OpenRaiser/Intern-Atlas huggingface dataset:
SPIRAL integration of generative AI in an undergraduate creative media course: effects on self-efficacy and career outcome expectations
Venue: arXiv cs.HC 2025
Year: 2025

Computing education and computing students are rapidly integrating generative AI, but we know relatively little about how different pedagogical strategies for intentionally integrating generative AI affect students' self-efficacy and career interests. This study investigates a SPIRAL integration of generative AI (Skills Practiced Independently, Revisited with AI Later), implemented in an introductory undergraduate creative media and technology course in Fall 2023 (n=31). Students first developed domain skills for half the semester, then revisited earlier material integrating using generative AI, with explicit instruction on how to use it critically and ethically. We contribute a mixed methods quantitative and qualitative analysis of changes in self-efficacy and career interests over time, including longitudinal qualitative interviews (n=9) and thematic analysis. We found positive changes in both students' creative media self-efficacy and generative AI use self-efficacy, and mixed changes for ethical generative AI use self-efficacy. We also found students experienced demystification, transitioning from initial fear about generative AI taking over their fields and jobs, to doubting AI capability to do so and/or that society will push back against AI, through personal use of AI and observing others' use of AI vicariously. For career interests, our SPIRAL integration of generative AI use appeared to have either a neutral or positive influence on students, including widening their perceived career options, depending on their view of how AI would influence the career itself. These findings suggest that careful pedagogical sequencing can mitigate some potential negative impacts of AI, while promoting ethical and critical AI use that supports or has a neutral effect on students' career formation. To our knowledge our SPIRAL integration strategy applied to generative AI integration is novel.
A generalised editor calculus (Short Paper)
Venue: arXiv cs.CL 2025
Year: 2025

In this paper, we present a generalization of a syntax-directed editor calculus, which can be used to instantiate a specialized syntax-directed editor for any language, given by some abstract syntax. The editor calculus guarantees the absence of syntactical errors while allowing incomplete programs. The generalized editor calculus is then encoded into a simply typed lambda calculus, extended with pairs, booleans, pattern matching and fixed points
On the Dual-Use Dilemma in Physical Reasoning and Force
Venue: arXiv cs.RO 2025
Year: 2025

Humans learn how and when to apply forces in the world via a complex physiological and psychological learning process. Attempting to replicate this in vision-language models (VLMs) presents two challenges: VLMs can produce harmful behavior, which is particularly dangerous for VLM-controlled robots which interact with the world, but imposing behavioral safeguards can limit their functional and ethical extents. We conduct two case studies on safeguarding VLMs which generate forceful robotic motion, finding that safeguards reduce both harmful and helpful behavior involving contact-rich manipulation of human body parts. Then, we discuss the key implication of this result--that value alignment may impede desirable robot capabilities--for model evaluation and robot learning.
Governing Equation Discovery from Data Based on Differential Invariants
Venue: arXiv cs.LG 2025
Year: 2025

The explicit governing equation is one of the simplest and most intuitive forms for characterizing physical laws. However, directly discovering partial differential equations (PDEs) from data poses significant challenges, primarily in determining relevant terms from a vast search space. Symmetry, as a crucial prior knowledge in scientific fields, has been widely applied in tasks such as designing equivariant networks and guiding neural PDE solvers. In this paper, we propose a pipeline for governing equation discovery based on differential invariants, which can losslessly reduce the search space of existing equation discovery methods while strictly adhering to symmetry. Specifically, we compute the set of differential invariants corresponding to the infinitesimal generators of the symmetry group and select them as the relevant terms for equation discovery. Taking DI-SINDy (SINDy based on Differential Invariants) as an example, we demonstrate that its success rate and accuracy in PDE discovery surpass those of other symmetry-informed governing equation discovery methods across a series of PDEs.
Mitigating Deceptive Alignment via Self-Monitoring
Venue: arXiv cs.AI 2025
Year: 2025

Modern large language models rely on chain-of-thought (CoT) reasoning to achieve impressive performance, yet the same mechanism can amplify deceptive alignment, situations in which a model appears aligned while covertly pursuing misaligned goals. Existing safety pipelines treat deception as a black-box output to be filtered post-hoc, leaving the model free to scheme during its internal reasoning. We ask: Can deception be intercepted while the model is thinking? We answer this question, the first framework that embeds a Self-Monitor inside the CoT process itself, named CoT Monitor+. During generation, the model produces (i) ordinary reasoning steps and (ii) an internal self-evaluation signal trained to flag and suppress misaligned strategies. The signal is used as an auxiliary reward in reinforcement learning, creating a feedback loop that rewards honest reasoning and discourages hidden goals. To study deceptive alignment systematically, we introduce DeceptionBench, a five-category benchmark that probes covert alignment-faking, sycophancy, etc. We evaluate various LLMs and show that unrestricted CoT roughly aggravates the deceptive tendency. In contrast, CoT Monitor+ cuts deceptive behaviors by 43.8% on average while preserving task accuracy. Further, when the self-monitor signal replaces an external weak judge in RL fine-tuning, models exhibit substantially fewer obfuscated thoughts and retain transparency. Our project website can be found at cot-monitor-plus.github.io
AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting
Venue: arXiv cs.AI 2025
Year: 2025

Modern large reasoning models demonstrate impressive problem-solving capabilities by employing sophisticated reasoning strategies. However, they often struggle to balance efficiency and effectiveness, frequently generating unnecessarily lengthy reasoning chains for simple problems. In this work, we propose AdaCtrl, a novel framework to support both difficulty-aware adaptive reasoning budget allocation and explicit user control over reasoning depth. AdaCtrl dynamically adjusts its reasoning length based on self-assessed problem difficulty, while also allowing users to manually control the budget to prioritize either efficiency or effectiveness. This is achieved through a two-stage training pipeline: an initial cold-start fine-tuning phase to instill the ability to self-aware difficulty and adjust reasoning budget, followed by a difficulty-aware reinforcement learning (RL) stage that refines the model's adaptive reasoning strategies and calibrates its difficulty assessments based on its evolving capabilities during online training. To enable intuitive user interaction, we design explicit length-triggered tags that function as a natural interface for budget control. Empirical results show that AdaCtrl adapts reasoning length based on estimated difficulty, compared to the standard training baseline that also incorporates fine-tuning and RL, it yields performance improvements and simultaneously reduces response length by 10.06% and 12.14% on the more challenging AIME2024 and AIME2025 datasets, which require elaborate reasoning, and by 62.05% and 91.04% on the MATH500 and GSM8K datasets, where more concise responses are sufficient. Furthermore, AdaCtrl enables precise user control over the reasoning budget, allowing for tailored responses to meet specific needs.
Multi-Party Conversational Agents: A Survey
Venue: arXiv cs.CL 2025
Year: 2025

Multi-party Conversational Agents (MPCAs) are systems designed to engage in dialogue with more than two participants simultaneously. Unlike traditional two-party agents, designing MPCAs faces additional challenges due to the need to interpret both utterance semantics and social dynamics. This survey explores recent progress in MPCAs by addressing three key questions: 1) Can agents model each participants' mental states? (State of Mind Modeling); 2) Can they properly understand the dialogue content? (Semantic Understanding); and 3) Can they reason about and predict future conversation flow? (Agent Action Modeling). We review methods ranging from classical machine learning to Large Language Models (LLMs) and multi-modal systems. Our analysis underscores Theory of Mind (ToM) as essential for building intelligent MPCAs and highlights multi-modal understanding as a promising yet underexplored direction. Finally, this survey offers guidance to future researchers on developing more capable MPCAs.
Securing Credit Inquiries: The Role of Real-Time User Approval in Preventing SSN Identity Theft
Venue: arXiv cs.CR 2025
Year: 2025

Unauthorized credit inquiries are also a central entry point for identity theft, with Social Security Numbers (SSNs) being widely utilized in fraudulent cases. Traditional credit inquiry systems do not usually possess strict user authentication, making them vulnerable to unauthorized access. This paper proposes a real-time user authorization system to enhance security by enforcing explicit user approval before processing any credit inquiry. The system employs real-time verification and approval techniques. This ensures that the authorized user only approves or rejects a credit check request. It minimizes the risks of interference by third parties. Apart from enhancing security, this system complies with regulations like the General Data Protection Regulation (GDPR) and the Fair Credit Reporting Act (FCRA) while maintaining a seamless user experience. This article discusses the technical issues, scaling-up issues, and ways of implementing real-time user authorization in financial systems. Through this framework, financial institutions can drastically minimize the risk of identity theft, avert unauthorized credit checks, and increase customer trust in the credit verification system.
Distribution-Aware Mobility-Assisted Decentralized Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Decentralized federated learning (DFL) has attracted significant attention due to its scalability and independence from a central server. In practice, some participating clients can be mobile, yet the impact of user mobility on DFL performance remains largely unexplored, despite its potential to facilitate communication and model convergence. In this work, we demonstrate that introducing a small fraction of mobile clients, even with random movement, can significantly improve the accuracy of DFL by facilitating information flow. To further enhance performance, we propose novel distribution-aware mobility patterns, where mobile clients strategically navigate the network, leveraging knowledge of data distributions and static client locations. The proposed moving strategies mitigate the impact of data heterogeneity and boost learning convergence. Extensive experiments validate the effectiveness of induced mobility in DFL and demonstrate the superiority of our proposed mobility patterns over random movement.
Sci-LoRA: Mixture of Scientific LoRAs for Cross-Domain Lay Paraphrasing
Venue: arXiv cs.CL 2025
Year: 2025

Lay paraphrasing aims to make scientific information accessible to audiences without technical backgrounds. However, most existing studies focus on a single domain, such as biomedicine. With the rise of interdisciplinary research, it is increasingly necessary to comprehend knowledge spanning multiple technical fields. To address this, we propose Sci-LoRA, a model that leverages a mixture of LoRAs fine-tuned on multiple scientific domains. In particular, Sci-LoRA dynamically generates and applies weights for each LoRA, enabling it to adjust the impact of different domains based on the input text, without requiring explicit domain labels. To balance domain-specific knowledge and generalization across various domains, Sci-LoRA integrates information at both the data and model levels. This dynamic fusion enhances the adaptability and performance across various domains. Experimental results across twelve domains on five public datasets show that Sci-LoRA significantly outperforms state-of-the-art large language models and demonstrates flexible generalization and adaptability in cross-domain lay paraphrasing.
The Parameterized Complexity of Computing the Linear Vertex Arboricity
Venue: arXiv cs.CC 2025
Year: 2025

The \emph{linear vertex arboricity} of a graph is the smallest number of sets into which the vertices of a graph can be partitioned so that each of these sets induces a linear forest. Chaplick et al. [JoCG 2020] showed that, somewhat surprisingly, the linear vertex arboricity of a graph is the same as the \emph{3D weak line cover number} of the graph, that is, the minimum number of straight lines necessary to cover the vertices of a crossing-free straight-line drawing of the graph in $\mathbb{R}^3$. Chaplick et al. [JGAA 2023] showed that deciding whether a given graph has linear vertex arboricity 2 is NP-hard. In this paper, we investigate the parameterized complexity of computing the linear vertex arboricity. We show that the problem is para-NP-hard with respect to the parameter maximum degree. Our result is tight in the following sense. All graphs of maximum degree 4 (except for $K_4$) have linear vertex arboricity at most 2, whereas we show that it is NP-hard to decide, given a graph of maximum degree 5, whether its linear vertex arboricity is 2. Moreover, we show that, for planar graphs, the same question is NP-hard for graphs of maximum degree 6, leaving open the maximum-degree-5 case. Finally, we prove that, for any $k \ge 1$, deciding whether the linear vertex arboricity of a graph is at most $k$ is fixed-parameter tractable with respect to the treewidth of the given graph.
Meta-aware Learning in text-to-SQL Large Language Model
Venue: arXiv cs.AI 2025
Year: 2025

The advancements of Large language models (LLMs) have provided great opportunities to text-to-SQL tasks to overcome the main challenges to understand complex domain information and complex database structures in business applications. In this paper, we propose a meta-aware learning framework to integrate domain knowledge, database schema, chain-of-thought reasoning processes, and metadata relationships to improve the SQL generation quality. The proposed framework includes four learning strategies: schema-based learning, Chain-of-Thought (CoT) learning, knowledge-enhanced learning, and key information tokenization. This approach provides a comprehensive understanding of database structure and metadata information towards LLM through fine-tuning to improve its performance on SQL generation within business domains. Through two experimental studies, we have demonstrated the superiority of the proposed methods in execution accuracy, multi-task SQL generation capability, and reduction of catastrophic forgetting.
Beyond Replacement or Augmentation: How Creative Workers Reconfigure Division of Labor with Generative AI
Venue: arXiv cs.CY 2025
Year: 2025

The introduction of generative AI tools such as ChatGPT into creative workplaces has sparked highly visible, but binary worker replacement and augmentation debates. This study reframes this argument by examining how creative professionals re-specify a division of labor with these tools. Through 17 ethnomethodologically informed interviews with international creative agency workers we demonstrate how roles are assigned to generative AI tools, how their contributions are modified and remediated, and how workers practically manage their outputs to reflect assumptions of internal and external stakeholders. This paper makes 3 unique contributions to CSCW: (1) we conceptualize generative AI prompting as a type of workplace situated, reflexive delegation, (2) we demonstrate that workers must continuously configure and repair AI role boundaries to maintain workplace intelligibility and accountability; and (3) we introduce the notion of interpretive templatized trust, where workers devise strategies to adapt automated generative templates for their setting, and reinforce stakeholder trust. This contribution has implications for organizing productive human-AI work in creative and stakeholder centric environments.
SANNet: A Semantic-Aware Agentic AI Networking Framework for Multi-Agent Cross-Layer Coordination
Venue: arXiv cs.AI 2025
Year: 2025

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm that relies on a large number of specialized AI agents to collaborate and coordinate for autonomous decision-making, dynamic environmental adaptation, and complex goal achievement. It has the potential to facilitate real-time network management alongside capabilities for self-configuration, self-optimization, and self-adaptation across diverse and complex networking environments, laying the foundation for fully autonomous networking systems in the future. Despite its promise, AgentNet is still in the early stage of development, and there still lacks an effective networking framework to support automatic goal discovery and multi-agent self-orchestration and task assignment. This paper proposes SANNet, a novel semantic-aware agentic AI networking architecture that can infer the semantic goal of the user and automatically assign agents associated with different layers of a mobile system to fulfill the inferred goal. Motivated by the fact that one of the major challenges in AgentNet is that different agents may have different and even conflicting objectives when collaborating for certain goals, we introduce a dynamic weighting-based conflict-resolving mechanism to address this issue. We prove that SANNet can provide theoretical guarantee in both conflict-resolving and model generalization performance for multi-agent collaboration in dynamic environment. We develop a hardware prototype of SANNet based on the open RAN and 5GS core platform. Our experimental results show that SANNet can significantly improve the performance of multi-agent networking systems, even when agents with conflicting objectives are selected to collaborate for the same goal.
Online Knowledge Distillation with Reward Guidance
Venue: arXiv cs.LG 2025
Year: 2025

This work studies knowledge distillation (KD) for large language models (LLMs) through preference optimization. We propose a reward-guided imitation learning framework for sequential KD, formulating a min-max optimization problem between the policy and reward model (RM) to minimize the performance gap between the student and teacher policies. Specifically, the reward optimization is constrained to achieve near-optimality within a confidence set for preference alignment. For preference data construction, we explore both offline and online preference-based KD. Additionally, we reformulate the RM using the $Q$-value function and extend the framework to white-box KD, where the teacher policy's predicted probabilities are accessible. Theoretical analysis and empirical results demonstrate the effectiveness of the proposed framework.
CDPDNet: Integrating Text Guidance with Hybrid Vision Encoders for Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Most publicly available medical segmentation datasets are only partially labeled, with annotations provided for a subset of anatomical structures. When multiple datasets are combined for training, this incomplete annotation poses challenges, as it limits the model's ability to learn shared anatomical representations among datasets. Furthermore, vision-only frameworks often fail to capture complex anatomical relationships and task-specific distinctions, leading to reduced segmentation accuracy and poor generalizability to unseen datasets. In this study, we proposed a novel CLIP-DINO Prompt-Driven Segmentation Network (CDPDNet), which combined a self-supervised vision transformer with CLIP-based text embedding and introduced task-specific text prompts to tackle these challenges. Specifically, the framework was constructed upon a convolutional neural network (CNN) and incorporated DINOv2 to extract both fine-grained and global visual features, which were then fused using a multi-head cross-attention module to overcome the limited long-range modeling capability of CNNs. In addition, CLIP-derived text embeddings were projected into the visual space to help model complex relationships among organs and tumors. To further address the partial label challenge and enhance inter-task discriminative capability, a Text-based Task Prompt Generation (TTPG) module that generated task-specific prompts was designed to guide the segmentation. Extensive experiments on multiple medical imaging datasets demonstrated that CDPDNet consistently outperformed existing state-of-the-art segmentation methods. Code and pretrained model are available at: https://github.com/wujiong-hub/CDPDNet.git.
Serial-OE: Anomalous sound detection based on serial method with outlier exposure capable of using small amounts of anomalous data for training
Venue: arXiv cs.SD 2025
Year: 2025

We introduce Serial-OE, a new approach to anomalous sound detection (ASD) that leverages small amounts of anomalous data to improve the performance. Conventional ASD methods rely primarily on the modeling of normal data, due to the cost of collecting anomalous data from various possible types of equipment breakdowns. Our method improves upon existing ASD systems by implementing an outlier exposure framework that utilizes normal and pseudo-anomalous data for training, with the capability to also use small amounts of real anomalous data. A comprehensive evaluation using the DCASE2020 Task2 dataset shows that our method outperforms state-of-the-art ASD models. We also investigate the impact on performance of using a small amount of anomalous data during training, of using data without machine ID information, and of using contaminated training data. Our experimental results reveal the potential of using a very limited amount of anomalous data during training to address the limitations of existing methods using only normal data for training due to the scarcity of anomalous data. This study contributes to the field by presenting a method that can be dynamically adapted to include anomalous data during the operational phase of an ASD system, paving the way for more accurate ASD.
STRICT: Stress Test of Rendering Images Containing Text
Venue: arXiv cs.LG 2025
Year: 2025

While diffusion models have revolutionized text-to-image generation with their ability to synthesize realistic and diverse scenes, they continue to struggle to generate consistent and legible text within images. This shortcoming is commonly attributed to the locality bias inherent in diffusion-based generation, which limits their ability to model long-range spatial dependencies. In this paper, we introduce $\textbf{STRICT}$, a benchmark designed to systematically stress-test the ability of diffusion models to render coherent and instruction-aligned text in images. Our benchmark evaluates models across multiple dimensions: (1) the maximum length of readable text that can be generated; (2) the correctness and legibility of the generated text, and (3) the ratio of not following instructions for generating text. We evaluate several state-of-the-art models, including proprietary and open-source variants, and reveal persistent limitations in long-range consistency and instruction-following capabilities. Our findings provide insights into architectural bottlenecks and motivate future research directions in multimodal generative modeling. We release our entire evaluation pipeline at https://github.com/tianyu-z/STRICT-Bench.