OpenRaiser/Intern-Atlas huggingface dataset:
Beyond One-Way Pruning: Bidirectional Pruning-Regrowth for Extreme Accuracy-Sparsity Tradeoff
Venue: arXiv cs.LG 2025
Year: 2025

As a widely adopted model compression technique, model pruning has demonstrated strong effectiveness across various architectures. However, we observe that when sparsity exceeds a certain threshold, both iterative and one-shot pruning methods lead to a steep decline in model performance. This rapid degradation limits the achievable compression ratio and prevents models from meeting the stringent size constraints required by certain hardware platforms, rendering them inoperable. To overcome this limitation, we propose a bidirectional pruning-regrowth strategy. Starting from an extremely compressed network that satisfies hardware constraints, the method selectively regenerates critical connections to recover lost performance, effectively mitigating the sharp accuracy drop commonly observed under high sparsity conditions.
Probabilistic Wildfire Susceptibility from Remote Sensing Using Random Forests and SHAP
Venue: arXiv cs.LG 2025
Year: 2025

Wildfires pose a significant global threat to ecosystems worldwide, with California experiencing recurring fires due to various factors, including climate, topographical features, vegetation patterns, and human activities. This study aims to develop a comprehensive wildfire risk map for California by applying the random forest (RF) algorithm, augmented with Explainable Artificial Intelligence (XAI) through Shapley Additive exPlanations (SHAP), to interpret model predictions. Model performance was assessed using both spatial and temporal validation strategies. The RF model demonstrated strong predictive performance, achieving near-perfect discrimination for grasslands (AUC = 0.996) and forests (AUC = 0.997). Spatial cross-validation revealed moderate transferability, yielding ROC-AUC values of 0.6155 for forests and 0.5416 for grasslands. In contrast, temporal split validation showed enhanced generalization, especially for forests (ROC-AUC = 0.6615, PR-AUC = 0.8423). SHAP-based XAI analysis identified key ecosystem-specific drivers: soil organic carbon, tree cover, and Normalized Difference Vegetation Index (NDVI) emerged as the most influential in forests, whereas Land Surface Temperature (LST), elevation, and vegetation health indices were dominant in grasslands. District-level classification revealed that Central Valley and Northern Buttes districts had the highest concentration of high-risk grasslands, while Northern Buttes and North Coast Redwoods dominated forested high-risk areas. This RF-SHAP framework offers a robust, comprehensible, and adaptable method for assessing wildfire risks, enabling informed decisions and creating targeted strategies to mitigate dangers.
A Bayesian Model for Multi-stage Censoring
Venue: arXiv cs.LG 2025
Year: 2025

Many sequential decision settings in healthcare feature funnel structures characterized by a series of stages, such as screenings or evaluations, where the number of patients who advance to each stage progressively decreases and decisions become increasingly costly. For example, an oncologist may first conduct a breast exam, followed by a mammogram for patients with concerning exams, followed by a biopsy for patients with concerning mammograms. A key challenge is that the ground truth outcome, such as the biopsy result, is only revealed at the end of this funnel. The selective censoring of the ground truth can introduce statistical biases in risk estimation, especially in underserved patient groups, whose outcomes are more frequently censored. We develop a Bayesian model for funnel decision structures, drawing from prior work on selective labels and censoring. We first show in synthetic settings that our model is able to recover the true parameters and predict outcomes for censored patients more accurately than baselines. We then apply our model to a dataset of emergency department visits, where in-hospital mortality is observed only for those who are admitted to either the hospital or ICU. We find that there are gender-based differences in hospital and ICU admissions. In particular, our model estimates that the mortality risk threshold to admit women to the ICU is higher for women (5.1%) than for men (4.5%).
Which Sparse Autoencoder Features Are Real? Model-X Knockoffs for False Discovery Rate Control
Venue: arXiv cs.LG 2025
Year: 2025

Although sparse autoencoders (SAEs) are crucial for identifying interpretable features in neural networks, it is still challenging to distinguish between real computational patterns and erroneous correlations. We introduce Model-X knockoffs to SAE feature selection, using knock-off+ to control the false discovery rate (FDR) with finite-sample guarantees under the standard Model-X assumptions (in our case, via a Gaussian surrogate for the latent distribution). We select 129 features at a target FDR q=0.1 after analyzing 512 high-activity SAE latents for sentiment classification using Pythia-70M. About 25% of the latents under examination carry task-relevant signal, whereas 75% do not, according to the chosen set, which displays a 5.40x separation in knockoff statistics compared to non-selected features. Our method offers a re-producible and principled framework for reliable feature discovery by combining SAEs with multiple-testing-aware inference, advancing the foundations of mechanistic interpretability.
Multiscale Grassmann Manifolds for Single-Cell Data Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Single-cell data analysis seeks to characterize cellular heterogeneity based on high-dimensional gene expression profiles. Conventional approaches represent each cell as a vector in Euclidean space, which limits their ability to capture intrinsic correlations and multiscale geometric structures. We propose a multiscale framework based on Grassmann manifolds that integrates machine learning with subspace geometry for single-cell data analysis. By generating embeddings under multiple representation scales, the framework combines their features from different geometric views into a unified Grassmann manifold. A power-based scale sampling function is introduced to control the selection of scales and balance in- formation across resolutions. Experiments on nine benchmark single-cell RNA-seq datasets demonstrate that the proposed approach effectively preserves meaningful structures and provides stable clustering performance, particularly for small to medium-sized datasets. These results suggest that Grassmann manifolds offer a coherent and informative foundation for analyzing single cell data.
A Meta-Heuristic Load Balancer for Cloud Computing Systems
Venue: arXiv cs.DC 2025
Year: 2025

This paper presents a strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.
An Integrated SERVQUAL and Lean Six Sigma Framework for Measuring Customer Satisfaction in Computer Service Companies
Venue: arXiv cs.CY 2025
Year: 2025

The computer service industry has expanded rapidly over the past two decades, driven by the proliferation of computing technologies, the entry of large firms, and the availability of online diagnostic and troubleshooting tools. In this increasingly competitive environment, many small and medium sized enterprises struggle to maintain customer satisfaction as rivals deliver higher quality services at lower cost. This study addresses the absence of robust measurement systems for assessing service quality, a key factor underlying customer attrition, by proposing an integrated framework for evaluating satisfaction and identifying sources of dissatisfaction in computer services. The framework combines core principles of Six Sigma with the SERVQUAL instrument within a structured DMAIC methodology (Define, Measure, Analyze, Improve, and Control). SERVQUAL provides the service quality dimensions and gap analysis techniques, while Six Sigma supplies the data driven approach to measurement and improvement. The literature suggests limited prior work integrating Lean Six Sigma with SERVQUAL, and this study contributes by operationalizing that integration in a real world setting. A case study of a computer services company was conducted to demonstrate feasibility and effectiveness. Satisfaction levels were quantified, and root causes of dissatisfaction were identified. The analysis revealed a low overall satisfaction level and five primary drivers of unmet customer requirements. Addressing these causes is expected to increase customer satisfaction, lower customer acquisition costs, and improve overall organizational performance.
How Machine Learning-Data Driven Replication Strategies Enhance Fault Tolerance in Large-Scale Distributed Systems
Venue: arXiv cs.DC 2025
Year: 2025

This research paper investigates how machine learning-driven data replication strategies can enhance fault tolerance in large-scale distributed systems. Traditional replication methods, which rely on static configurations, often struggle to adapt to dynamic workloads and unexpected failures, leading to inefficient resource utilization and prolonged downtime. By integrating machine learning techniques-specifically predictive analytics and reinforcement learning. The study proposes adaptive replication mechanisms capable of forecasting system failures and optimizing data placement in real time. Through an extensive literature review, qualitative analysis, and comparative evaluations with traditional approaches, the paper identifies key limitations in existing replication strategies and highlights the transformative potential of machine learning in creating more resilient, self-optimizing systems. The findings underscore both the promise and the challenges of implementing ML-driven solutions in real-world environments, offering recommendations for future research and practical deployment in cloud-based and enterprise systems.
Batch Transformer Architecture: Case of Synthetic Image Generation for Emotion Expression Facial Recognition
Venue: arXiv cs.CV 2025
Year: 2025

A novel Transformer variation architecture is proposed in the implicit sparse style. Unlike "traditional" Transformers, instead of attention to sequential or batch entities in their entirety of whole dimensionality, in the proposed Batch Transformers, attention to the "important" dimensions (primary components) is implemented. In such a way, the "important" dimensions or feature selection allows for a significant reduction of the bottleneck size in the encoder-decoder ANN architectures. The proposed architecture is tested on the synthetic image generation for the face recognition task in the case of the makeup and occlusion data set, allowing for increased variability of the limited original data set.
Bridging the Skills Gap: A Course Model for Modern Generative AI Education
Venue: arXiv cs.CY 2025
Year: 2025

Research on how the popularization of generative Artificial Intelligence (AI) tools impacts learning environments has led to hesitancy among educators to teach these tools in classrooms, creating two observed disconnects. Generative AI competency is increasingly valued in industry but not in higher education, and students are experimenting with generative AI without formal guidance. The authors argue students across fields must be taught to responsibly and expertly harness the potential of AI tools to ensure job market readiness and positive outcomes. Computer Science trajectories are particularly impacted, and while consistently top ranked U.S. Computer Science departments teach the mechanisms and frameworks underlying AI, few appear to offer courses on applications for existing generative AI tools. A course was developed at a private research university to teach undergraduate and graduate Computer Science students applications for generative AI tools in software development. Two mixed method surveys indicated students overwhelmingly found the course valuable and effective. Co-authored by the instructor and one of the graduate students, this paper explores the context, implementation, and impact of the course through data analysis and reflections from both perspectives. It additionally offers recommendations for replication in and beyond Computer Science departments. This is the extended version of this paper to include technical appendices.
Scaling Equitable Reflection Assessment in Education via Large Language Models and Role-Based Feedback Agents
Venue: arXiv cs.CY 2025
Year: 2025

Formative feedback is widely recognized as one of the most effective drivers of student learning, yet it remains difficult to implement equitably at scale. In large or low-resource courses, instructors often lack the time, staffing, and bandwidth required to review and respond to every student reflection, creating gaps in support precisely where learners would benefit most. This paper presents a theory-grounded system that uses five coordinated role-based LLM agents (Evaluator, Equity Monitor, Metacognitive Coach, Aggregator, and Reflexion Reviewer) to score learner reflections with a shared rubric and to generate short, bias-aware, learner-facing comments. The agents first produce structured rubric scores, then check for potentially biased or exclusionary language, add metacognitive prompts that invite students to think about their own thinking, and finally compose a concise feedback message of at most 120 words. The system includes simple fairness checks that compare scoring error across lower and higher scoring learners, enabling instructors to monitor and bound disparities in accuracy. We evaluate the pipeline in a 12-session AI literacy program with adult learners. In this setting, the system produces rubric scores that approach expert-level agreement, and trained graders rate the AI-generated comments as helpful, empathetic, and well aligned with instructional goals. Taken together, these results show that multi-agent LLM systems can deliver equitable, high-quality formative feedback at a scale and speed that would be impossible for human graders alone. More broadly, the work points toward a future where feedback-rich learning becomes feasible for any course size or context, advancing long-standing goals of equity, access, and instructional capacity in education.
CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning is a promising paradigm that utilizes distributed client resources while preserving data privacy. Most existing FL approaches assume clients possess labeled data, however, in real-world scenarios, client-side labels are often unavailable. Semi-supervised Federated learning, where only the server holds labeled data, addresses this issue. However, it experiences significant performance degradation as the number of labeled data decreases. To tackle this problem, we propose \textit{CATCHFed}, which introduces client-aware adaptive thresholds considering class difficulty, hybrid thresholds to enhance pseudo-label quality, and utilizes unpseudo-labeled data for consistency regularization. Extensive experiments across various datasets and configurations demonstrate that CATCHFed effectively leverages unlabeled client data, achieving superior performance even in extremely limited-label settings.
Do LLMs Really Struggle at NL-FOL Translation? Revealing their Strengths via a Novel Benchmarking Strategy
Venue: arXiv cs.AI 2025
Year: 2025

Due to its expressiveness and unambiguous nature, First-Order Logic (FOL) is a powerful formalism for representing concepts expressed in natural language (NL). This is useful, e.g., for specifying and verifying desired system properties. While translating FOL into human-readable English is relatively straightforward, the inverse problem, converting NL to FOL (NL-FOL translation), has remained a longstanding challenge, for both humans and machines. Although the emergence of Large Language Models (LLMs) promised a breakthrough, recent literature provides contrasting results on their ability to perform NL-FOL translation. In this work, we provide a threefold contribution. First, we critically examine existing datasets and protocols for evaluating NL-FOL translation performance, revealing key limitations that may cause a misrepresentation of LLMs' actual capabilities. Second, to overcome these shortcomings, we propose a novel evaluation protocol explicitly designed to distinguish genuine semantic-level logical understanding from superficial pattern recognition, memorization, and dataset contamination. Third, using this new approach, we show that state-of-the-art, dialogue-oriented LLMs demonstrate strong NL-FOL translation skills and a genuine grasp of sentence-level logic, whereas embedding-centric models perform markedly worse.
Towards Autoformalization of LLM-generated Outputs for Requirement Verification
Venue: arXiv cs.CL 2025
Year: 2025

Autoformalization, the process of translating informal statements into formal logic, has gained renewed interest with the emergence of powerful Large Language Models (LLMs). While LLMs show promise in generating structured outputs from natural language (NL), such as Gherkin Scenarios from NL feature requirements, there's currently no formal method to verify if these outputs are accurate. This paper takes a preliminary step toward addressing this gap by exploring the use of a simple LLM-based autoformalizer to verify LLM-generated outputs against a small set of natural language requirements. We conducted two distinct experiments. In the first one, the autoformalizer successfully identified that two differently-worded NL requirements were logically equivalent, demonstrating the pipeline's potential for consistency checks. In the second, the autoformalizer was used to identify a logical inconsistency between a given NL requirement and an LLM-generated output, highlighting its utility as a formal verification tool. Our findings, while limited, suggest that autoformalization holds significant potential for ensuring the fidelity and logical consistency of LLM-generated outputs, laying a crucial foundation for future, more extensive studies into this novel application.
TD-Orch: Scalable Load-Balancing for Distributed Systems with Applications to Graph Processing
Venue: arXiv cs.DC 2025
Year: 2025

In this paper, we introduce a task-data orchestration abstraction that supports a range of distributed applications, including graph processing and key-value stores. Given a batch of lambda tasks each requesting one or more data items, where both tasks and data are distributed across multiple machines, each task must be co-located with its target data (by moving tasks and/or data) and then executed. We present TD-Orch, an efficient and scalable orchestration framework featuring a simple application developer interface. TD-Orch employs a distributed push-pull technique, leveraging the bidirectional flow of both tasks and data to achieve scalable load balance across machines even under highly skewed data requests (data hot spots), with minimal communication overhead. Experimental results show that TD-Orch achieves up to 2.8x speedup over existing distributed scheduling baselines. Building on TD-Orch, we present TDO-GP, a distributed graph processing system for general graph problems, demonstrating the effectiveness of the underlying framework. We design three families of implementation techniques to fully leverage the execution flow provided by TD-Orch. Experimental results show that TDO-GP achieves an average speedup of 4.1x over the best prior open-source distributed graph systems for general graph processing.
Learning Conjugate Direction Fields for Planar Quadrilateral Mesh Generation
Venue: arXiv cs.GR 2025
Year: 2025

Planar quadrilateral (PQ) mesh generation is a key process in computer-aided design, particularly for architectural applications where the goal is to discretize a freeform surface using planar quad faces. The conjugate direction field (CDF) defined on the freeform surface plays a significant role in generating a PQ mesh, as it largely determines the PQ mesh layout. Conventionally, a CDF is obtained by solving a complex non-linear optimization problem that incorporates user preferences, i.e., aligning the CDF with user-specified strokes on the surface. This often requires a large number of iterations that are computationally expensive, preventing the interactive CDF design process for a desirable PQ mesh. To address this challenge, we propose a data-driven approach based on neural networks for controlled CDF generation. Our approach can effectively learn and fuse features from the freeform surface and the user strokes, and efficiently generate quality CDF respecting user guidance. To enable training and testing, we also present a dataset composed of 50000+ freeform surfaces with ground-truth CDFs, as well as a set of metrics for quantitative evaluation. The effectiveness and efficiency of our work are demonstrated by extensive experiments using testing data, architectural surfaces, and general 3D shapes.
A Leakage-Aware Data Layer For Student Analytics: The Capire Framework For Multilevel Trajectory Modeling
Venue: arXiv cs.CY 2025
Year: 2025

Predictive models for student dropout, while often accurate, frequently rely on opportunistic feature sets and suffer from undocumented data leakage, limiting their explanatory power and institutional usefulness. This paper introduces a leakage-aware data layer for student trajectory analytics, which serves as the methodological foundation for the CAPIRE framework for multilevel modelling. We propose a feature engineering design that organizes predictors into four levels: N1 (personal and socio-economic attributes), N2 (entry moment and academic history), N3 (curricular friction and performance), and N4 (institutional and macro-context variables)As a core component, we formalize the Value of Observation Time (VOT) as a critical design parameter that rigorously separates observation windows from outcome horizons, preventing data leakage by construction. An illustrative application in a long-cycle engineering program (1,343 students, ~57% dropout) demonstrates that VOT-restricted multilevel features support robust archetype discovery. A UMAP + DBSCAN pipeline uncovers 13 trajectory archetypes, including profiles of "early structural crisis," "sustained friction," and "hidden vulnerability" (low friction but high dropout). Bootstrap and permutation tests confirm these archetypes are statistically robust and temporally stable. We argue that this approach transforms feature engineering from a technical step into a central methodological artifact. This data layer serves as a disciplined bridge between retention theory, early-warning systems, and the future implementation of causal inference and agent-based modelling (ABM) within the CAPIRE program.