OpenRaiser/Intern-Atlas huggingface dataset:
Prior-free Collusion-proof Dynamic Mechanisms
Venue: arXiv cs.GT 2025
Year: 2025

For a general class of dynamic stochastic multi-player problems, Csóka, Liu, Rodivilov, and Teytelboym (2024) proposed prior-dependent mechanisms. The Guaranteed Utility Mechanism with transfers (TU-GUM) implements efficiency in a Guaranteed Utility Equilibrium (GUE). Its transfer-free variant (NTU-GUM) implements approximate efficiency in ε-GUE. In this paper, we define prior-free versions of both TU-GUM and NTU-GUM. As a special case, we believe that the new prior-free NTU-GUM implements a 1.283-approximation to Pareto efficiency for the repeated single good allocation problem in Fikioris, Banerjee, and Tardos (2024).
LinApart2: efficient parallel partial fraction decomposition algorithm for denominators with polynomials of general degree
Venue: arXiv cs.SC 2025
Year: 2025

We present LinApart2, a major update to the LinApart algorithm for univariate partial fraction decomposition. Unlike its predecessor, LinApart2 can handle denominators of arbitrary polynomial degree without explicit factorization, while retaining the efficiency and parallelizability of the Laurent series method. Benchmarks show substantial speedups in both runtime and memory usage compared to Mathematica's built-in routine Apart and to the Euclidean algorithm, enabling computations that were previously intractable.
AnonLFI 2.0: Extensible Architecture for PII Pseudonymization in CSIRTs with OCR and Technical Recognizers
Venue: arXiv cs.CR 2025
Year: 2025

This work presents AnonLFI 2.0, a modular pseudonymization framework for CSIRTs that uses HMAC SHA256 to generate strong and reversible pseudonyms, preserves XML and JSON structures, and integrates OCR and technical recognizers for PII and security artifacts. In two case studies involving OCR applied to PDF documents and an OpenVAS XML report, the system achieved perfect precision and F1 scores of 76.5 and 92.13, demonstrating its effectiveness for securely preparing complex cybersecurity datasets.
Rethinking Kernel Program Repair: Benchmarking and Enhancing LLMs with RGym
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) have revolutionized automated program repair (APR) but current benchmarks like SWE-Bench predominantly focus on userspace applications and overlook the complexities of kernel-space debugging and repair. The Linux kernel poses unique challenges due to its monolithic structure, concurrency, and low-level hardware interactions. Prior efforts such as KGym and CrashFixer have highlighted the difficulty of APR in this domain, reporting low success rates or relying on costly and complex pipelines and pricey cloud infrastructure. In this work, we introduce RGym, a lightweight, platform-agnostic APR evaluation framework for the Linux kernel designed to operate on local commodity hardware. Built on RGym, we propose a simple yet effective APR pipeline leveraging specialized localization techniques (e.g., call stacks and blamed commits) to overcome the unrealistic usage of oracles in KGym. We test on a filtered and verified dataset of 143 bugs. Our method achieves up to a 43.36% pass rate with GPT-5 Thinking while maintaining a cost of under $0.20 per bug. We further conduct an ablation study to analyze contributions from our proposed localization strategy, prompt structure, and model choice, and demonstrate that feedback-based retries can significantly enhance success rates.
Balancing Natural Language Processing Accuracy and Normalisation in Extracting Medical Insights
Venue: arXiv cs.AI 2025
Year: 2025

Extracting structured medical insights from unstructured clinical text using Natural Language Processing (NLP) remains an open challenge in healthcare, particularly in non-English contexts where resources are scarce. This study presents a comparative analysis of NLP low-compute rule-based methods and Large Language Models (LLMs) for information extraction from electronic health records (EHR) obtained from the Voivodeship Rehabilitation Hospital for Children in Ameryka, Poland. We evaluate both approaches by extracting patient demographics, clinical findings, and prescribed medications while examining the effects of lack of text normalisation and translation-induced information loss. Results demonstrate that rule-based methods provide higher accuracy in information retrieval tasks, particularly for age and sex extraction. However, LLMs offer greater adaptability and scalability, excelling in drug name recognition. The effectiveness of the LLMs was compared with texts originally in Polish and those translated into English, assessing the impact of translation. These findings highlight the trade-offs between accuracy, normalisation, and computational cost when deploying NLP in healthcare settings. We argue for hybrid approaches that combine the precision of rule-based systems with the adaptability of LLMs, offering a practical path toward more reliable and resource-efficient clinical NLP in real-world hospitals.
TopoReformer: Mitigating Adversarial Attacks Using Topological Purification in OCR Models
Venue: arXiv cs.LG 2025
Year: 2025

Adversarially perturbed images of text can cause sophisticated OCR systems to produce misleading or incorrect transcriptions from seemingly invisible changes to humans. Some of these perturbations even survive physical capture, posing security risks to high-stakes applications such as document processing, license plate recognition, and automated compliance systems. Existing defenses, such as adversarial training, input preprocessing, or post-recognition correction, are often model-specific, computationally expensive, and affect performance on unperturbed inputs while remaining vulnerable to unseen or adaptive attacks. To address these challenges, TopoReformer is introduced, a model-agnostic reformation pipeline that mitigates adversarial perturbations while preserving the structural integrity of text images. Topology studies properties of shapes and spaces that remain unchanged under continuous deformations, focusing on global structures such as connectivity, holes, and loops rather than exact distance. Leveraging these topological features, TopoReformer employs a topological autoencoder to enforce manifold-level consistency in latent space and improve robustness without explicit gradient regularization. The proposed method is benchmarked on EMNIST, MNIST, against standard adversarial attacks (FGSM, PGD, Carlini-Wagner), adaptive attacks (EOT, BDPA), and an OCR-specific watermark attack (FAWA).
The Evolving Ethics of Medical Data Stewardship
Venue: arXiv cs.CY 2025
Year: 2025

Healthcare stands at a critical crossroads. Artificial Intelligence and modern computing are unlocking opportunities, yet their value lies in the data that fuels them. The value of healthcare data is no longer limited to individual patients. However, data stewardship and governance has not kept pace, and privacy-centric policies are hindering both innovation and patient protections. As healthcare moves toward a data-driven future, we must define reformed data stewardship that prioritizes patients' interests by proactively managing modern risks and opportunities while addressing key challenges in cost, efficacy, and accessibility. Current healthcare data policies are rooted in 20th-century legislation shaped by outdated understandings of data-prioritizing perceived privacy over innovation and inclusion. While other industries thrive in a data-driven era, the evolution of medicine remains constrained by regulations that impose social rather than scientific boundaries. Large-scale aggregation is happening, but within opaque, closed systems. As we continue to uphold foundational ethical principles - autonomy, beneficence, nonmaleficence, and justice - there is a growing imperative to acknowledge they exist in evolving technological, social, and cultural realities. Ethical principles should facilitate, rather than obstruct, dialogue on adapting to meet opportunities and address constraints in medical practice and healthcare delivery. The new ethics of data stewardship places patients first by defining governance that adapts to changing landscapes. It also rejects the legacy of treating perceived privacy as an unquestionable, guiding principle. By proactively redefining data stewardship norms, we can drive an era of medicine that promotes innovation, protects patients, and advances equity - ensuring future generations advance medical discovery and care.
Attention-Based Feature Online Conformal Prediction for Time Series
Venue: arXiv cs.LG 2025
Year: 2025

Online conformal prediction (OCP) wraps around any pre-trained predictor to produce prediction sets with coverage guarantees that hold irrespective of temporal dependencies or distribution shifts. However, standard OCP faces two key limitations: it operates in the output space using simple nonconformity (NC) scores, and it treats all historical observations uniformly when estimating quantiles. This paper introduces attention-based feature OCP (AFOCP), which addresses both limitations through two key innovations. First, AFOCP operates in the feature space of pre-trained neural networks, leveraging learned representations to construct more compact prediction sets by concentrating on task-relevant information while suppressing nuisance variation. Second, AFOCP incorporates an attention mechanism that adaptively weights historical observations based on their relevance to the current test point, effectively handling non-stationarity and distribution shifts. We provide theoretical guarantees showing that AFOCP maintains long-term coverage while provably achieving smaller prediction intervals than standard OCP under mild regularity conditions. Extensive experiments on synthetic and real-world time series datasets demonstrate that AFOCP consistently reduces the size of prediction intervals by as much as $88\%$ as compared to OCP, while maintaining target coverage levels, validating the benefits of both feature-space calibration and attention-based adaptive weighting.
Transparent Early ICU Mortality Prediction with Clinical Transformer and Per-Case Modality Attribution
Venue: arXiv cs.LG 2025
Year: 2025

Early identification of intensive care patients at risk of in-hospital mortality enables timely intervention and efficient resource allocation. Despite high predictive performance, existing machine learning approaches lack transparency and robustness, limiting clinical adoption. We present a lightweight, transparent multimodal ensemble that fuses physiological time-series measurements with unstructured clinical notes from the first 48 hours of an ICU stay. A logistic regression model combines predictions from two modality-specific models: a bidirectional LSTM for vitals and a finetuned ClinicalModernBERT transformer for notes. This traceable architecture allows for multilevel interpretability: feature attributions within each modality and direct per-case modality attributions quantifying how vitals and notes influence each decision. On the MIMIC-III benchmark, our late-fusion ensemble improves discrimination over the best single model (AUPRC 0.565 vs. 0.526; AUROC 0.891 vs. 0.876) while maintaining well-calibrated predictions. The system remains robust through a calibrated fallback when a modality is missing. These results demonstrate competitive performance with reliable, auditable risk estimates and transparent, predictable operation, which together are crucial for clinical use.
Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

The automated analysis of historical documents, particularly maps, has drastically benefited from advances in deep learning and its success across various computer vision applications. However, most deep learning-based methods heavily rely on large amounts of annotated training data, which are typically unavailable for historical maps, especially for those belonging to specific, homogeneous cartographic domains, also known as corpora. Creating high-quality training data suitable for machine learning often takes a significant amount of time and involves extensive manual effort. While synthetic training data can alleviate the scarcity of real-world samples, it often lacks the affinity (realism) and diversity (variation) necessary for effective learning. By transferring the cartographic style of an original historical map corpus onto vector data, we bootstrap an effectively unlimited number of synthetic historical maps suitable for tasks such as land-cover interpretation of a homogeneous historical map corpus. We propose an automatic deep generative approach and a alternative manual stochastic degradation technique to emulate the visual uncertainty and noise, also known as data-dependent uncertainty, commonly observed in historical map scans. To quantitatively evaluate the effectiveness and applicability of our approach, the generated training datasets were employed for domain-adaptive semantic segmentation on a homogeneous map corpus using a Self-Constructing Graph Convolutional Network, enabling a comprehensive assessment of the impact of our data bootstrapping methods.
Mind the Motions: Benchmarking Theory-of-Mind in Everyday Body Language
Venue: arXiv cs.CL 2025
Year: 2025

Our ability to interpret others' mental states through nonverbal cues (NVCs) is fundamental to our survival and social cohesion. While existing Theory of Mind (ToM) benchmarks have primarily focused on false-belief tasks and reasoning with asymmetric information, they overlook other mental states beyond belief and the rich tapestry of human nonverbal communication. We present Motion2Mind, a framework for evaluating the ToM capabilities of machines in interpreting NVCs. Leveraging an expert-curated body-language reference as a proxy knowledge base, we build Motion2Mind, a carefully curated video dataset with fine-grained nonverbal cue annotations paired with manually verified psychological interpretations. It encompasses 222 types of nonverbal cues and 397 mind states. Our evaluation reveals that current AI systems struggle significantly with NVC interpretation, exhibiting not only a substantial performance gap in Detection, as well as patterns of over-interpretation in Explanation compared to human annotators.
Thinking, Faithful and Stable: Mitigating Hallucinations in LLMs
Venue: arXiv cs.AI 2025
Year: 2025

This project develops a self correcting framework for large language models (LLMs) that detects and mitigates hallucinations during multi-step reasoning. Rather than relying solely on final answer correctness, our approach leverages fine grained uncertainty signals: 1) self-assessed confidence alignment, and 2) token-level entropy spikes to detect unreliable and unfaithful reasoning in real time. We design a composite reward function that penalizes unjustified high confidence and entropy spikes, while encouraging stable and accurate reasoning trajectories. These signals guide a reinforcement learning (RL) policy that makes the model more introspective and shapes the model's generation behavior through confidence-aware reward feedback, improving not just outcome correctness but the coherence and faithfulness of their intermediate reasoning steps. Experiments show that our method improves both final answer accuracy and reasoning calibration, with ablations validating the individual contribution of each signal.
Optimizing Communication in Byzantine Agreement Protocols with Slim-HBBFT
Venue: arXiv cs.DC 2025
Year: 2025

Byzantine agreement protocols in asynchronous networks have received renewed interest because they do not rely on network behavior to achieve termination. Conventional asynchronous Byzantine agreement protocols require every party to broadcast its requests (e.g., transactions), and at the end of the protocol, parties agree on one party's request. If parties agree on one party's requests while exchanging every party's request, the protocol becomes expensive. These protocols are used to design an atomic broadcast (ABC) protocol where parties agree on $\langle n-f \rangle$ parties' requests (assuming $n=3f+1$, where $n$ is the total number of parties, and $f$ is the number of Byzantine parties). Although the parties agree on a subset of requests in the ABC protocol, if the requests do not vary (are duplicated), investing in a costly protocol is not justified. We propose Slim-HBBFT, an atomic broadcast protocol that considers requests from a fraction of $n$ parties and improves communication complexity by a factor of $O(n)$. At the core of our design is a prioritized provable-broadcast (P-PB) protocol that generates proof of broadcast only for selected parties. We use the P-PB protocol to design the Slim-HBBFT atomic broadcast protocol. Additionally, we conduct a comprehensive security analysis to demonstrate that Slim-HBBFT satisfies the properties of the Asynchronous Common Subset protocol, ensuring robust security and reliability.
JudgeBoard: Benchmarking and Enhancing Small Language Models for Reasoning Evaluation
Venue: arXiv cs.AI 2025
Year: 2025

While small language models (SLMs) have shown promise on various reasoning tasks, their ability to judge the correctness of answers remains unclear compared to large language models (LLMs). Prior work on LLM-as-a-judge frameworks typically relies on comparing candidate answers against ground-truth labels or other candidate answers using predefined metrics like entailment. However, this approach is inherently indirect and difficult to fully automate, offering limited support for fine-grained and scalable evaluation of reasoning outputs. In this work, we propose JudgeBoard, a novel evaluation pipeline that directly queries models to assess the correctness of candidate answers without requiring extra answer comparisons. We focus on two core reasoning domains: mathematical reasoning and science/commonsense reasoning, and construct task-specific evaluation leaderboards using both accuracy-based ranking and an Elo-based rating system across five benchmark datasets, enabling consistent model comparison as judges rather than comparators. To improve judgment performance in lightweight models, we propose MAJ (Multi-Agent Judging), a novel multi-agent evaluation framework that leverages multiple interacting SLMs with distinct reasoning profiles to approximate LLM-level judgment accuracy through collaborative deliberation. Experimental results reveal a significant performance gap between SLMs and LLMs in isolated judging tasks. However, our MAJ framework substantially improves the reliability and consistency of SLMs. On the MATH dataset, MAJ using smaller-sized models as backbones performs comparatively well or even better than their larger-sized counterparts. Our findings highlight that multi-agent SLM systems can potentially match or exceed LLM performance in judgment tasks, with implications for scalable and efficient assessment.
Efficient Chromosome Parallelization for Precision Medicine Genomic Workflows
Venue: arXiv cs.DC 2025
Year: 2025

Large-scale genomic workflows used in precision medicine can process datasets spanning tens to hundreds of gigabytes per sample, leading to high memory spikes, intensive disk I/O, and task failures due to out-of-memory errors. Simple static resource allocation methods struggle to handle the variability in per-chromosome RAM demands, resulting in poor resource utilization and long runtimes. In this work, we propose multiple mechanisms for adaptive, RAM-efficient parallelization of chromosome-level bioinformatics workflows. First, we develop a symbolic regression model that estimates per-chromosome memory consumption for a given task and introduces an interpolating bias to conservatively minimize over-allocation. Second, we present a dynamic scheduler that adaptively predicts RAM usage with a polynomial regression model, treating task packing as a Knapsack problem to optimally batch jobs based on predicted memory requirements. Additionally, we present a static scheduler that optimizes chromosome processing order to minimize peak memory while preserving throughput. Our proposed methods, evaluated on simulations and real-world genomic pipelines, provide new mechanisms to reduce memory overruns and balance load across threads. We thereby achieve faster end-to-end execution, showcasing the potential to optimize large-scale genomic workflows.
Scenario-Aware Control of Segmented Ladder Bus: Design and FPGA Implementation
Venue: arXiv cs.NE 2025
Year: 2025

Large-scale neuromorphic architectures consist of computing tiles that communicate spikes using a shared interconnect. The communication patterns in these systems are inherently sparse, asynchronous, and localized, as neural activity is characterized by temporal sparsity with occasional bursts of high traffic. These characteristics require optimized interconnects to handle high-activity bursts while consuming minimal power during idle periods. Among the proposed interconnect solutions, the dynamic segmented bus has gained attention due to its structural simplicity, scalability, and energy efficiency. Since the benefits of a dynamic segmented bus stem from its simplicity, it is essential to develop a streamlined control plane that can scale efficiently with the network. In this paper, we present a design methodology for a scenario-aware control plane tailored to a segmented ladder bus, with the aim of minimizing control overhead and optimizing energy and area utilization. We evaluated our approach using a combination of FPGA implementation and software simulation to assess scalability. The results demonstrated that our design process effectively reduces the control plane's area footprint compared to the data plane while maintaining scalability with network size.
QueryGym: A Toolkit for Reproducible LLM-Based Query Reformulation
Venue: arXiv cs.IR 2025
Year: 2025

We present QueryGym, a lightweight, extensible Python toolkit that supports large language model (LLM)-based query reformulation. This is an important tool development since recent work on llm-based query reformulation has shown notable increase in retrieval effectiveness. However, while different authors have sporadically shared the implementation of their methods, there is no unified toolkit that provides a consistent implementation of such methods, which hinders fair comparison, rapid experimentation, consistent benchmarking and reliable deployment. QueryGym addresses this gap by providing a unified framework for implementing, executing, and comparing llm-based reformulation methods. The toolkit offers: (1) a Python API for applying diverse LLM-based methods, (2) a retrieval-agnostic interface supporting integration with backends such as Pyserini and PyTerrier, (3) a centralized prompt management system with versioning and metadata tracking, (4) built-in support for benchmarks like BEIR and MS MARCO, and (5) a completely open-source extensible implementation available to all researchers. QueryGym is publicly available at https://github.com/radinhamidi/QueryGym.