OpenRaiser/Intern-Atlas huggingface dataset:
PriFFT: Privacy-preserving Federated Fine-tuning of Large Language Models via Hybrid Secret Sharing
Venue: arXiv cs.CR 2025
Year: 2025

Fine-tuning large language models (LLMs) raises privacy concerns due to the risk of exposing sensitive training data. Federated learning (FL) mitigates this risk by keeping training samples on local devices, while facing the following problems in privacy-preserving federated fine-tuning. (i) Recent studies show that adversaries can still infer private information in FL. (ii) LLM parameters are shared publicly during federated fine-tuning, while developers are often reluctant to disclose these parameters, posing further security challenges. (iii) Existing works focus on secure inference of LLMs but do not consider privacy-preserving fine-tuning. Inspired by the above problems, we propose PriFFT, a privacy-preserving federated fine-tuning mechanism, to protect both the model parameters and users' privacy. Due to considerable LLM parameters, we present hybrid secret sharing combining arithmetic secret sharing (ASS) and function secret sharing (FSS) to build secure operations and implement secure layers and activation for privacy-preserving fine-tuning. To improve the efficiency of privacy-preserving federated fine-tuning of LLMs, we optimize several secure computation protocols based on FSS, including reciprocal calculation, tensor products, natural exponentiation, softmax, sigmoid, hyperbolic tangent, and dropout. The hybrid secret sharing enables PriFFT to apply our optimized FSS protocols while combining ASS protocols to support complex computation without extra communication. The optimized protocols reduce execution time up to 62.5% and communication overhead up to 70.7% compared to existing protocols. Besides, PriFFT reduces execution time and communication overhead in privacy-preserving fine-tuning up to 59.1%$ and 77.0%$ without accuracy drop compared to the existing secret sharing methods.
A Systematic Survey on Debugging Techniques for Machine Learning Systems
Venue: arXiv cs.SE 2025
Year: 2025

Debugging ML software (i.e., the detection, localization and fixing of faults) poses unique challenges compared to traditional software largely due to the probabilistic nature and heterogeneity of its development process. Various methods have been proposed for testing, diagnosing, and repairing ML systems. However, the big picture informing important research directions that really address the dire needs of developers is yet to unfold, leaving several key questions unaddressed: (1) What faults have been targeted in the ML debugging research that fulfill developers needs in practice? (2) How are these faults addressed? (3) What are the challenges in addressing the yet untargeted faults? In this paper, we conduct a systematic study of debugging techniques for machine learning systems. We first collect technical papers focusing on debugging components in machine learning software. We then map these papers to a taxonomy of faults to assess the current state of fault resolution identified in existing literature. Subsequently, we analyze which techniques are used to address specific faults based on the collected papers. This results in a comprehensive taxonomy that aligns faults with their corresponding debugging methods. Finally, we examine previously released transcripts of interviewing developers to identify the challenges in resolving unfixed faults. Our analysis reveals that only 48 percent of the identified ML debugging challenges have been explicitly addressed by researchers, while 46.9 percent remain unresolved or unmentioned. In real world applications, we found that 52.6 percent of issues reported on GitHub and 70.3% of problems discussed in interviews are still unaddressed by research in ML debugging. The study identifies 13 primary challenges in ML debugging, highlighting a significant gap between the identification of ML debugging issues and their resolution in practice.
PolyVer: A Compositional Approach for Polyglot System Modeling and Verification
Venue: arXiv cs.PL 2025
Year: 2025

Several software systems are polyglot; that is, they comprise programs implemented in a combination of programming languages. Verifiers that directly run on mainstream programming languages are currently customized for single languages. Thus, to verify polyglot systems, one usually translates them into a common verification language or formalism on which the verifier runs. In this paper, we present an alternative approach, PolyVer, which employs abstraction, compositional reasoning, and synthesis to directly perform polyglot verification. PolyVer constructs a formal model of the original polyglot system as a transition system where the update functions associated with transitions are implemented in target languages such as C or Rust. To perform verification, PolyVer then connects a model checker for transition systems with language-specific verifiers (e.g., for C or Rust) using pre/post-condition contracts for the update functions. These contracts are automatically generated by synthesis oracles based on syntax-guided synthesis or large language models (LLMs), and checked by the language-specific verifiers. The contracts form abstractions of the update functions using which the model checker verifies the overall system-level property on the polyglot system model. PolyVer iterates between counterexample-guided abstraction-refinement (CEGAR) and counterexample-guided inductive synthesis (CEGIS) until the property is verified or a true system-level counterexample is found. We demonstrate the utility of PolyVer for verifying programs in the Lingua Franca polyglot language using the UCLID5 model checker connected with the CBMC and Kani verifiers for C and Rust respectively.
LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law
Venue: arXiv cs.CL 2025
Year: 2025

Analyzing large volumes of case law to uncover evolving legal principles, across multiple cases, on a given topic is a demanding task for legal professionals. Structured topical reports provide an effective solution by summarizing key issues, principles, and judgments, enabling comprehensive legal analysis on a particular topic. While prior works have advanced query-based individual case summarization, none have extended to automatically generating multi-case structured reports. To address this, we introduce LexGenie, an automated LLM-based pipeline designed to create structured reports using the entire body of case law on user-specified topics within the European Court of Human Rights jurisdiction. LexGenie retrieves, clusters, and organizes relevant passages by topic to generate a structured outline and cohesive content for each section. Expert evaluation confirms LexGenie's utility in producing structured reports that enhance efficient, scalable legal analysis.
Conformal Transformations for Symmetric Power Transformers
Venue: arXiv cs.LG 2025
Year: 2025

Transformers with linear attention offer significant computational advantages over softmax-based transformers but often suffer from degraded performance. The symmetric power (sympow) transformer, a particular type of linear transformer, addresses some of this performance gap by leveraging symmetric tensor embeddings, achieving comparable performance to softmax transformers. However, the finite capacity of the recurrent state in sympow transformers limits their ability to retain information, leading to performance degradation when scaling the training or evaluation context length. To address this issue, we propose the conformal-sympow transformer, which dynamically frees up capacity using data-dependent multiplicative gating and adaptively stores information using data-dependent rotary embeddings. Preliminary experiments on the LongCrawl64 dataset demonstrate that conformal-sympow overcomes the limitations of sympow transformers, achieving robust performance across scaled training and evaluation contexts.
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions
Venue: arXiv cs.CV 2025
Year: 2025

Visual Language Models (VLMs) have demonstrated impressive capabilities in visual grounding tasks. However, their effectiveness in the medical domain, particularly for abnormality detection and localization within medical images, remains underexplored. A major challenge is the complex and abstract nature of medical terminology, which makes it difficult to directly associate pathological anomaly terms with their corresponding visual features. In this work, we introduce a novel approach to enhance VLM performance in medical abnormality detection and localization by leveraging decomposed medical knowledge. Instead of directly prompting models to recognize specific abnormalities, we focus on breaking down medical concepts into fundamental attributes and common visual patterns. This strategy promotes a stronger alignment between textual descriptions and visual features, improving both the recognition and localization of abnormalities in medical images.We evaluate our method on the 0.23B Florence-2 base model and demonstrate that it achieves comparable performance in abnormality grounding to significantly larger 7B LLaVA-based medical VLMs, despite being trained on only 1.5% of the data used for such models. Experimental results also demonstrate the effectiveness of our approach in both known and previously unseen abnormalities, suggesting its strong generalization capabilities.
Gaussian highpass guided image filtering
Venue: arXiv cs.CV 2025
Year: 2025

Guided image filtering (GIF) is a popular smoothing technique, in which an additional image is used as a structure guidance for noise removal with edge preservation. The original GIF and some of its subsequent improvements are derived from a two-parameter local affine model (LAM), where the filtering output is a local affine transformation of the guidance image, but the input image is not taken into account in the LAM formulation. In this paper, we first introduce a single-parameter Prior Model based on Gaussian (highpass/lowpass) Filtering (PM-GF), in which the filtering output is the sum of a weighted portion of Gaussian highpass filtering of the guidance image and Gaussian smoothing of the input image. In the PM-GF, the guidance structure determined by Gaussian highpass filtering is obviously transferred to the filtering output, thereby better revealing the structure transfer mechanism of guided filtering. Then we propose several Gaussian highpass GIFs (GH-GIFs) based on the PM-GF by emulating the original GIF and some improvements, i.e., using PM-GF instead of LAM in these GIFs. Experimental results illustrate that the proposed GIFs outperform their counterparts in several image processing applications.
Which books do I like?
Venue: arXiv cs.DL 2025
Year: 2025

Finding enjoyable fiction books can be challenging, partly because stories are multi-faceted and one's own literary taste might be difficult to ascertain. Here, we introduce the ISAAC method (Introspection-Support, AI-Annotation, and Curation), a pipeline which supports fiction readers in gaining awareness of their literary preferences and finding enjoyable books. ISAAC consists of four steps: a user supplies book ratings, an AI agent researches and annotates the provided books, patterns in book enjoyment are reviewed by the user, and the AI agent recommends new books. In this proof-of-concept self-study, the authors test whether ISAAC can highlight idiosyncratic patterns in their book enjoyment, spark a deeper reflection about their literary tastes, and make accurate, personalized recommendations of enjoyable books and underexplored literary niches. Results highlight substantial advantages of ISAAC over existing methods such as an integration of automation and intuition, accurate and customizable annotations, and explainable book recommendations. Observed disadvantages are that ISAAC's outputs can elicit false self-narratives (if statistical patterns are taken at face value), that books cannot be annotated if their online documentation is lacking, and that people who are new to reading have to rely on assumed book ratings or movie ratings to power the ISAAC pipeline. We discuss additional opportunities of ISAAC-style book annotations for the study of literary trends, and the scientific classification of books and readers.
Differential Machine Learning for Time Series Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Accurate time series prediction is challenging due to the inherent nonlinearity and sensitivity to initial conditions. We propose a novel approach that enhances neural network predictions through differential learning, which involves training models on both the original time series and its differential series. Specifically, we develop a differential long short-term memory (Diff-LSTM) network that uses a shared LSTM cell to simultaneously process both data streams, effectively capturing intrinsic patterns and temporal dynamics. Evaluated on the Mackey-Glass, Lorenz, and Rössler chaotic time series, as well as a real-world financial dataset from ACI Worldwide Inc., our results demonstrate that the Diff- LSTM network outperforms prevalent models such as recurrent neural networks, convolutional neural networks, and bidirectional and encoder-decoder LSTM networks in both short-term and long-term predictions. This framework offers a promising solution for enhancing time series prediction, even when comprehensive knowledge of the underlying dynamics of the time series is not fully available.
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Venue: arXiv cs.LG 2025
Year: 2025

Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios. They typically exhibit distinctive structure and domain-specific knowledge, motivating the development of a Graph Foundation Model (GFM) that generalizes across diverse graphs and tasks. Despite large efforts to integrate Large Language Models (LLMs) and Graph Neural Networks (GNNs) for TAGs, existing approaches suffer from decoupled architectures with two-stage alignment, limiting their synergistic potential. Even worse, existing methods assign out-of-vocabulary (OOV) tokens to graph nodes, leading to graph-specific semantics, token explosion, and incompatibility with task-oriented prompt templates, which hinders cross-graph and cross-task transferability. To address these challenges, we propose PromptGFM, a versatile GFM for TAGs grounded in graph vocabulary learning. PromptGFM comprises two key components: (1) Graph Understanding Module, which explicitly prompts LLMs to replicate the finest GNN workflow within the text space, facilitating seamless GNN-LLM integration and elegant graph-text alignment; (2) Graph Inference Module, which establishes a language-based graph vocabulary ensuring expressiveness, transferability, and scalability, enabling readable instructions for LLM fine-tuning. Extensive experiments demonstrate our superiority and transferability across diverse graphs and tasks. The code is available at this: https://github.com/agiresearch/PromptGFM.
IoT Integration Protocol for Enhanced Hospital Care
Venue: arXiv cs.CY 2025
Year: 2025

This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care", a comprehensive framework designed to leverage Internet of Things (IoT) technology to enhance patient care, improve operational efficiency, and ensure data security in hospital settings. With the growing emphasis on utilizing advanced technologies in healthcare, this protocol aims to harness the potential of IoT devices to optimize patient monitoring, enable remote care, and support clinical decision-making. By integrating IoT seamlessly into nursing workflows and patient care plans, hospitals can achieve higher levels of patient-centric care and real-time data insights, leading to better treatment outcomes and resource allocation. This paper outlines the protocol's objectives, key components, and expected benefits, while emphasizing the importance of ethical considerations and ongoing evaluation to ensure successful implementation.
A unified approach for degree bound estimates of linear differential operators
Venue: arXiv cs.SC 2025
Year: 2025

We identify a common scheme in several existing algorithms addressing computational problems on linear differential equations with polynomial coefficients. These algorithms reduce to computing a linear relation between vectors obtained as iterates of a simple differential operator known as pseudo-linear map. We focus on establishing precise degree bounds on the output of this class of algorithms. It turns out that in all known instances (least common left multiple, symmetric product,. . . ), the bounds that are derived from the linear algebra step using Cramer's rule are pessimistic. The gap with the behaviour observed in practice is often of one order of magnitude, and better bounds are sometimes known and derived from ad hoc methods and independent arguments. We propose a unified approach for proving output degree bounds for all instances of the class at once. The main technical tools come from the theory of realisations of matrices of rational functions and their determinantal denominators.
Delay Analysis of Random Network Coding Enabled ad-hoc M-to-N broadcast network
Venue: arXiv cs.NI 2025
Year: 2025

In this paper, we analyze the delay performance of an ad-hoc dynamic network where random network coding and broadcast are used in combination to distribute the messages. The analysis is comprehensive for that we consider M-to-N broadcast instead of 1-to-N, which allows both different messages and same messages to be transmitted by several sources at the same time. Although the routes between source-destination pairs are subject to change when some nodes have large backlogs, we derive fixed equivalent routes to provide a upper bound of delay. For some special cases, an detour method is also provided to increase the estimation accuracy. Different network topologies are tested in numeric simulation. The results demonstrate the accuracy of our delay performance approximation.
Iterating Pointers: Enabling Static Analysis for Loop-based Pointers
Venue: arXiv cs.PL 2025
Year: 2025

Pointers are an integral part of C and other programming languages. They enable substantial flexibility from the programmer's standpoint, allowing the user fine, unmediated control over data access patterns. However, accesses done through pointers are often hard to track, and challenging to understand for optimizers, compilers, and sometimes, even for the developers themselves because of the direct memory access they provide. We alleviate this problem by exposing additional information to analyzers and compilers. By separating the concept of a pointer into a data container and an offset, we can optimize C programs beyond what other state-of-the-art approaches are capable of, in some cases even enabling auto-parallelization. Using this process, we are able to successfully analyze and optimize code from OpenSSL, the Mantevo benchmark suite, and the Lempel-Ziv-Oberhumer compression algorithm. We provide the only automatic approach able to find all parallelization opportunities in the HPCCG benchmark from the Mantevo suite the developers identified and even outperform the reference implementation by up to 18%, as well as speed up the PBKDF2 algorithm implementation from OpenSSL by up to 11x.
Paths and Ambient Spaces in Neural Loss Landscapes
Venue: arXiv cs.LG 2025
Year: 2025

Understanding the structure of neural network loss surfaces, particularly the emergence of low-loss tunnels, is critical for advancing neural network theory and practice. In this paper, we propose a novel approach to directly embed loss tunnels into the loss landscape of neural networks. Exploring the properties of these loss tunnels offers new insights into their length and structure and sheds light on some common misconceptions. We then apply our approach to Bayesian neural networks, where we improve subspace inference by identifying pitfalls and proposing a more natural prior that better guides the sampling procedure.
Exploring Visual Prompts: Refining Images with Scribbles and Annotations in Generative AI Image Tools
Venue: arXiv cs.HC 2025
Year: 2025

Generative AI (GenAI) tools are increasingly integrated into design workflows. While text prompts remain the primary input method for GenAI image tools, designers often struggle to craft effective ones. Moreover, research has primarily focused on input methods for ideation, with limited attention to refinement tasks. This study explores designers' preferences for three input methods - text prompts, annotations, and scribbles - through a preliminary digital paper-based study with seven professional designers. Designers preferred annotations for spatial adjustments and referencing in-image elements, while scribbles were favored for specifying attributes such as shape, size, and position, often combined with other methods. Text prompts excelled at providing detailed descriptions or when designers sought greater GenAI creativity. However, designers expressed concerns about AI misinterpreting annotations and scribbles and the effort needed to create effective text prompts. These insights inform GenAI interface design to better support refinement tasks, align with workflows, and enhance communication with AI systems.
When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits
Venue: arXiv cs.CL 2025
Year: 2025

Online misinformation remains a critical challenge, and fact-checkers increasingly rely on claim matching systems that use sentence embedding models to retrieve relevant fact-checks. However, as users interact with claims online, they often introduce edits, and it remains unclear whether current embedding models used in retrieval are robust to such edits. To investigate this, we introduce a perturbation framework that generates valid and natural claim variations, enabling us to assess the robustness of a wide-range of sentence embedding models in a multi-stage retrieval pipeline and evaluate the effectiveness of various mitigation approaches. Our evaluation reveals that standard embedding models exhibit notable performance drops on edited claims, while LLM-distilled embedding models offer improved robustness at a higher computational cost. Although a strong reranker helps to reduce the performance drop, it cannot fully compensate for first-stage retrieval gaps. To address these retrieval gaps, we evaluate train- and inference-time mitigation approaches, demonstrating that they can improve in-domain robustness by up to 17 percentage points and boost out-of-domain generalization by 10 percentage points. Overall, our findings provide practical improvements to claim-matching systems, enabling more reliable fact-checking of evolving misinformation. Code and data are available at https://github.com/JabezNzomo99/claim-matching-robustness.