OpenRaiser/Intern-Atlas huggingface dataset:
Hype and Adoption of Generative Artificial Intelligence Applications
Venue: arXiv cs.CY 2025
Year: 2025

New technologies create opportunities while displacing others. They enhance life by supporting entertainment, education, and social connectivity but also replace humans in productivity and analytical tasks. Adapting to these shifts requires technical adjustments and social readiness. For digital transformation to succeed, organizations and their workforce must be psychologically prepared. We are entering the era of Generative AI with tools like ChatGPT, Bing AI, and Microsoft Office Copilot. Understanding public sentiment toward these innovations is crucial for refining technology acceptance models and informing market strategies. Using the Gartner Hype Cycle and Kubler-Ross Change Curve, this study suggests that generative AI adoption is a dual-stage process. It follows the phases of technology trigger, peak of expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity, while also reflecting emotional stages like shock, denial, and integration. The study used sentiment and emotion analysis on a large dataset of tweets about generative AI, translating them into scores to track user responses over time. Unlike prior research, which offered a snapshot of sentiment, this study captures the dynamic evolution of attitudes, linking empirical evidence with theoretical frameworks. It shifts the focus from information seekers to content creators. With the release of generative AI tools, there is a significant gap in understanding societal reception and adaptation. Policymakers face uncertainty about guiding markets for these changes. This research validates the applicability of the Gartner Hype Cycle and Kubler-Ross Change Curve to generative AI. It provides insights for businesses in integrating these tools and crafting policies to enhance readiness and resilience.
Reliable and efficient inverse analysis using physics-informed neural networks with normalized distance functions and adaptive weight tuning
Venue: arXiv cs.LG 2025
Year: 2025

Physics-informed neural networks have attracted significant attention in scientific machine learning for their capability to solve forward and inverse problems governed by partial differential equations. However, the accuracy of PINN solutions is often limited by the treatment of boundary conditions. Conventional penalty-based methods, which incorporate boundary conditions as penalty terms in the loss function, cannot guarantee exact satisfaction of the given boundary conditions and are highly sensitive to the choice of penalty parameters. This paper demonstrates that distance functions, specifically R-functions, can be leveraged to enforce boundary conditions, overcoming these limitations. R-functions provide normalized distance fields, enabling flexible representation of boundary geometries, including non-convex domains, and facilitating various types of boundary conditions. Nevertheless, distance functions alone are insufficient for accurate inverse analysis in PINNs. To address this, we propose an integrated framework that combines the normalized distance field with bias-corrected adaptive weight tuning to improve both accuracy and efficiency. Numerical results show that the proposed method provides more accurate and efficient solutions to various inverse problems than penalty-based approaches, even in the presence of non-convex geometries with complex boundary conditions. This approach offers a reliable and efficient framework for inverse analysis using PINNs, with potential applications across a wide range of engineering problems.
Subject-independent Classification of Meditative State from the Resting State using EEG
Venue: arXiv cs.LG 2025
Year: 2025

While it is beneficial to objectively determine whether a subject is meditating, most research in the literature reports good results only in a subject-dependent manner. This study aims to distinguish the modified state of consciousness experienced during Rajyoga meditation from the resting state of the brain in a subject-independent manner using EEG data. Three architectures have been proposed and evaluated: The CSP-LDA Architecture utilizes common spatial pattern (CSP) for feature extraction and linear discriminant analysis (LDA) for classification. The CSP-LDA-LSTM Architecture employs CSP for feature extraction, LDA for dimensionality reduction, and long short-term memory (LSTM) networks for classification, modeling the binary classification problem as a sequence learning problem. The SVD-NN Architecture uses singular value decomposition (SVD) to select the most relevant components of the EEG signals and a shallow neural network (NN) for classification. The CSP-LDA-LSTM architecture gives the best performance with 98.2% accuracy for intra-subject classification. The SVD-NN architecture provides significant performance with 96.4\% accuracy for inter-subject classification. This is comparable to the best-reported accuracies in the literature for intra-subject classification. Both architectures are capable of capturing subject-invariant EEG features for effectively classifying the meditative state from the resting state. The high intra-subject and inter-subject classification accuracies indicate these systems' robustness and their ability to generalize across different subjects.
Application and Optimization of Large Models Based on Prompt Tuning for Fact-Check-Worthiness Estimation
Venue: arXiv cs.CL 2025
Year: 2025

In response to the growing problem of misinformation in the context of globalization and informatization, this paper proposes a classification method for fact-check-worthiness estimation based on prompt tuning. We construct a model for fact-check-worthiness estimation at the methodological level using prompt tuning. By applying designed prompt templates to large language models, we establish in-context learning and leverage prompt tuning technology to improve the accuracy of determining whether claims have fact-check-worthiness, particularly when dealing with limited or unlabeled data. Through extensive experiments on public datasets, we demonstrate that the proposed method surpasses or matches multiple baseline methods in the classification task of fact-check-worthiness estimation assessment, including classical pre-trained models such as BERT, as well as recent popular large models like GPT-3.5 and GPT-4. Experiments show that the prompt tuning-based method proposed in this study exhibits certain advantages in evaluation metrics such as F1 score and accuracy, thereby effectively validating its effectiveness and advancement in the task of fact-check-worthiness estimation.
Comparative Study on the Discourse Meaning of Chinese and English Media in the Paris Olympics Based on LDA Topic Modeling Technology and LLM Prompt Engineering
Venue: arXiv cs.CL 2025
Year: 2025

This study analyzes Chinese and English media reports on the Paris Olympics using topic modeling, Large Language Model (LLM) prompt engineering, and corpus phraseology methods to explore similarities and differences in discourse construction and attitudinal meanings. Common topics include the opening ceremony, athlete performance, and sponsorship brands. Chinese media focus on specific sports, sports spirit, doping controversies, and new technologies, while English media focus on female athletes, medal wins, and eligibility controversies. Chinese reports show more frequent prepositional co-occurrences and positive semantic prosody in describing the opening ceremony and sports spirit. English reports exhibit positive semantic prosody when covering female athletes but negative prosody in predicting opening ceremony reactions and discussing women's boxing controversies.
Study on Real-Time Road Surface Reconstruction Using Stereo Vision
Venue: arXiv cs.CV 2025
Year: 2025

Road surface reconstruction plays a crucial role in autonomous driving, providing essential information for safe and smooth navigation. This paper enhances the RoadBEV [1] framework for real-time inference on edge devices by optimizing both efficiency and accuracy. To achieve this, we proposed to apply Isomorphic Global Structured Pruning to the stereo feature extraction backbone, reducing network complexity while maintaining performance. Additionally, the head network is redesigned with an optimized hourglass structure, dynamic attention heads, reduced feature channels, mixed precision inference, and efficient probability volume computation. Our approach improves inference speed while achieving lower reconstruction error, making it well-suited for real-time road surface reconstruction in autonomous driving.
EcoServe: Enabling Cost-effective LLM Serving with Proactive Intra- and Inter-Instance Orchestration
Venue: arXiv cs.DC 2025
Year: 2025

Existing LLM serving strategies can be categorized based on whether prefill and decode phases are disaggregated: non-disaggregated (NoDG) or fully disaggregated (FuDG). However, the NoDG strategy leads to strong prefill-decode interference and the FuDG strategy highly relies on high-performance interconnects, making them less cost-effective. We introduce EcoServe, a system that enables cost-effective LLM serving on clusters with commodity interconnects. EcoServe is built on the partially disaggregated (PaDG) strategy, applying temporal disaggregation and rolling activation for proactive intra- and inter-instance scheduling. It first disaggregates the prefill and decode phases along the time dimension within a single instance to mitigate inter-phase interference and enhance throughput. Next, it coordinates multiple instances and cyclically activates them to ensure the continuous availability of prefill processing, thereby improving latency. Thus, EcoServe's basic serving unit is the macro instance, within which multiple instances collaborate. It further integrates an adaptive scheduling algorithm to route requests in a macro instance and a mitosis scaling approach to enable fine-grained capacity scaling. Beyond delivering high goodput, EcoServe excels in load balancing, hardware cost, parallelism compatibility, and even engineering simplicity compared to existing solutions. When serving 30B- and 70B-scale models on a production-level cluster with 32 NVIDIA L20 GPUs using commodity Ethernet, EcoServe averagely improves goodput by 82.49%, 86.17%, 122.76%, and 126.96% over four representative NoDG and FuDG systems.
Label-independent hyperparameter-free self-supervised single-view deep subspace clustering
Venue: arXiv cs.CV 2025
Year: 2025

Deep subspace clustering (DSC) algorithms face several challenges that hinder their widespread adoption across variois application domains. First, clustering quality is typically assessed using only the encoder's output layer, disregarding valuable information present in the intermediate layers. Second, most DSC approaches treat representation learning and subspace clustering as independent tasks, limiting their effectiveness. Third, they assume the availability of a held-out dataset for hyperparameter tuning, which is often impractical in real-world scenarios. Fourth, learning termination is commonly based on clustering error monitoring, requiring external labels. Finally, their performance often depends on post-processing techniques that rely on labeled data. To address this limitations, we introduce a novel single-view DSC approach that: (i) minimizes a layer-wise self expression loss using a joint representation matrix; (ii) optimizes a subspace-structured norm to enhance clustering quality; (iii) employs a multi-stage sequential learning framework, consisting of pre-training and fine-tuning, enabling the use of multiple regularization terms without hyperparameter tuning; (iv) incorporates a relative error-based self-stopping mechanism to terminate training without labels; and (v) retains a fixed number of leading coefficients in the learned representation matrix based on prior knowledge. We evaluate the proposed method on six datasets representing faces, digits, and objects. The results show that our method outperforms most linear SC algorithms with careffulyl tuned hyperparameters while maintaining competitive performance with the best performing linear appoaches.
Aligning Language Models for Icelandic Legal Text Summarization
Venue: arXiv cs.CL 2025
Year: 2025

The integration of language models in the legal domain holds considerable promise for streamlining processes and improving efficiency in managing extensive workloads. However, the specialized terminology, nuanced language, and formal style of legal texts can present substantial challenges. This study examines whether preference-based training techniques, specifically Reinforcement Learning from Human Feedback and Direct Preference Optimization, can enhance models' performance in generating Icelandic legal summaries that align with domain-specific language standards and user preferences. We compare models fine-tuned with preference training to those using conventional supervised learning. Results indicate that preference training improves the legal accuracy of generated summaries over standard fine-tuning but does not significantly enhance the overall quality of Icelandic language usage. Discrepancies between automated metrics and human evaluations further underscore the importance of qualitative assessment in developing language models for the legal domain.
Multi-Grained Compositional Visual Clue Learning for Image Intent Recognition
Venue: arXiv cs.CV 2025
Year: 2025

In an era where social media platforms abound, individuals frequently share images that offer insights into their intents and interests, impacting individual life quality and societal stability. Traditional computer vision tasks, such as object detection and semantic segmentation, focus on concrete visual representations, while intent recognition relies more on implicit visual clues. This poses challenges due to the wide variation and subjectivity of such clues, compounded by the problem of intra-class variety in conveying abstract concepts, e.g. "enjoy life". Existing methods seek to solve the problem by manually designing representative features or building prototypes for each class from global features. However, these methods still struggle to deal with the large visual diversity of each intent category. In this paper, we introduce a novel approach named Multi-grained Compositional visual Clue Learning (MCCL) to address these challenges for image intent recognition. Our method leverages the systematic compositionality of human cognition by breaking down intent recognition into visual clue composition and integrating multi-grained features. We adopt class-specific prototypes to alleviate data imbalance. We treat intent recognition as a multi-label classification problem, using a graph convolutional network to infuse prior knowledge through label embedding correlations. Demonstrated by a state-of-the-art performance on the Intentonomy and MDID datasets, our approach advances the accuracy of existing methods while also possessing good interpretability. Our work provides an attempt for future explorations in understanding complex and miscellaneous forms of human expression.
Automated Work Records for Precision Agriculture Management: A Low-Cost GNSS IoT Solution for Paddy Fields in Central Japan
Venue: arXiv cs.CY 2025
Year: 2025

Agricultural field operations are generally tracked as work records (WR), incorporating data points such as; work type, machine type, timestamped trajectories and field information. WR data which is automatically recorded by modern machinery equipped with Information and Communication Technologies (ICT) can enable efficient farm management decision making. Globally, farmers often rely on aged or legacy farming machinery and manual data recording, which introduces significant labor costs and increases the risk of inaccurate data input. To address this challenge, a field study in Central Japan was conducted to showcase automated data collection by retrofitting legacy farming machinery with low-cost Internet of Things (IoT) devices. For single-purpose vehicles (SPV), which only carry out single work types such as planting, LTE (Long Term Evolution) and Global Navigation Satellite System (GNSS) units were installed to record trajectory data. For multi-purpose vehicles (MPV), such as tractors which perform multiple work types, the configuration settings of these vehicles had to include implements and attachments data. To obtain this data, industry standard LTE-GNSS Bluetooth gateways were fitted onto MPV and low-cost BLE (Bluetooth Low Energy) beacons were attached to implements. After installation, over a seven-month field preparation and planting period 1,623 WR, including 421 WR for SPV and 1,120 WR for MVP, were automatically obtained. For MPV, the WR included detailed configuration settings enabling detection of the specific work types. These findings demonstrate the potential of low cost IoT GNSS devices for precision agriculture strategies to support management decisions in farming operations.
Two Means to an End Goal: Connecting Explainability and Contestability in the Regulation of Public Sector AI
Venue: arXiv cs.CY 2025
Year: 2025

Explainability and its emerging counterpart contestability have become important normative and design principles for trustworthy AI as they enable users and subjects to understand and challenge AI decisions. However, realizing these principles is difficult, as they assume different meanings in technical, legal, and organizational dimensions of AI regulation. To resolve this conceptual polysemy, in this paper, we present the findings of an interview study with 14 experts to examine the intersection and implementation of explainability and contestability, and their understanding in different research communities. We outline differentiations between descriptive and normative explainability, judicial and non-judicial channels of contestation, and individual and collective contestation action. We further describe the main points of friction in the realization of both principles, including the alignment between top-down and bottom-up regulation, the assignment of responsibility, and the need for interdisciplinary collaboration. Lastly, we formulate three recommendations for AI policy to implement both principles through a Regulation by Design perspective. We believe our contributions can inform policy-making and regulation of these core principles and enable more effective and equitable design, development, and deployment of trustworthy public AI systems.
Efficient Matching of Some Fundamental Regular Expressions with Backreferences
Venue: arXiv cs.FL 2025
Year: 2025

Regular expression matching is of practical importance due to its widespread use in real-world applications. In practical use, regular expressions are often used with real-world extensions. Accordingly, the matching problem of regular expressions with real-world extensions has been actively studied in recent years, yielding steady progress. However, backreference, a popular extension supported by most modern programming languages such as Java, Python, JavaScript and others in their standard libraries for string processing, is an exception to this positive trend. In fact, it is known that the matching problem of regular expressions with backreferences (rewbs) is theoretically hard and the existence of an asymptotically fast matching algorithm for arbitrary rewbs seems unlikely. Even among currently known partial solutions, the balance between efficiency and generality remains unsatisfactory. To bridge this gap, we present an efficient matching algorithm for rewbs of the form $e_0 (e)_1 e_1 \backslash 1 e_2$ where $e_0, e, e_1, e_2$ are pure regular expressions, which are fundamental and frequently used in practical applications. It runs in quadratic time with respect to the input string length, substantially improving the best-known cubic time complexity for these rewbs. Our algorithm combines ideas from both stringology and automata theory in a novel way. We leverage two techniques from automata theory, injection and summarization, to simultaneously examine matches whose backreferenced substrings are either a fixed right-maximal repeat or its extendable prefixes, which are concepts from stringology. By further utilizing a subtle property of extendable prefixes, our algorithm correctly decides the matching problem while achieving the quadratic-time complexity.
MAGI: Multi-Agent Guided Interview for Psychiatric Assessment
Venue: arXiv cs.CL 2025
Year: 2025

Automating structured clinical interviews could revolutionize mental healthcare accessibility, yet existing large language models (LLMs) approaches fail to align with psychiatric diagnostic protocols. We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational workflows through coordinated multi-agent collaboration. MAGI dynamically navigates clinical logic via four specialized agents: 1) an interview tree guided navigation agent adhering to the MINI's branching structure, 2) an adaptive question agent blending diagnostic probing, explaining, and empathy, 3) a judgment agent validating whether the response from participants meet the node, and 4) a diagnosis Agent generating Psychometric Chain-of- Thought (PsyCoT) traces that explicitly map symptoms to clinical criteria. Experimental results on 1,002 real-world participants covering depression, generalized anxiety, social anxiety and suicide shows that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.
A comprehensive review of classifier probability calibration metrics
Venue: arXiv cs.LG 2025
Year: 2025

Probabilities or confidence values produced by artificial intelligence (AI) and machine learning (ML) models often do not reflect their true accuracy, with some models being under or over confident in their predictions. For example, if a model is 80% sure of an outcome, is it correct 80% of the time? Probability calibration metrics measure the discrepancy between confidence and accuracy, providing an independent assessment of model calibration performance that complements traditional accuracy metrics. Understanding calibration is important when the outputs of multiple systems are combined, for assurance in safety or business-critical contexts, and for building user trust in models. This paper provides a comprehensive review of probability calibration metrics for classifier and object detection models, organising them according to a number of different categorisations to highlight their relationships. We identify 82 major metrics, which can be grouped into four classifier families (point-based, bin-based, kernel or curve-based, and cumulative) and an object detection family. For each metric, we provide equations where available, facilitating implementation and comparison by future researchers.
Deep Reinforcement Learning Based Navigation with Macro Actions and Topological Maps
Venue: arXiv cs.LG 2025
Year: 2025

This paper addresses the challenge of navigation in large, visually complex environments with sparse rewards. We propose a method that uses object-oriented macro actions grounded in a topological map, allowing a simple Deep Q-Network (DQN) to learn effective navigation policies. The agent builds a map by detecting objects from RGBD input and selecting discrete macro actions that correspond to navigating to these objects. This abstraction drastically reduces the complexity of the underlying reinforcement learning problem and enables generalization to unseen environments. We evaluate our approach in a photorealistic 3D simulation and show that it significantly outperforms a random baseline under both immediate and terminal reward conditions. Our results demonstrate that topological structure and macro-level abstraction can enable sample-efficient learning even from pixel data.