatively stable takeover buffers across varying traffic densities and n-back tasks. This study introduces an adaptive time budget framework that dynamically allocates transition time by incorporating a predicted takeover time and a preferred takeover buffer (piecewise function). This can serve as an important first step toward providing drivers with sufficient time to resume vehicle control across diverse scenarios, which needs to be validated in more diverse and real-world driving contexts. By aligning the provided time budget with driver needs under specific circumstances, the adaptive framework can improve reliability of control transitions, facilitate human-centered automated driving, reduce crash risk, and maintain overall traffic efficiency.
When AI Meets the Web: Prompt Injection Risks in Third-Party AI Chatbot Plugins
Venue: arXiv cs.CR 2025
Year: 2025

Prompt injection attacks pose a critical threat to large language models (LLMs), with prior work focusing on cutting-edge LLM applications like personal copilots. In contrast, simpler LLM applications, such as customer service chatbots, are widespread on the web, yet their security posture and exposure to such attacks remain poorly understood. These applications often rely on third-party chatbot plugins that act as intermediaries to commercial LLM APIs, offering non-expert website builders intuitive ways to customize chatbot behaviors. To bridge this gap, we present the first large-scale study of 17 third-party chatbot plugins used by over 10,000 public websites, uncovering previously unknown prompt injection risks in practice. First, 8 of these plugins (used by 8,000 websites) fail to enforce the integrity of the conversation history transmitted in network requests between the website visitor and the chatbot. This oversight amplifies the impact of direct prompt injection attacks by allowing adversaries to forge conversation histories (including fake system messages), boosting their ability to elicit unintended behavior (e.g., code generation) by 3 to 8x. Second, 15 plugins offer tools, such as web-scraping, to enrich the chatbot's context with website-specific content. However, these tools do not distinguish the website's trusted content (e.g., product descriptions) from untrusted, third-party content (e.g., customer reviews), introducing a risk of indirect prompt injection. Notably, we found that ~13% of e-commerce websites have already exposed their chatbots to third-party content. We systematically evaluate both vulnerabilities through controlled experiments grounded in real-world observations, focusing on factors such as system prompt design and the underlying LLM. Our findings show that many plugins adopt insecure practices that undermine the built-in LLM safeguards.
Catching Contamination Before Generation: Spectral Kill Switches for Agents
Venue: arXiv cs.LG 2025
Year: 2025

Agentic language models compose multi step reasoning chains, yet intermediate steps can be corrupted by inconsistent context, retrieval errors, or adversarial inputs, which makes post hoc evaluation too late because errors propagate before detection. We introduce a diagnostic that requires no additional training and uses only the forward pass to emit a binary accept or reject signal during agent execution. The method analyzes token graphs induced by attention and computes two spectral statistics in early layers, namely the high frequency energy ratio and spectral entropy. We formalize these signals, establish invariances, and provide finite sample estimators with uncertainty quantification. Under a two regime mixture assumption with a monotone likelihood ratio property, we show that a single threshold on the high frequency energy ratio is optimal in the Bayes sense for detecting context inconsistency. Empirically, the high frequency energy ratio exhibits robust bimodality during context verification across multiple model families, which enables gating decisions with overhead below one millisecond on our hardware and configurations. We demonstrate integration into retrieval augmented agent pipelines and discuss deployment as an inline safety monitor. The approach detects contamination while the model is still processing the text, before errors commit to the reasoning chain.
An Empirical Study of Java Code Improvements Based on Stack Overflow Answer Edits
Venue: arXiv cs.SE 2025
Year: 2025

Suboptimal code is prevalent in software systems. Developers often write low-quality code due to factors like technical knowledge gaps, insufficient experience, time pressure, management decisions, or personal factors. Once integrated, the accumulation of this suboptimal code leads to significant maintenance costs and technical debt. Developers frequently consult external knowledge bases, such as API documentation and Q&A websites like Stack Overflow (SO), to aid their programming tasks. SO's crowdsourced, collaborative nature has created a vast repository of programming knowledge. Its community-curated content is constantly evolving, with new answers posted or existing ones edited. In this paper, we present an empirical study of SO Java answer edits and their application to improving code in open-source projects. We use a modified code clone search tool to analyze SO code snippets with version history and apply it to open-source Java projects. This identifies outdated or unoptimized code and suggests improved alternatives. Analyzing 140,840 Java accepted answers from SOTorrent and 10,668 GitHub Java projects, we manually categorized SO answer edits and created pull requests to open-source projects with the suggested code improvements. Our results show that 6.91% of SO Java accepted answers have more than one revision (average of 2.82). Moreover, 49.24% of the code snippets in the answer edits are applicable to open-source projects, and 11 out of 36 proposed bug fixes based on these edits were accepted by the GitHub project maintainers.
Parametric Pareto Set Learning for Expensive Multi-Objective Optimization
Venue: arXiv cs.NE 2025
Year: 2025

Parametric multi-objective optimization (PMO) addresses the challenge of solving an infinite family of multi-objective optimization problems, where optimal solutions must adapt to varying parameters. Traditional methods require re-execution for each parameter configuration, leading to prohibitive costs when objective evaluations are computationally expensive. To address this issue, we propose Parametric Pareto Set Learning with multi-objective Bayesian Optimization (PPSL-MOBO), a novel framework that learns a unified mapping from both preferences and parameters to Pareto-optimal solutions. PPSL-MOBO leverages a hypernetwork with Low-Rank Adaptation (LoRA) to efficiently capture parametric variations, while integrating Gaussian process surrogates and hypervolume-based acquisition to minimize expensive function evaluations. We demonstrate PPSL-MOBO's effectiveness on two challenging applications: multi-objective optimization with shared components, where certain design variables must be identical across solution families due to modular constraints, and dynamic multi-objective optimization, where objectives evolve over time. Unlike existing methods that cannot directly solve PMO problems in a unified manner, PPSL-MOBO learns a single model that generalizes across the entire parameter space. By enabling instant inference of Pareto sets for new parameter values without retraining, PPSL-MOBO provides an efficient solution for expensive PMO problems.
TYrPPG: Uncomplicated and Enhanced Learning Capability rPPG for Remote Heart Rate Estimation
Venue: arXiv cs.CV 2025
Year: 2025

Remote photoplethysmography (rPPG) can remotely extract physiological signals from RGB video, which has many advantages in detecting heart rate, such as low cost and no invasion to patients. The existing rPPG model is usually based on the transformer module, which has low computation efficiency. Recently, the Mamba model has garnered increasing attention due to its efficient performance in natural language processing tasks, demonstrating potential as a substitute for transformer-based algorithms. However, the Mambaout model and its variants prove that the SSM module, which is the core component of the Mamba model, is unnecessary for the vision task. Therefore, we hope to prove the feasibility of using the Mambaout-based module to remotely learn the heart rate. Specifically, we propose a novel rPPG algorithm called uncomplicated and enhanced learning capability rPPG (TYrPPG). This paper introduces an innovative gated video understanding block (GVB) designed for efficient analysis of RGB videos. Based on the Mambaout structure, this block integrates 2D-CNN and 3D-CNN to enhance video understanding for analysis. In addition, we propose a comprehensive supervised loss function (CSL) to improve the model's learning capability, along with its weakly supervised variants. The experiments show that our TYrPPG can achieve state-of-the-art performance in commonly used datasets, indicating its prospects and superiority in remote heart rate estimation. The source code is available at https://github.com/Taixi-CHEN/TYrPPG.
Retrieval Quality at Context Limit
Venue: arXiv cs.IR 2025
Year: 2025

The ability of large language models (LLMs) to recall and retrieve information from long contexts is critical for many real-world applications. Prior work (Liu et al., 2023) reported that LLMs suffer significant drops in retrieval accuracy for facts placed in the middle of large contexts, an effect known as "Lost in the Middle" (LITM). We find the model Gemini 2.5 Flash can answer needle-in-a-haystack questions with great accuracy regardless of document position including when the document is nearly at the input context limit. Our results suggest that the "Lost in the Middle" effect is not present for simple factoid Q\&A in Gemini 2.5 Flash, indicating substantial improvements in long-context retrieval.
CGCE: Classifier-Guided Concept Erasure in Generative Models
Venue: arXiv cs.CV 2025
Year: 2025

Recent advancements in large-scale generative models have enabled the creation of high-quality images and videos, but have also raised significant safety concerns regarding the generation of unsafe content. To mitigate this, concept erasure methods have been developed to remove undesirable concepts from pre-trained models. However, existing methods remain vulnerable to adversarial attacks that can regenerate the erased content. Moreover, achieving robust erasure often degrades the model's generative quality for safe, unrelated concepts, creating a difficult trade-off between safety and performance. To address this challenge, we introduce Classifier-Guided Concept Erasure (CGCE), an efficient plug-and-play framework that provides robust concept erasure for diverse generative models without altering their original weights. CGCE uses a lightweight classifier operating on text embeddings to first detect and then refine prompts containing undesired concepts. This approach is highly scalable, allowing for multi-concept erasure by aggregating guidance from several classifiers. By modifying only unsafe embeddings at inference time, our method prevents harmful content generation while preserving the model's original quality on benign prompts. Extensive experiments show that CGCE achieves state-of-the-art robustness against a wide range of red-teaming attacks. Our approach also maintains high generative utility, demonstrating a superior balance between safety and performance. We showcase the versatility of CGCE through its successful application to various modern T2I and T2V models, establishing it as a practical and effective solution for safe generative AI.
Towards a Humanized Social-Media Ecosystem: AI-Augmented HCI Design Patterns for Safety, Agency & Well-Being
Venue: arXiv cs.HC 2025
Year: 2025

Social platforms connect billions of people, yet their engagement-first algorithms often work on users rather than with them, amplifying stress, misinformation, and a loss of control. We propose Human-Layer AI (HL-AI)--user-owned, explainable intermediaries that sit in the browser between platform logic and the interface. HL-AI gives people practical, moment-to-moment control without requiring platform cooperation. We contribute a working Chrome/Edge prototype implementing five representative pattern frameworks--Context-Aware Post Rewriter, Post Integrity Meter, Granular Feed Curator, Micro-Withdrawal Agent, and Recovery Mode--alongside a unifying mathematical formulation balancing user utility, autonomy costs, and risk thresholds. Evaluation spans technical accuracy, usability, and behavioral outcomes. The result is a suite of humane controls that help users rewrite before harm, read with integrity cues, tune feeds with intention, pause compulsive loops, and seek shelter during harassment, all while preserving agency through explanations and override options. This prototype offers a practical path to retrofit today's feeds with safety, agency, and well-being, inviting rigorous cross-cultural user evaluation.
MoEGCL: Mixture of Ego-Graphs Contrastive Representation Learning for Multi-View Clustering
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, the advancement of Graph Neural Networks (GNNs) has significantly propelled progress in Multi-View Clustering (MVC). However, existing methods face the problem of coarse-grained graph fusion. Specifically, current approaches typically generate a separate graph structure for each view and then perform weighted fusion of graph structures at the view level, which is a relatively rough strategy. To address this limitation, we present a novel Mixture of Ego-Graphs Contrastive Representation Learning (MoEGCL). It mainly consists of two modules. In particular, we propose an innovative Mixture of Ego-Graphs Fusion (MoEGF), which constructs ego graphs and utilizes a Mixture-of-Experts network to implement fine-grained fusion of ego graphs at the sample level, rather than the conventional view-level fusion. Additionally, we present the Ego Graph Contrastive Learning (EGCL) module to align the fused representation with the view-specific representation. The EGCL module enhances the representation similarity of samples from the same cluster, not merely from the same sample, further boosting fine-grained graph representation. Extensive experiments demonstrate that MoEGCL achieves state-of-the-art results in deep multi-view clustering tasks. The source code is publicly available at https://github.com/HackerHyper/MoEGCL.
Research On CODP Localization Decision Model Of Automotive Supply Chain Based On Delayed Manufacturing Strategy
Venue: arXiv cs.SI 2025
Year: 2025

Under the market background of increasingly personalized product demand and compressed response cycle, the traditional manufacturing model with standardized mass production as the core has been difficult to meet the dual expectations of customers for differentiation and fast delivery. In order to improve the efficiency of resource allocation and market response, automobile manufacturers need to build a production system that takes into account cost and flexibility. Based on the delayed response manufacturing strategy, this study built an order response node configuration model suitable for automotive manufacturing scenarios, focusing on the positioning of order driven intervention points in the production process. The model comprehensively considers the structural cost changes brought by process adjustment, the dynamic characteristics of the changes of unit manufacturing cost and intermediate inventory cost at different stages with the location of nodes, and introduces delivery time constraints to embed time factors into the inventory decision logic to enhance the practicality of the model and the adaptation of realistic constraints. In terms of solution methods, this paper adopts function fitting and simulation analysis methods, combined with mathematical modeling tools, systematically describes the change trend of total cost, and verifies the rationality and effectiveness of the model structure and solution through actual enterprise cases. The research results provide a theoretical basis and decision support for automobile manufacturing enterprises to realize the synergy of flexible production and cost control in the environment of variable demand, and also provide an empirical reference for the implementation path and system optimization of subsequent relevant strategies.
CoEdge-RAG: Optimizing Hierarchical Scheduling for Retrieval-Augmented LLMs in Collaborative Edge Computing
Venue: arXiv cs.DC 2025
Year: 2025

Motivated by the imperative for real-time responsiveness and data privacy preservation, large language models (LLMs) are increasingly deployed on resource-constrained edge devices to enable localized inference. To improve output quality, retrieval-augmented generation (RAG) is an efficient technique that seamlessly integrates local data into LLMs. However, existing edge computing paradigms primarily focus on single-node optimization, neglecting opportunities to holistically exploit distributed data and heterogeneous resources through cross-node collaboration. To bridge this gap, we propose CoEdge-RAG, a hierarchical scheduling framework for retrieval-augmented LLMs in collaborative edge computing. In general, privacy constraints preclude accurate a priori acquisition of heterogeneous data distributions across edge nodes, directly impeding RAG performance optimization. Thus, we first design an online query identification mechanism using proximal policy optimization (PPO), which autonomously infers query semantics and establishes cross-domain knowledge associations in an online manner. Second, we devise a dynamic inter-node scheduling strategy that balances workloads across heterogeneous edge nodes by synergizing historical performance analytics with real-time resource thresholds. Third, we develop an intra-node scheduler based on online convex optimization, adaptively allocating query processing ratios and memory resources to optimize the latency-quality trade-off under fluctuating assigned loads. Comprehensive evaluations across diverse QA benchmarks demonstrate that our proposed method significantly boosts the performance of collaborative retrieval-augmented LLMs, achieving performance gains of 4.23\% to 91.39\% over baseline methods across all tasks.
Artificial intelligence and the Gulf Cooperation Council workforce adapting to the future of work
Venue: arXiv cs.CY 2025
Year: 2025

The rapid expansion of artificial intelligence (AI) in the Gulf Cooperation Council (GCC) raises a central question: are investments in compute infrastructure matched by an equally robust build-out of skills, incentives, and governance? Grounded in socio-technical systems (STS) theory, this mixed-methods study audits workforce preparedness across Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), Qatar, Kuwait, Bahrain, and Oman. We combine term frequency--inverse document frequency (TF--IDF) analysis of six national AI strategies (NASs), an inventory of 47 publicly disclosed AI initiatives (January 2017--April 2025), paired case studies, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and the Saudi Data & Artificial Intelligence Authority (SDAIA) Academy, and a scenario matrix linking oil-revenue slack (technical capacity) to regulatory coherence (social alignment). Across the corpus, 34/47 initiatives (0.72; 95% Wilson CI 0.58--0.83) exhibit joint social--technical design; country-level indices span 0.57--0.90 (small n; intervals overlap). Scenario results suggest that, under our modeled conditions, regulatory convergence plausibly binds outcomes more than fiscal capacity: fragmented rules can offset high oil revenues, while harmonized standards help preserve progress under austerity. We also identify an emerging two-track talent system, research elites versus rapidly trained practitioners, that risks labor-market bifurcation without bridging mechanisms. By extending STS inquiry to oil-rich, state-led economies, the study refines theory and sets a research agenda focused on longitudinal coupling metrics, ethnographies of coordination, and outcome-based performance indicators.