OpenRaiser/Intern-Atlas huggingface dataset:
A Characterization of Basic Feasible Functionals Through Higher-Order Rewriting and Tuple Interpretations
Venue: arXiv cs.LO 2025
Year: 2025

The class of type-two basic feasible functionals ($\mathtt{BFF}_2$) is the analogue of $\mathtt{FP}$ (polynomial time functions) for type-2 functionals, that is, functionals that can take (first-order) functions as arguments. $\mathtt{BFF}_2$ can be defined through Oracle Turing machines with running time bounded by second-order polynomials. On the other hand, higher-order term rewriting provides an elegant formalism for expressing higher-order computation. We address the problem of characterizing $\mathtt{BFF}_2$ by higher-order term rewriting. Various kinds of interpretations for first-order term rewriting have been introduced in the literature for proving termination and characterizing first-order complexity classes. In this paper, we consider a recently introduced notion of cost-size interpretations for higher-order term rewriting and see second order rewriting as ways of computing type-2 functionals. We then prove that the class of functionals represented by higher-order terms admitting polynomially bounded cost-size interpretations exactly corresponds to $\mathtt{BFF}_2$.
Computation of classical and $v$-adic $L$-series of $t$-motives
Venue: arXiv cs.SC 2025
Year: 2025

We design an algorithm for computing the $L$-series associated to an Anderson $t$-motives, exhibiting quasilinear complexity with respect to the target precision. Based on experiments, we conjecture that the order of vanishing at $T=1$ of the $v$-adic $L$-series of a given Anderson $t$-motive with good reduction does not depend on the finite place $v$.
Decoding University Hierarchy and Prestige in China through Domestic Ph.D. Hiring Network
Venue: arXiv cs.DL 2025
Year: 2025

The academic job market for fresh Ph.D. students to pursue postdoctoral and junior faculty positions plays a crucial role in shaping the future orientations, developments, and status of the global academic system. In this work, we focus on the domestic Ph.D. hiring network among universities in China by exploring the doctoral education and academic employment of nearly 28,000 scientists across all Ph.D.-granting Chinese universities over three decades. We employ the minimum violation rankings algorithm to decode the rankings for universities based on the Ph.D. hiring network, which offers a deep understanding of the structure and dynamics within the network. Our results uncover a consistent, highly structured hierarchy within this hiring network, indicating the imbalances wherein a limited number of universities serve as the main sources of fresh Ph.D. across diverse disciplines. Furthermore, over time, it has become increasingly challenging for Chinese Ph.D. graduates to secure positions at institutions more prestigious than their alma maters. This study quantitatively captures the evolving structure of talent circulation in the domestic environment, providing valuable insights to enhance the organization, diversity, and talent distribution in China's academic enterprise.
Few-Shot Learning for Mental Disorder Detection: A Continuous Multi-Prompt Engineering Approach with Medical Knowledge Injection
Venue: arXiv cs.CL 2025
Year: 2025

This study harnesses state-of-the-art AI technology for detecting mental disorders through user-generated textual content. Existing studies typically rely on fully supervised machine learning, which presents challenges such as the labor-intensive manual process of annotating extensive training data for each research problem and the need to design specialized deep learning architectures for each task. We propose a novel method to address these challenges by leveraging large language models and continuous multi-prompt engineering, which offers two key advantages: (1) developing personalized prompts that capture each user's unique characteristics and (2) integrating structured medical knowledge into prompts to provide context for disease detection and facilitate predictive modeling. We evaluate our method using three widely prevalent mental disorders as research cases. Our method significantly outperforms existing methods, including feature engineering, architecture engineering, and discrete prompt engineering. Meanwhile, our approach demonstrates success in few-shot learning, i.e., requiring only a minimal number of training examples. Moreover, our method can be generalized to other rare mental disorder detection tasks with few positive labels. In addition to its technical contributions, our method has the potential to enhance the well-being of individuals with mental disorders and offer a cost-effective, accessible alternative for stakeholders beyond traditional mental disorder screening methods.
Sensing environmental physical interaction to traverse cluttered obstacles
Venue: arXiv cs.RO 2025
Year: 2025

The long-standing, dominant approach to robotic obstacle negotiation relies on mapping environmental geometry to avoid obstacles. However, this approach does not allow for traversal of cluttered obstacles, hindering applications such as search and rescue operations through earthquake rubble and exploration across lunar and Martian rocks. To overcome this challenge, robots must further sense and utilize environmental physical interactions to control themselves to traverse obstacles. Recently, a physics-based approach has been established towards this vision. Self-propelled robots interacting with obstacles results in a potential energy landscape. On this landscape, to traverse obstacles, a robot must escape from certain landscape basins that attract it into failure modes, to reach other basins that lead to successful modes. Thus, sensing the potential energy landscape is crucial. Here, we developed new methods and performed systematic experiments to demonstrate that the potential energy landscape can be estimated by sensing environmental physical interaction. We developed a minimalistic robot capable of sensing obstacle contact forces and torques for systematic experiments over a wide range of parameter space. Surprisingly, although these forces and torques are not fully conservative, they match the potential energy landscape gradients that are conservative forces and torques, enabling an accurate estimation of the potential energy landscape. Additionally, a bio-inspired strategy further enhanced estimation accuracy. Our results provided a foundation for further refining these methods for use in free-locomoting robots. Our study is a key step in establishing a new physics-based approach for robots to traverse clustered obstacles to advance their mobility in complex, real-world environments.
Open-Source, Cost-Aware Kinematically Feasible Planning for Mobile and Surface Robotics
Venue: arXiv cs.RO 2025
Year: 2025

We present Smac Planner, an openly available, search-based planning framework that addresses the critical need for kinematically feasible path planning across diverse robot platforms. Smac Planner provides high-performance implementations of Cost-Aware A*, Hybrid-A*, and State Lattice planners that can be deployed for Ackermann, legged, and other large non-circular robots. Our framework introduces novel "Cost-Aware" variations that significantly improve performance in complex environments common to mobile robotics while maintaining kinematic feasibility constraints. Integrated as the standard planning system within the popular ROS 2 Navigation stack, Nav2, Smac Planner now powers thousands of robots worldwide across academic research, commercial applications, and field deployments.
Constructing a fully homomorphic encryption scheme with the Yoneda Lemma
Venue: arXiv cs.CR 2025
Year: 2025

This paper redefines the foundations of asymmetric cryptography's homomorphic cryptosystems through the application of the Yoneda Lemma. It demonstrates that widely adopted systems, including ElGamal, RSA, Benaloh, Regev's LWE, and NTRUEncrypt, are directly derived from the principles of the Yoneda Lemma. This synthesis leads to the creation of a holistic homomorphic encryption framework, the Yoneda Encryption Scheme. Within this framework, encryption is modeled using the bijective maps of the Yoneda Lemma Isomorphism, with decryption following naturally from the properties of these maps. This unification suggests a conjecture for a unified model theory framework, offering a foundation for reasoning about both homomorphic and fully homomorphic encryption (FHE) schemes. As a practical demonstration, the paper introduces the FHE scheme ACES, which supports arbitrary finite sequences of encrypted multiplications and additions without relying on conventional bootstrapping techniques for ciphertext refreshment. This highlights the practical implications of the theoretical advancements and proposes a new approach for leveraging model theory and forcing techniques in cryptography, particularly in the design of FHE schemes.
On the Approximate Core and Nucleon of Flow Games with Public Arcs
Venue: arXiv cs.GT 2025
Year: 2025

We investigate flow games featuring both private arcs owned by individual players and public arcs accessible cost-free to all coalitions. We explore two solution concepts within this framework: the approximate core and the nucleon. The approximate core relaxes core requirements by permitting a bounded relative payoff deviation for every coalition, and the nucleon is a multiplicative analogue of Schmeidler's nucleolus which lexicographically maximizes the vector consisting of relative payoff deviations for every coalition arranged in a non-decreasing order. By leveraging a decomposition property for paths and cycles in a flow network, we derive complete characterizations for the approximate core and demonstrate that the nucleon can be computed in polynomial time.
Winding Clearness for Differentiable Point Cloud Optimization
Venue: arXiv cs.GR 2025
Year: 2025

We propose to explore the properties of raw point clouds through the \emph{winding clearness}, a concept we first introduce for measuring the clarity of the interior/exterior relationships represented by the winding number field of the point cloud. In geometric modeling, the winding number is a powerful tool for distinguishing the interior and exterior of a given surface $\partial Ω$, and it has been previously used for point normal orientation and surface reconstruction. In this work, we introduce a novel approach to evaluate and optimize the quality of point clouds based on the winding clearness. We observe that point clouds with less noise generally exhibit better winding clearness. Accordingly, we propose an objective function that quantifies the error in winding clearness, solely utilizing the coordinates of the point clouds. Moreover, we demonstrate that the winding clearness error is differentiable and can serve as a loss function in point cloud processing. We present this observation from two aspects: 1) We update the coordinates of the points by back-propagating the loss function for individual point clouds, resulting in an overall improvement without involving a neural network. 2) We incorporate winding clearness as a geometric constraint in the diffusion-based 3D generative model and update the network parameters to generate point clouds with less noise. Experimental results demonstrate the effectiveness of optimizing the winding clearness in enhancing the point cloud quality. Notably, our method exhibits superior performance in handling noisy point clouds with thin structures, highlighting the benefits of the global perspective enabled by the winding number.
Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning
Venue: arXiv cs.LG 2025
Year: 2025

Machine Learning (ML) has revolutionized various domains, offering predictive capabilities in several areas. However, with the increasing accessibility of ML tools, many practitioners, lacking deep ML expertise, adopt a "push the button" approach, utilizing user-friendly interfaces without a thorough understanding of underlying algorithms. While this approach provides convenience, it raises concerns about the reliability of outcomes, leading to challenges such as incorrect performance evaluation. This paper addresses a critical issue in ML, known as data leakage, where unintended information contaminates the training data, impacting model performance evaluation. Users, due to a lack of understanding, may inadvertently overlook crucial steps, leading to optimistic performance estimates that may not hold in real-world scenarios. The discrepancy between evaluated and actual performance on new data is a significant concern. In particular, this paper categorizes data leakage in ML, discussing how certain conditions can propagate through the ML workflow. Furthermore, it explores the connection between data leakage and the specific task being addressed, investigates its occurrence in Transfer Learning, and compares standard inductive ML with transductive ML frameworks. The conclusion summarizes key findings, emphasizing the importance of addressing data leakage for robust and reliable ML applications.
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality
Venue: arXiv cs.LG 2025
Year: 2025

Multimodal federated learning (MFL) has emerged as a decentralized machine learning paradigm, allowing multiple clients with different modalities to collaborate on training a global model across diverse data sources without sharing their private data. However, challenges, such as data heterogeneity and severely missing modalities, pose crucial hindrances to the robustness of MFL, significantly impacting the performance of global model. The occurrence of missing modalities in real-world applications, such as autonomous driving, often arises from factors like sensor failures, leading knowledge gaps during the training process. Specifically, the absence of a modality introduces misalignment during the local training phase, stemming from zero-filling in the case of clients with missing modalities. Consequently, achieving robust generalization in global model becomes imperative, especially when dealing with clients that have incomplete data. In this paper, we propose $\textbf{Multimodal Federated Cross Prototype Learning (MFCPL)}$, a novel approach for MFL under severely missing modalities. Our MFCPL leverages the complete prototypes to provide diverse modality knowledge in modality-shared level with the cross-modal regularization and modality-specific level with cross-modal contrastive mechanism. Additionally, our approach introduces the cross-modal alignment to provide regularization for modality-specific features, thereby enhancing the overall performance, particularly in scenarios involving severely missing modalities. Through extensive experiments on three multimodal datasets, we demonstrate the effectiveness of MFCPL in mitigating the challenges of data heterogeneity and severely missing modalities while improving the overall performance and robustness of MFL.
Leveraging Foundation Models for Crafting Narrative Visualization: A Survey
Venue: arXiv cs.HC 2025
Year: 2025

Narrative visualization transforms data into engaging stories, making complex information accessible to a broad audience. Foundation models, with their advanced capabilities such as natural language processing, content generation, and multimodal integration, hold substantial potential for enriching narrative visualization. Recently, a collection of techniques have been introduced for crafting narrative visualizations based on foundation models from different aspects. We build our survey upon 66 papers to study how foundation models can progressively engage in this process and then propose a reference model categorizing the reviewed literature into four essential phases: Analysis, Narration, Visualization, and Interaction. Furthermore, we identify eight specific tasks (e.g. Insight Extraction and Authoring) where foundation models are applied across these stages to facilitate the creation of visual narratives. Detailed descriptions, related literature, and reflections are presented for each task. To make it a more impactful and informative experience for diverse readers, we discuss key research problems and provide the strengths and weaknesses in each task to guide people in identifying and seizing opportunities while navigating challenges in this field.
CreativeSynth: Cross-Art-Attention for Artistic Image Synthesis with Multimodal Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Although remarkable progress has been made in image style transfer, style is just one of the components of artistic paintings. Directly transferring extracted style features to natural images often results in outputs with obvious synthetic traces. This is because key painting attributes including layout, perspective, shape, and semantics often cannot be conveyed and expressed through style transfer. Large-scale pretrained text-to-image generation models have demonstrated their capability to synthesize a vast amount of high-quality images. However, even with extensive textual descriptions, it is challenging to fully express the unique visual properties and details of paintings. Moreover, generic models often disrupt the overall artistic effect when modifying specific areas, making it more complicated to achieve a unified aesthetic in artworks. Our main novel idea is to integrate multimodal semantic information as a synthesis guide into artworks, rather than transferring style to the real world. We also aim to reduce the disruption to the harmony of artworks while simplifying the guidance conditions. Specifically, we propose an innovative multi-task unified framework called CreativeSynth, based on the diffusion model with the ability to coordinate multimodal inputs. CreativeSynth combines multimodal features with customized attention mechanisms to seamlessly integrate real-world semantic content into the art domain through Cross-Art-Attention for aesthetic maintenance and semantic fusion. We demonstrate the results of our method across a wide range of different art categories, proving that CreativeSynth bridges the gap between generative models and artistic expression. Code and results are available at https://github.com/haha-lisa/CreativeSynth.
The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support
Venue: arXiv cs.HC 2025
Year: 2025

People experiencing severe distress increasingly use Large Language Model (LLM) chatbots as mental health support tools. Discussions on social media have described how engagements were lifesaving for some, but evidence suggests that general-purpose LLM chatbots also have notable risks that could endanger the welfare of users if not designed responsibly. In this study, we investigate the lived experiences of people who have used LLM chatbots for mental health support. We build on interviews with 21 individuals from globally diverse backgrounds to analyze how users create unique support roles for their chatbots, fill in gaps in everyday care, and navigate associated cultural limitations when seeking support from chatbots. We ground our analysis in psychotherapy literature around effective support, and introduce the concept of therapeutic alignment, or aligning AI with therapeutic values for mental health contexts. Our study offers recommendations for how designers can approach the ethical and effective use of LLM chatbots and other AI mental health support tools in mental health care.
ParaLog: Consistent Host-side Logging for Parallel Checkpoints
Venue: arXiv cs.DC 2025
Year: 2025

Output-intensive scientific applications are highly sensitive to low storage throughput. While existing scientific application stacks are optimized for traditional High-Performance Computing (HPC) environments with high remote storage and network bandwidth, these assumptions often fail in modern settings like cloud deployment. This is because the existing scientific application I/O stack fails to leverage the available resources. At the same time, scientific applications exhibit special synchronization and data output requirements that are difficult to satisfy using traditional approaches such as block-level or filesystem-level caching. We introduce ParaLog, a distributed host-side logging approach designed to accelerate scientific applications transparently. ParaLog emphasizes deployability, enabling support for unmodified message passing interface (MPI) applications and implementations while preserving crash consistency semantics. We evaluate ParaLog across traditional HPC, cloud HPC, local clusters, and hybrid environments, demonstrating its capability to reduce end-to-end execution time by 13-26% for popular scientific applications in cloud settings.
Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs
Venue: arXiv cs.CL 2025
Year: 2025

Attributed Question Answering (AQA) has attracted wide attention, but there are still several limitations in evaluating the attributions, including lacking fine-grained attribution categories, relying on manual annotations, and failing to compare attributions with only subtle differences. To bridge these gaps, we introduce Complex Attributed Question Answering (CAQA), a large-scale benchmark containing comprehensive attribution categories, automatically generated using Knowledge Graphs (KGs), and complex attribution scenarios. We have conducted extensive experiments to verify the effectiveness of CAQA, including the benchmarking of 25 automatic evaluators, their comparison with human evaluators, the testing of LLM evaluators fine-tuned by CAQA and so on. These experiments also lead to a series of important findings that can benefit the future research of AQA. All the codes and data are publicly accessible at https://github.com/HuuuNan/CAQA-Benchmark.
The Rise of Plurimorphisms: Algebraic Approach to Approximation
Venue: arXiv cs.CC 2025
Year: 2025

Following the success of the so-called algebraic approach to the study of decision constraint satisfaction problems (CSPs), exact optimization of valued CSPs, and most recently promise CSPs, we propose an algebraic framework for valued promise CSPs. To every valued promise CSP we associate an algebraic object, its so-called valued minion. Our main result shows that the existence of a homomorphism between the associated valued minions implies a polynomial-time reduction between the original CSPs. We also show that this general reduction theorem includes important inapproximability results, for instance, the inapproximability of almost solvable systems of linear equations beyond the random assignment threshold.
Robust Plan Evaluation based on Approximate Probabilistic Machine Learning
Venue: arXiv cs.DB 2025
Year: 2025

Query optimizers in RDBMSs search for execution plans expected to be optimal for given queries. They use parameter estimates, often inaccurate, and make assumptions that may not hold in practice. Consequently, they may select plans that are suboptimal at runtime if estimates and assumptions are not valid. Therefore, they do not sufficiently support robust query optimization. Using ML to improve data systems has shown promising results for query optimization. Inspired by this, we propose Robust Query Optimizer (Roq), a holistic framework based on a risk-aware learning approach. Roq includes a novel formalization of the notion of robustness in the context of query optimization and a principled approach for its quantification and measurement based on approximate probabilistic ML. It also includes novel strategies and algorithms for query plan evaluation and selection. Roq includes a novel learned cost model that is designed to predict the cost of query execution and the associated risks and performs query optimization accordingly. We demonstrate that Roq provides significant improvements in robust query optimization compared with the state-of-the-art.