OpenRaiser/Intern-Atlas huggingface dataset:
VizGenie: Toward Self-Refining, Domain-Aware Workflows for Next-Generation Scientific Visualization
Venue: arXiv cs.HC 2025
Year: 2025

We present VizGenie, a self-improving, agentic framework that advances scientific visualization through large language model (LLM) by orchestrating of a collection of domain-specific and dynamically generated modules. Users initially access core functionalities--such as threshold-based filtering, slice extraction, and statistical analysis--through pre-existing tools. For tasks beyond this baseline, VizGenie autonomously employs LLMs to generate new visualization scripts (e.g., VTK Python code), expanding its capabilities on-demand. Each generated script undergoes automated backend validation and is seamlessly integrated upon successful testing, continuously enhancing the system's adaptability and robustness. A distinctive feature of VizGenie is its intuitive natural language interface, allowing users to issue high-level feature-based queries (e.g., ``visualize the skull"). The system leverages image-based analysis and visual question answering (VQA) via fine-tuned vision models to interpret these queries precisely, bridging domain expertise and technical implementation. Additionally, users can interactively query generated visualizations through VQA, facilitating deeper exploration. Reliability and reproducibility are further strengthened by Retrieval-Augmented Generation (RAG), providing context-driven responses while maintaining comprehensive provenance records. Evaluations on complex volumetric datasets demonstrate significant reductions in cognitive overhead for iterative visualization tasks. By integrating curated domain-specific tools with LLM-driven flexibility, VizGenie not only accelerates insight generation but also establishes a sustainable, continuously evolving visualization practice. The resulting platform dynamically learns from user interactions, consistently enhancing support for feature-centric exploration and reproducible research in scientific visualization.
A Study on Variants of Conventional, Fuzzy, and Nullspace-Based Independence Criteria for Improving Supervised and Unsupervised Learning
Venue: arXiv cs.LG 2025
Year: 2025

Unsupervised and supervised learning methods conventionally use kernels to capture nonlinearities inherent in data structure. However experts have to ensure their proposed nonlinearity maximizes variability and capture inherent diversity of data. We reviewed all independence criteria to design unsupervised learners. Then we proposed 3 independence criteria and used them to design unsupervised and supervised dimensionality reduction methods. We evaluated contrast, accuracy and interpretability of these methods in both linear and neural nonlinear settings. The results show that the methods have outperformed the baseline (tSNE, PCA, regularized LDA, VAE with (un)supervised learner and layer sharing) and opened a new line of interpretable machine learning (ML) for the researchers.
Project Patti: Why can You Solve Diabolical Puzzles on one Sudoku Website but not Easy Puzzles on another Sudoku Website?
Venue: arXiv cs.AI 2025
Year: 2025

In this paper we try to answer the question "What constitutes Sudoku difficulty rating across different Sudoku websites?" Using two distinct methods that can both solve every Sudoku puzzle, I propose two new metrics to characterize Sudoku difficulty. The first method is based on converting a Sudoku puzzle into its corresponding Satisfiability (SAT) problem. The first proposed metric is derived from SAT Clause Length Distribution which captures the structural complexity of a Sudoku puzzle including the number of given digits and the cells they are in. The second method simulates human Sudoku solvers by intertwining four popular Sudoku strategies within a backtracking algorithm called Nishio. The second metric is computed by counting the number of times Sudoku strategies are applied within the backtracking iterations of a randomized Nishio. Using these two metrics, I analyze more than a thousand Sudoku puzzles across five popular websites to characterize every difficulty level in each website. I evaluate the relationship between the proposed metrics and website-labeled difficulty levels using Spearman's rank correlation coefficient, finding strong correlations for 4 out of 5 websites. I construct a universal rating system using a simple, unsupervised classifier based on the two proposed metrics. This rating system is capable of classifying both individual puzzles and entire difficulty levels from the different Sudoku websites into three categories - Universal Easy, Universal Medium, and Universal Hard - thereby enabling consistent difficulty mapping across Sudoku websites. The experimental results show that for 4 out of 5 Sudoku websites, the universal classification aligns well with website-labeled difficulty levels. Finally, I present an algorithm that can be used by early Sudoku practitioners to solve Sudoku puzzles.
The Geometry of Harmfulness in LLMs through Subconcept Probing
Venue: arXiv cs.AI 2025
Year: 2025

Recent advances in large language models (LLMs) have intensified the need to understand and reliably curb their harmful behaviours. We introduce a multidimensional framework for probing and steering harmful content in model internals. For each of 55 distinct harmfulness subconcepts (e.g., racial hate, employment scams, weapons), we learn a linear probe, yielding 55 interpretable directions in activation space. Collectively, these directions span a harmfulness subspace that we show is strikingly low-rank. We then test ablation of the entire subspace from model internals, as well as steering and ablation in the subspace's dominant direction. We find that dominant direction steering allows for near elimination of harmfulness with a low decrease in utility. Our findings advance the emerging view that concept subspaces provide a scalable lens on LLM behaviour and offer practical tools for the community to audit and harden future generations of language models.
WaveVerify: A Novel Audio Watermarking Framework for Media Authentication and Combatting Deepfakes
Venue: arXiv cs.CR 2025
Year: 2025

The rapid advancement of voice generation technologies has enabled the synthesis of speech that is perceptually indistinguishable from genuine human voices. While these innovations facilitate beneficial applications such as personalized text-to-speech systems and voice preservation, they have also introduced significant risks, including deepfake impersonation scams and synthetic media-driven disinformation campaigns. Recent reports indicate that in 2024, deepfake fraud attempts surged by over 1,300% compared to 2023, underscoring the urgent need for robust audio content authentication. The financial sector has been particularly impacted, with a loss of over 10 million USD to voice scams and individual victims reporting losses exceeding $6,000 from AI-generated deepfake calls. In response, regulators and governments worldwide are enacting measures to improve AI content transparency and traceability, emphasizing the development of forensic tools and watermarking techniques as essential strategies to uphold media integrity.
Deep Reinforcement Learning for Real-Time Green Energy Integration in Data Centers
Venue: arXiv cs.LG 2025
Year: 2025

This paper explores the implementation of a Deep Reinforcement Learning (DRL)-optimized energy management system for e-commerce data centers, aimed at enhancing energy efficiency, cost-effectiveness, and environmental sustainability. The proposed system leverages DRL algorithms to dynamically manage the integration of renewable energy sources, energy storage, and grid power, adapting to fluctuating energy availability in real time. The study demonstrates that the DRL-optimized system achieves a 38\% reduction in energy costs, significantly outperforming traditional Reinforcement Learning (RL) methods (28\%) and heuristic approaches (22\%). Additionally, it maintains a low SLA violation rate of 1.5\%, compared to 3.0\% for RL and 4.8\% for heuristic methods. The DRL-optimized approach also results in an 82\% improvement in energy efficiency, surpassing other methods, and a 45\% reduction in carbon emissions, making it the most environmentally friendly solution. The system's cumulative reward of 950 reflects its superior performance in balancing multiple objectives. Through rigorous testing and ablation studies, the paper validates the effectiveness of the DRL model's architecture and parameters, offering a robust solution for energy management in data centers. The findings highlight the potential of DRL in advancing energy optimization strategies and addressing sustainability challenges.
SPADE-S: A Sparsity-Robust Foundational Forecaster
Venue: arXiv cs.LG 2025
Year: 2025

Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging for state-of-the-art deep learning architectures. We identify several factors that lead existing models to systematically underperform on low-magnitude and sparse time series, including loss functions with implicit biases toward high-magnitude series, training-time sampling methods, and limitations of time series encoding methods. SPADE-S is a robust forecasting architecture that significantly reduces magnitude- and sparsity-based systematic biases and improves overall prediction accuracy. Empirical results demonstrate that SPADE-S outperforms existing state-of-the-art approaches across a diverse set of use cases in demand forecasting. In particular, we show that, depending on the quantile forecast and magnitude of the series, SPADE-S can improve forecast accuracy by up to 15%. This results in P90 overall forecast accuracy gains of 2.21%, 6.58%, and 4.28%, and P50 forecast accuracy gains of 0.92%, 0.77%, and 1.95%, respectively, for each of three distinct datasets, ranging from 3 million to 700 million series, from a large online retailer.
AGORA: Incentivizing Group Emergence Capability in LLMs via Group Distillation
Venue: arXiv cs.LG 2025
Year: 2025

Progress in complex reasoning is constrained by the static nature of the current training datasets. We propose structured interaction as a new scaling axis, moving beyond the prevailing paradigm of increasing model parameters. Our self-evolving framework, AGORA, enables a collaborative ensemble to achieve reasoning performance exceeding state-of-the-art monolithic systems by up to 4.45 percentage points on challenging mathematical benchmarks. This gain stems from group emergent ability-the synthesis of collective capabilities unattainable by isolated models, validating interaction as a scalable driver of intelligence. Our results position the engineering of collaborative ecosystems as a vital frontier for capability emergence.
Trustworthy AI: UK Air Traffic Control Revisited
Venue: arXiv cs.CY 2025
Year: 2025

Exploring the socio-technical challenges confronting the adoption of AI in organisational settings is something that has so far been largely absent from the related literature. In particular, research into requirements for trustworthy AI typically overlooks how people deal with the problems of trust in the tools that they use as part of their everyday work practices. This article presents some findings from an ongoing ethnographic study of how current tools are used in air traffic control work and what it reveals about requirements for trustworthy AI in air traffic control and other safety-critical application domains.
A ChatGPT-based approach for questions generation in higher education
Venue: arXiv cs.CY 2025
Year: 2025

Large language models have been widely applied in many aspects of real life, bringing significant efficiency to businesses and offering distinctive user experiences. In this paper, we focus on exploring the application of ChatGPT, a chatbot based on a large language model, to support higher educator in generating quiz questions and assessing learners. Specifically, we explore interactive prompting patterns to design an optimal AI-powered question bank creation process. The generated questions are evaluated through a "Blind test" survey sent to various stakeholders including lecturers and learners. Initial results at the Banking Academy of Vietnam are relatively promising, suggesting a potential direction to streamline the time and effort involved in assessing learners at higher education institutes.
Embeddings to Diagnosis: Latent Fragility under Agentic Perturbations in Clinical LLMs
Venue: arXiv cs.LG 2025
Year: 2025

LLMs for clinical decision support often fail under small but clinically meaningful input shifts such as masking a symptom or negating a finding, despite high performance on static benchmarks. These reasoning failures frequently go undetected by standard NLP metrics, which are insensitive to latent representation shifts that drive diagnosis instability. We propose a geometry-aware evaluation framework, LAPD (Latent Agentic Perturbation Diagnostics), which systematically probes the latent robustness of clinical LLMs under structured adversarial edits. Within this framework, we introduce Latent Diagnosis Flip Rate (LDFR), a model-agnostic diagnostic signal that captures representational instability when embeddings cross decision boundaries in PCA-reduced latent space. Clinical notes are generated using a structured prompting pipeline grounded in diagnostic reasoning, then perturbed along four axes: masking, negation, synonym replacement, and numeric variation to simulate common ambiguities and omissions. We compute LDFR across both foundation and clinical LLMs, finding that latent fragility emerges even under minimal surface-level changes. Finally, we validate our findings on 90 real clinical notes from the DiReCT benchmark (MIMIC-IV), confirming the generalizability of LDFR beyond synthetic settings. Our results reveal a persistent gap between surface robustness and semantic stability, underscoring the importance of geometry-aware auditing in safety-critical clinical AI.
Learning from Limited and Imperfect Data
Venue: arXiv cs.LG 2025
Year: 2025

The distribution of data in the world (eg, internet, etc.) significantly differs from the well-curated datasets and is often over-populated with samples from common categories. The algorithms designed for well-curated datasets perform suboptimally when used for learning from imperfect datasets with long-tailed imbalances and distribution shifts. To expand the use of deep models, it is essential to overcome the labor-intensive curation process by developing robust algorithms that can learn from diverse, real-world data distributions. Toward this goal, we develop practical algorithms for Deep Neural Networks which can learn from limited and imperfect data present in the real world. This thesis is divided into four segments, each covering a scenario of learning from limited or imperfect data. The first part of the thesis focuses on Learning Generative Models from Long-Tail Data, where we mitigate the mode-collapse and enable diverse aesthetic image generations for tail (minority) classes. In the second part, we enable effective generalization on tail classes through Inductive Regularization schemes, which allow tail classes to generalize as effectively as the head classes without requiring explicit generation of images. In the third part, we develop algorithms for Optimizing Relevant Metrics for learning from long-tailed data with limited annotation (semi-supervised), followed by the fourth part, which focuses on the Efficient Domain Adaptation of the model to various domains with very few to zero labeled samples.
Bangla BERT for Hyperpartisan News Detection: A Semi-Supervised and Explainable AI Approach
Venue: arXiv cs.CL 2025
Year: 2025

In the current digital landscape, misinformation circulates rapidly, shaping public perception and causing societal divisions. It is difficult to identify hyperpartisan news in Bangla since there aren't many sophisticated natural language processing methods available for this low-resource language. Without effective detection methods, biased content can spread unchecked, posing serious risks to informed discourse. To address this gap, our research fine-tunes Bangla BERT. This is a state-of-the-art transformer-based model, designed to enhance classification accuracy for hyperpartisan news. We evaluate its performance against traditional machine learning models and implement semi-supervised learning to enhance predictions further. Not only that, we use LIME to provide transparent explanations of the model's decision-making process, which helps to build trust in its outcomes. With a remarkable accuracy score of 95.65%, Bangla BERT outperforms conventional approaches, according to our trial data. The findings of this study demonstrate the usefulness of transformer models even in environments with limited resources, which opens the door to further improvements in this area.
Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors
Venue: arXiv cs.LG 2025
Year: 2025

In an inverse problem, the goal is to recover an unknown parameter (e.g., an image) that has typically undergone some lossy or noisy transformation during measurement. Recently, deep generative models, particularly diffusion models, have emerged as powerful priors for protein structure generation. However, integrating noisy experimental data from multiple sources to guide these models remains a significant challenge. Existing methods often require precise knowledge of experimental noise levels and manually tuned weights for each data modality. In this work, we introduce Adam-PnP, a Plug-and-Play framework that guides a pre-trained protein diffusion model using gradients from multiple, heterogeneous experimental sources. Our framework features an adaptive noise estimation scheme and a dynamic modality weighting mechanism integrated into the diffusion process, which reduce the need for manual hyperparameter tuning. Experiments on complex reconstruction tasks demonstrate significantly improved accuracy using Adam-PnP.
Generating Highly Structured Test Inputs Leveraging Constraint-Guided Graph Refinement
Venue: arXiv cs.SE 2025
Year: 2025

[Context] Modern AI applications increasingly process highly structured data, such as 3D meshes and point clouds, where test input generation must preserve both structural and semantic validity. However, existing fuzzing tools and input generators are typically handcrafted for specific input types and often generate invalid inputs that are subsequently discarded, leading to inefficiency and poor generalizability. [Objective] This study investigates whether test inputs for structured domains can be unified through a graph-based representation, enabling general, reusable mutation strategies while enforcing structural constraints. We will evaluate the effectiveness of this approach in enhancing input validity and semantic preservation across eight AI systems. [Method] We develop and evaluate GRAphRef, a graph-based test input generation framework that supports constraint-based mutation and refinement. GRAphRef maps structured inputs to graphs, applies neighbor-similarity-guided mutations, and uses a constraint-refinement phase to repair invalid inputs. We will conduct a confirmatory study across eight real-world mesh-processing AI systems, comparing GRAphRef with AFL, MeshAttack, Saffron, and two ablated variants. Evaluation metrics include structural validity, semantic preservation (via prediction consistency), and performance overhead. Experimental data is derived from ShapeNetCore mesh seeds and model outputs from systems like MeshCNN and HodgeNet. Statistical analysis and component latency breakdowns will be used to assess each hypothesis.
Curiosity by Design: An LLM-based Coding Assistant Asking Clarification Questions
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) are increasingly used as coding assistants. However, the ambiguity of the developer's prompt often leads to incorrect code generation, as current models struggle to infer user intent without extensive prompt engineering or external context. This work aims to build an LLM-based coding assistant that mimics the human code review process by asking clarification questions when faced with ambiguous or under-specified queries. Our end-to-end system includes (1) a query classifier trained to detect unclear programming-related queries and (2) a fine-tuned LLM that generates clarification questions. Our evaluation shows that the fine-tuned LLM outperforms standard zero-shot prompting in generating useful clarification questions. Furthermore, our user study indicates that users find the clarification questions generated by our model to outperform the baseline, demonstrating that our coding assistant produces more accurate and helpful code responses compared to baseline coding assistants.
Structured Relevance Assessment for Robust Retrieval-Augmented Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Retrieval-Augmented Language Models (RALMs) face significant challenges in reducing factual errors, particularly in document relevance evaluation and knowledge integration. We introduce a framework for structured relevance assessment that enhances RALM robustness through improved document evaluation, balanced intrinsic and external knowledge integration, and effective handling of unanswerable queries. Our approach employs a multi-dimensional scoring system that considers both semantic matching and source reliability, utilizing embedding-based relevance scoring and synthetic training data with mixed-quality documents. We implement specialized benchmarking on niche topics, a knowledge integration mechanism, and an "unknown" response protocol for queries with insufficient knowledge coverage. Preliminary evaluations demonstrate significant reductions in hallucination rates and improved transparency in reasoning processes. Our framework advances the development of more reliable question-answering systems capable of operating effectively in dynamic environments with variable data quality. While challenges persist in accurately distinguishing credible information and balancing system latency with thoroughness, this work represents a meaningful step toward enhancing RALM reliability.
A Deep Learning Automatic Speech Recognition Model for Shona Language
Venue: arXiv cs.CL 2025
Year: 2025

This study presented the development of a deep learning-based Automatic Speech Recognition system for Shona, a low-resource language characterized by unique tonal and grammatical complexities. The research aimed to address the challenges posed by limited training data, lack of labelled data, and the intricate tonal nuances present in Shona speech, with the objective of achieving significant improvements in recognition accuracy compared to traditional statistical models. The research first explored the feasibility of using deep learning to develop an accurate ASR system for Shona. Second, it investigated the specific challenges involved in designing and implementing deep learning architectures for Shona speech recognition and proposed strategies to mitigate these challenges. Lastly, it compared the performance of the deep learning-based model with existing statistical models in terms of accuracy. The developed ASR system utilized a hybrid architecture consisting of a Convolutional Neural Network for acoustic modelling and a Long Short-Term Memory network for language modelling. To overcome the scarcity of data, data augmentation techniques and transfer learning were employed. Attention mechanisms were also incorporated to accommodate the tonal nature of Shona speech. The resulting ASR system achieved impressive results, with a Word Error Rate of 29%, Phoneme Error Rate of 12%, and an overall accuracy of 74%. These metrics indicated the potential of deep learning to enhance ASR accuracy for under-resourced languages like Shona. This study contributed to the advancement of ASR technology for under-resourced languages like Shona, ultimately fostering improved accessibility and communication for Shona speakers worldwide.