OpenRaiser/Intern-Atlas huggingface dataset:
Computing change of level and isogenies between abelian varieties
Venue: arXiv cs.SC 2025
Year: 2025

Let $m,n,d > 1$ be integers such that $n=md$. In this paper, we present an efficient change of level algorithm that takes as input $(B, \mathscr{M}, Θ_\mathscr{M})$ a marked abelian variety of level $m$ over the base field $k$ of odd characteristic and returns $(B, \mathscr{M}^d, Θ_{\mathscr{M}^d})$ a marked abelian variety of level $n$ at the expense of $O(m^g d^{2g})$ operations in $k$. A similar algorithm allows to compute $d$-isogenies: from $(B, \mathscr{M}, Θ_\mathscr{M})$ a marked abelian variety of level $m$, $K\subset B[d]$ isotropic for the Weil pairing isomorphic to $(\mathbb{Z}/d\mathbb{Z})^g$ defined over $k$, the isogeny algorithm returns $(A, \mathscr{L}, Θ_\mathscr{L})$ of level $m$ such that $A=B/K$ with $O(m^g d^g)$ operations in $k$. Our algorithms extend previous known results in the case that $d \wedge m=1$ and $d$ odd. In this paper, we lift theses restrictions. We use the same general approach as in the literature in conjunction with the notion of symmetric compatible that we introduce, study and link to previous results of Mumford. For practical computation, most of the time $m$ is $2$ or $4$ so that our algorithms allows in particular to compute $2^e$-isogenies which are important for the theory of theta functions but also for computational applications such as isogeny based cryptography.
Seeking Specifications: The Case for Neuro-Symbolic Specification Synthesis
Venue: arXiv cs.SE 2025
Year: 2025

This work is concerned with the generation of formal specifications from code, using Large Language Models (LLMs) in combination with symbolic methods. Concretely, in our study, the programming language is C, the specification language is ACSL, and the LLM is Deepseek-R1. In this context, we address two research directions, namely the specification of intent vs. implementation on the one hand, and the combination of symbolic analyses with LLMs on the other hand. For the first, we investigate how the absence or presence of bugs in the code impacts the generated specifications, as well as whether and how a user can direct the LLM to specify intent or implementation, respectively. For the second, we investigate the impact of results from symbolic analyses on the specifications generated by the LLM. The LLM prompts are augmented with outputs from two formal methods tools in the Frama-C ecosystem, Pathcrawler and EVA. We demonstrate how the addition of symbolic analysis to the workflow impacts the quality of annotations.
LLM Enhancer: Merged Approach using Vector Embedding for Reducing Large Language Model Hallucinations with External Knowledge
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs), such as ChatGPT, have demonstrated the capability to generate human like, natural responses across a range of tasks, including task oriented dialogue and question answering. However, their application in real world, critical scenarios is often hindered by a tendency to produce inaccurate information and a limited ability to leverage external knowledge sources. This paper introduces the LLM ENHANCER system, designed to integrate multiple online sources such as Google, Wikipedia, and DuckDuckGo to enhance data accuracy. The LLMs employed within this system are open source. The data acquisition process for the LLM ENHANCER system operates in parallel, utilizing custom agent tools to manage the flow of information. Vector embeddings are used to identify the most pertinent information, which is subsequently supplied to the LLM for user interaction. The LLM ENHANCER system mitigates hallucinations in chat based LLMs while preserving response naturalness and accuracy.
Differentially Private Secure Multiplication with Erasures and Adversaries
Venue: arXiv cs.IT 2025
Year: 2025

We consider a private distributed multiplication problem involving N computation nodes and T colluding nodes. Shamir's secret sharing algorithm provides perfect information-theoretic privacy, while requiring an honest majority, i.e., N \ge 2T + 1. Recent work has investigated approximate computation and characterized privacy-accuracy trade-offs for the honest minority setting N \le 2T for real-valued data, quantifying privacy leakage via the differential privacy (DP) framework and accuracy via the mean squared error. However, it does not incorporate the error correction capabilities of Shamir's secret-sharing algorithm. This paper develops a new polynomial-based coding scheme for secure multiplication with an honest minority, and characterizes its achievable privacy-utility tradeoff, showing that the tradeoff can approach the converse bound as closely as desired. Unlike previous schemes, the proposed scheme inherits the capability of the Reed-Solomon (RS) code to tolerate erasures and adversaries. We utilize a modified Berlekamp-Welch algorithm over the real number field to detect adversarial nodes.
GLIP-OOD: Zero-Shot Graph OOD Detection with Graph Foundation Model
Venue: arXiv cs.LG 2025
Year: 2025

Out-of-distribution (OOD) detection is critical for ensuring the safety and reliability of machine learning systems, particularly in dynamic and open-world environments. In the vision and text domains, zero-shot OOD detection - which requires no training on in-distribution (ID) data - has advanced significantly through the use of large-scale pretrained models, such as vision-language models (VLMs) and large language models (LLMs). However, zero-shot OOD detection in graph-structured data remains largely unexplored, primarily due to the challenges posed by complex relational structures and the absence of powerful, large-scale pretrained models for graphs. In this work, we take the first step toward enabling zero-shot graph OOD detection by leveraging a graph foundation model (GFM). Our experiments show that, when provided only with class label names for both ID and OOD categories, the GFM can effectively perform OOD detection - often surpassing existing "supervised" OOD detection methods that rely on extensive labeled node data. We further address the practical scenario in which OOD label names are not available in real-world settings by introducing GLIP-OOD, a framework that uses LLMs to generate semantically informative pseudo-OOD labels from unlabeled data. These generated OOD labels allow the GFM to better separate ID and OOD classes, facilitating more precise OOD detection - all without any labeled nodes (only ID label names). To our knowledge, this is the first approach to achieve node-level graph OOD detection in a fully zero-shot setting, and it attains performance comparable to state-of-the-art supervised methods on four benchmark text-attributed graph datasets.
LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning
Venue: arXiv cs.LG 2025
Year: 2025

FPGAs are increasingly adopted in datacenter environments for their reconfigurability and energy efficiency. High-Level Synthesis (HLS) tools have eased FPGA programming by raising the abstraction level from RTL to untimed C/C++, yet attaining high performance still demands expert knowledge and iterative manual insertion of optimization pragmas to modify the microarchitecture. To address this challenge, we propose LIFT, a large language model (LLM)-based coding assistant for HLS that automatically generates performance-critical pragmas given a C/C++ design. We fine-tune the LLM by tightly integrating and supervising the training process with a graph neural network (GNN), combining the sequential modeling capabilities of LLMs with the structural and semantic understanding of GNNs necessary for reasoning over code and its control/data dependencies. On average, LIFT produces designs that improve performance by 3.52x and 2.16x than prior state-of the art AutoDSE and HARP respectively, and 66x than GPT-4o.
A Koopman Operator-based NMPC Framework for Mobile Robot Navigation under Uncertainty
Venue: arXiv cs.RO 2025
Year: 2025

Mobile robot navigation can be challenged by system uncertainty. For example, ground friction may vary abruptly causing slipping, and noisy sensor data can lead to inaccurate feedback control. Traditional model-based methods may be limited when considering such variations, making them fragile to varying types of uncertainty. One way to address this is by leveraging learned prediction models by means of the Koopman operator into nonlinear model predictive control (NMPC). This paper describes the formulation of, and provides the solution to, an NMPC problem using a lifted bilinear model that can accurately predict affine input systems with stochastic perturbations. System constraints are defined in the Koopman space, while the optimization problem is solved in the state space to reduce computational complexity. Training data to estimate the Koopman operator for the system are given via randomized control inputs. The output of the developed method enables closed-loop navigation control over environments populated with obstacles. The effectiveness of the proposed method has been tested through numerical simulations using a wheeled robot with additive stochastic velocity perturbations, Gazebo simulations with a realistic digital twin robot, and physical hardware experiments without knowledge of the true dynamics.
Realizing Quantum Wireless Sensing Without Extra Reference Sources: Architecture, Algorithm, and Sensitivity Maximization
Venue: arXiv cs.IT 2025
Year: 2025

Rydberg Atomic REceivers (RAREs) have demonstrated remarkable capabilities for radio-frequency signal measurement, enabling advanced quantum wireless sensing. Existing RARE-based sensing systems popularly adopt the heterodyne detection methodology, which requires an additional reference source to serve as an atomic mixer. However, this approach entails a bulky transceiver architecture and is limited in the supportable sensing bandwidth. To address these limitations, we propose a self-heterodyne sensing paradigm where the transmitter's self-interference naturally provides the reference signal. We demonstrate that a self-heterodyne RARE functions as an atomic autocorrelator, eliminating the need for external reference sources while supporting substantially wider bandwidth than conventional heterodyne methods. Next, a two-stage algorithm is devised to perform target ranging in self-heterodyne RARE systems. This algorithm is shown to closely approach the Cramer-Rao lower bound. Furthermore, we introduce the power-trajectory ($P$-trajectory) design for RAREs, which maximizes the sensing sensitivity through time-varying transmission power control. An internal noise (ITN)-limited $P$-trajectory is developed to capture the profile of the asymptotically optimal time-varying power in the presence of ITN only. This design is then extended to the practical $P$-trajectory by incorporating both the ITN and external noise. Numerical results validate that the proposed self-heterodyne sensing can achieve a $\sim$100 MHz-level bandwidth with high sensitivity, substantially surpassing existing heterodyne counterparts and paving the way for high-resolution quantum wireless sensing.
Countering underproduction of peer produced goods
Venue: arXiv cs.HC 2025
Year: 2025

Peer produced goods such as online knowledge bases and free/libre open source software rely on contributors who often choose their tasks regardless of consumer needs. These goods are susceptible to underproduction: when popular goods are relatively low quality. Although underproduction is a common feature of peer production, very little is known about how to counteract it. We use a detailed longitudinal dataset from English Wikipedia to show that more experienced contributors -- including those who contribute without an account -- tend to contribute to underproduced goods. A within-person analysis shows that contributors' efforts shift toward underproduced goods over time. These findings illustrate the value of retaining contributors in peer production, including those contributing without accounts, as a means to counter underproduction.
Subject Information Extraction for Novelty Detection with Domain Shifts
Venue: arXiv cs.CV 2025
Year: 2025

Unsupervised novelty detection (UND), aimed at identifying novel samples, is essential in fields like medical diagnosis, cybersecurity, and industrial quality control. Most existing UND methods assume that the training data and testing normal data originate from the same domain and only consider the distribution variation between training data and testing data. However, in real scenarios, it is common for normal testing and training data to originate from different domains, a challenge known as domain shift. The discrepancies between training and testing data often lead to incorrect classification of normal data as novel by existing methods. A typical situation is that testing normal data and training data describe the same subject, yet they differ in the background conditions. To address this problem, we introduce a novel method that separates subject information from background variation encapsulating the domain information to enhance detection performance under domain shifts. The proposed method minimizes the mutual information between the representations of the subject and background while modelling the background variation using a deep Gaussian mixture model, where the novelty detection is conducted on the subject representations solely and hence is not affected by the variation of domains. Extensive experiments demonstrate that our model generalizes effectively to unseen domains and significantly outperforms baseline methods, especially under substantial domain shifts between training and testing data.
Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA
Venue: arXiv cs.CL 2025
Year: 2025

Medical question answering (QA) is a reasoning-intensive task that remains challenging for large language models (LLMs) due to hallucinations and outdated domain knowledge. Retrieval-Augmented Generation (RAG) provides a promising post-training solution by leveraging external knowledge. However, existing medical RAG systems suffer from two key limitations: (1) a lack of modeling for human-like reasoning behaviors during information retrieval, and (2) reliance on suboptimal medical corpora, which often results in the retrieval of irrelevant or noisy snippets. To overcome these challenges, we propose Discuss-RAG, a plug-and-play module designed to enhance the medical QA RAG system through collaborative agent-based reasoning. Our method introduces a summarizer agent that orchestrates a team of medical experts to emulate multi-turn brainstorming, thereby improving the relevance of retrieved content. Additionally, a decision-making agent evaluates the retrieved snippets before their final integration. Experimental results on four benchmark medical QA datasets show that Discuss-RAG consistently outperforms MedRAG, especially significantly improving answer accuracy by up to 16.67% on BioASQ and 12.20% on PubMedQA. The code is available at: https://github.com/LLM-VLM-GSL/Discuss-RAG.
BiasGuard: A Reasoning-enhanced Bias Detection Tool For Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Identifying bias in LLM-generated content is a crucial prerequisite for ensuring fairness in LLMs. Existing methods, such as fairness classifiers and LLM-based judges, face limitations related to difficulties in understanding underlying intentions and the lack of criteria for fairness judgment. In this paper, we introduce BiasGuard, a novel bias detection tool that explicitly analyzes inputs and reasons through fairness specifications to provide accurate judgments. BiasGuard is implemented through a two-stage approach: the first stage initializes the model to explicitly reason based on fairness specifications, while the second stage leverages reinforcement learning to enhance its reasoning and judgment capabilities. Our experiments, conducted across five datasets, demonstrate that BiasGuard outperforms existing tools, improving accuracy and reducing over-fairness misjudgments. We also highlight the importance of reasoning-enhanced decision-making and provide evidence for the effectiveness of our two-stage optimization pipeline.
An Evaluation of a Visual Question Answering Strategy for Zero-shot Facial Expression Recognition in Still Images
Venue: arXiv cs.CV 2025
Year: 2025

Facial expression recognition (FER) is a key research area in computer vision and human-computer interaction. Despite recent advances in deep learning, challenges persist, especially in generalizing to new scenarios. In fact, zero-shot FER significantly reduces the performance of state-of-the-art FER models. To address this problem, the community has recently started to explore the integration of knowledge from Large Language Models for visual tasks. In this work, we evaluate a broad collection of locally executed Visual Language Models (VLMs), avoiding the lack of task-specific knowledge by adopting a Visual Question Answering strategy. We compare the proposed pipeline with state-of-the-art FER models, both integrating and excluding VLMs, evaluating well-known FER benchmarks: AffectNet, FERPlus, and RAF-DB. The results show excellent performance for some VLMs in zero-shot FER scenarios, indicating the need for further exploration to improve FER generalization.
A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees
Venue: arXiv cs.LG 2025
Year: 2025

Meta federated learning (FL) is a personalized variant of FL, where multiple agents collaborate on training an initial shared model without exchanging raw data samples. The initial model should be trained in a way that current or new agents can easily adapt it to their local datasets after one or a few fine-tuning steps, thus improving the model personalization. Conventional meta FL approaches minimize the average loss of agents on the local models obtained after one step of fine-tuning. In practice, agents may need to apply several fine-tuning steps to adapt the global model to their local data, especially under highly heterogeneous data distributions across agents. To this end, we present a generalized framework for the meta FL by minimizing the average loss of agents on their local model after any arbitrary number $ν$ of fine-tuning steps. For this generalized framework, we present a variant of the well-known federated averaging (FedAvg) algorithm and conduct a comprehensive theoretical convergence analysis to characterize the convergence speed as well as behavior of the meta loss functions in both the exact and approximated cases. Our experiments on real-world datasets demonstrate superior accuracy and faster convergence for the proposed scheme compared to conventional approaches.
Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Lung Nodule Malignancy Prediction
Venue: arXiv cs.CV 2025
Year: 2025

Machine learning models have utilized semantic features, deep features, or both to assess lung nodule malignancy. However, their reliance on manual annotation during inference, limited interpretability, and sensitivity to imaging variations hinder their application in real-world clinical settings. Thus, this research aims to integrate semantic features derived from radiologists' assessments of nodules, guiding the model to learn clinically relevant, robust, and explainable imaging features for predicting lung cancer. We obtained 938 low-dose CT scans from the National Lung Screening Trial (NLST) with 1,261 nodules and semantic features. Additionally, the Lung Image Database Consortium dataset contains 1,018 CT scans, with 2,625 lesions annotated for nodule characteristics. Three external datasets were obtained from UCLA Health, the LUNGx Challenge, and the Duke Lung Cancer Screening. We fine-tuned a pretrained Contrastive Language-Image Pretraining (CLIP) model with a parameter-efficient fine-tuning approach to align imaging and semantic text features and predict the one-year lung cancer diagnosis. Our model outperformed state-of-the-art (SOTA) models in the NLST test set with an AUROC of 0.901 and AUPRC of 0.776. It also showed robust results in external datasets. Using CLIP, we also obtained predictions on semantic features through zero-shot inference, such as nodule margin (AUROC: 0.807), nodule consistency (0.812), and pleural attachment (0.840). Our approach surpasses the SOTA models in predicting lung cancer across datasets collected from diverse clinical settings, providing explainable outputs, aiding clinicians in comprehending the underlying meaning of model predictions. This approach also prevents the model from learning shortcuts and generalizes across clinical settings. The code is available at https://github.com/luotingzhuang/CLIP_nodule.
Synergy-CLIP: Extending CLIP with Multi-modal Integration for Robust Representation Learning
Venue: arXiv cs.LG 2025
Year: 2025

Multi-modal representation learning has become a pivotal area in artificial intelligence, enabling the integration of diverse modalities such as vision, text, and audio to solve complex problems. However, existing approaches predominantly focus on bimodal interactions, such as image-text pairs, which limits their ability to fully exploit the richness of multi-modal data. Furthermore, the integration of modalities in equal-scale environments remains underexplored due to the challenges of constructing large-scale, balanced datasets. In this study, we propose Synergy-CLIP, a novel framework that extends the contrastive language-image pre-training (CLIP) architecture to enhance multi-modal representation learning by integrating visual, textual, and audio modalities. Unlike existing methods that focus on adapting individual modalities to vanilla-CLIP, Synergy-CLIP aligns and captures latent information across three modalities equally. To address the high cost of constructing large-scale multi-modal datasets, we introduce VGG-sound+, a triple-modal dataset designed to provide equal-scale representation of visual, textual, and audio data. Synergy-CLIP is validated on various downstream tasks, including zero-shot classification, where it outperforms existing baselines. Additionally, we introduce a missing modality reconstruction task, demonstrating Synergy-CLIP's ability to extract synergy among modalities in realistic application scenarios. These contributions provide a robust foundation for advancing multi-modal representation learning and exploring new research directions.