OpenRaiser/Intern-Atlas huggingface dataset:
Skin Lesion Classification Based on ResNet-50 Enhanced With Adaptive Spatial Feature Fusion
Venue: arXiv cs.CV 2025
Year: 2025

Skin cancer classification remains a challenging problem due to high inter-class similarity, intra-class variability, and image noise in dermoscopic images. To address these issues, we propose an improved ResNet-50 model enhanced with Adaptive Spatial Feature Fusion (ASFF), which adaptively integrates multi-scale semantic and surface features to improve feature representation and reduce overfitting. The ResNet-50 model is enhanced with an adaptive feature fusion mechanism to achieve more effective multi-scale feature extraction and improve overall performance. Specifically, a dual-branch design fuses high-level semantic and mid-level detail features, which are processed through global average pooling and fully connected layers to generate adaptive weights for weighted fusion, thereby strengthening feature learning and reducing the impact of noise on classification. The method is evaluated on a subset of the ISIC 2020 dataset containing 3297 benign and malignant skin lesion images. Experimental results show that the proposed ASFF-based ResNet-50 achieves the best overall performance compared with 5 classic convolutional neural networks (CNNs) models. The proposed model reached an accuracy of 93.18% along with higher precision, recall, specificity, and F1 score. The improved model achieves an AUC value of 0.9670 and 0.9717 in the P-R and ROC curve, respectively. Then, the evaluation based on Grad-CAM further proved that the improved model adaptively focuses on lesion-relevant regions while suppressing irrelevant background information, thereby validating its enhanced feature learning capability from a deep representation perspective. These findings demonstrate that the proposed approach provides a more effective and efficient solution for computer-aided skin cancer diagnosis.
Multi-Modal Oral Cancer Detection Using Weighted Ensemble Convolutional Neural Networks
Venue: arXiv cs.CV 2025
Year: 2025

Aims Late diagnosis of Oral Squamous Cell Carcinoma (OSCC) contributes significantly to its high global mortality rate, with over 50\% of cases detected at advanced stages and a 5-year survival rate below 50\% according to WHO statistics. This study aims to improve early detection of OSCC by developing a multimodal deep learning framework that integrates clinical, radiological, and histopathological images using a weighted ensemble of DenseNet-121 convolutional neural networks (CNNs). Material and Methods A retrospective study was conducted using publicly available datasets representing three distinct medical imaging modalities. Each modality-specific dataset was used to train a DenseNet-121 CNN via transfer learning. Augmentation and modality-specific preprocessing were applied to increase robustness. Predictions were fused using a validation-weighted ensemble strategy. Evaluation was performed using accuracy, precision, recall, F1-score. Results High validation accuracy was achieved for radiological (100\%) and histopathological (95.12\%) modalities, with clinical images performing lower (63.10\%) due to visual heterogeneity. The ensemble model demonstrated improved diagnostic robustness with an overall accuracy of 84.58\% on a multimodal validation dataset of 55 samples. Conclusion The multimodal ensemble framework bridges gaps in the current diagnostic workflow by offering a non-invasive, AI-assisted triage tool that enhances early identification of high-risk lesions. It supports clinicians in decision-making, aligning with global oncology guidelines to reduce diagnostic delays and improve patient outcomes.
Exploring Instruction Data Quality for Explainable Image Quality Assessment
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, with the rapid development of powerful multimodal large language models (MLLMs), explainable image quality assessment (IQA) has gradually become popular, aiming at providing quality-related descriptions and answers of images. To achieve this goal, recent methods seek to construct a large-scale instruction tuning dataset to empower the MLLM with quality perception ability following the well-known scaling law. However, a large amount of instruction tuning data may cause substantial computational costs and redundant data, which in turn will cause harm to the performance of the model. To cope with this problem, in this paper, we challenge the scaling law and systematically investigate the role of data quality of the instruction tuning dataset for explainable IQA. Using a powerful pre-trained MLLM, we first investigate the changes in model performance after fine-tuning with different sizes of instruction tuning data. We find that selecting a subset of the data set randomly using an appropriate ratio can even lead to better results than training with the entire instruction tuning dataset, demonstrating the redundancy of current explainable IQA instruction tuning data. Beyond randomly sampling a subset, we propose a clustering-based data selection framework with three stages: clustering feature extraction, cluster quota allocation, and cluster sampling strategy. Then we systematically analyze the choices of each stage and propose a simple but efficient data selection method IQA-Select for explainable IQA. The experimental results demonstrate that IQA-Select can achieve 102.1% and 103.7% performance of full fine-tuning using only 10% selected data in Q-Bench and AesBench respectively, significantly reducing computational costs while achieving better performance.
Rethinking Services in the Quantum Age: The SOQ Paradigm
Venue: arXiv cs.SE 2025
Year: 2025

Quantum computing is rapidly progressing from theoretical promise to practical implementation, offering significant computational advantages for tasks in optimization, simulation, cryptography, and machine learning. However, its integration into real-world software systems remains constrained by hardware fragility, platform heterogeneity, and the absence of robust software engineering practices. This paper introduces Service-Oriented Quantum (SOQ), a novel paradigm that reimagines quantum software systems through the lens of classical service-oriented computing. Unlike prior approaches such as Quantum Service-Oriented Computing (QSOC), which treat quantum capabilities as auxiliary components within classical systems, SOQ positions quantum services as autonomous, composable, and interoperable entities. We define the foundational principles of SOQ, propose a layered technology stack to support its realization, and identify the key research and engineering challenges that must be addressed, including interoperability, hybridity, pricing models, service abstractions, and workforce development. This approach is of vital importance for the advancement of quantum technology because it enables the scalable, modular, and interoperable integration of quantum computing into real-world software systems independently and without relying on a dedicated classical environment to manage quantum processing.
Kantian-Utilitarian XAI: Meta-Explained
Venue: arXiv cs.AI 2025
Year: 2025

We present a gamified explainable AI (XAI) system for ethically aware consumer decision-making in the coffee domain. Each session comprises six rounds with three options per round. Two symbolic engines provide real-time reasons: a Kantian module flags rule violations (e.g., child labor, deforestation risk without shade certification, opaque supply chains, unsafe decaf), and a utilitarian module scores options via multi-criteria aggregation over normalized attributes (price, carbon, water, transparency, farmer income share, taste/freshness, packaging, convenience). A meta-explainer with a regret bound (0.2) highlights Kantian--utilitarian (mis)alignment and switches to a deontically clean, near-parity option when welfare loss is small. We release a structured configuration (attribute schema, certification map, weights, rule set), a policy trace for auditability, and an interactive UI.
Bridge Thinking and Acting: Unleashing Physical Potential of VLM with Generalizable Action Expert
Venue: arXiv cs.CV 2025
Year: 2025

Although Vision-Language Models (VLM) have demonstrated impressive planning and reasoning capabilities, translating these abilities into the physical world introduces significant challenges. Conventional Vision-Language-Action (VLA) models, which integrate reasoning and action into a monolithic architecture, generalize poorly because they are constrained by scarce, narrow-domain data. While recent dual-system approaches attempt to decouple "thinking" from "acting", they are often constrained by semantic ambiguities within the action module. This ambiguity makes large-scale, cross-task training infeasible. Consequently, these systems typically necessitate fine-tuning on newly collected data when deployed to novel environments, and the cooperation mechanism between the two systems remains ill-defined. To address these limitations, we introduce, for the first time, a framework centered around a generalizable action expert. Our approach utilizes sparse 3D trajectories as an intermediate representation, effectively bridging the high-level planning capabilities of the VLM with the low-level physical action module. During the planning phase, the VLM is only required to generate coarse 3D waypoints. These waypoints are then processed by our generalizable action expert, which refines them into dense, executable action sequences by sampling real-time point cloud observations of the environment. To promote training efficiency and robust generalization, we introduce a novel "Action Pre-training, Pointcloud Fine-tuning" paradigm. Our method combines the broad generalization capabilities of VLMs in visual understanding and planning with the fine-grained, action-level generalization of action expert.
Generating Human Motion Videos using a Cascaded Text-to-Video Framework
Venue: arXiv cs.CV 2025
Year: 2025

Human video generation is becoming an increasingly important task with broad applications in graphics, entertainment, and embodied AI. Despite the rapid progress of video diffusion models (VDMs), their use for general-purpose human video generation remains underexplored, with most works constrained to image-to-video setups or narrow domains like dance videos. In this work, we propose CAMEO, a cascaded framework for general human motion video generation. It seamlessly bridges Text-to-Motion (T2M) models and conditional VDMs, mitigating suboptimal factors that may arise in this process across both training and inference through carefully designed components. Specifically, we analyze and prepare both textual prompts and visual conditions to effectively train the VDM, ensuring robust alignment between motion descriptions, conditioning signals, and the generated videos. Furthermore, we introduce a camera-aware conditioning module that connects the two stages, automatically selecting viewpoints aligned with the input text to enhance coherence and reduce manual intervention. We demonstrate the effectiveness of our approach on both the MovieGen benchmark and a newly introduced benchmark tailored to the T2M-VDM combination, while highlighting its versatility across diverse use cases.
OpenFLAME: Federated Visual Positioning System to Enable Large-Scale Augmented Reality Applications
Venue: arXiv cs.CV 2025
Year: 2025

World-scale augmented reality (AR) applications need a ubiquitous 6DoF localization backend to anchor content to the real world consistently across devices. Large organizations such as Google and Niantic are 3D scanning outdoor public spaces in order to build their own Visual Positioning Systems (VPS). These centralized VPS solutions fail to meet the needs of many future AR applications -- they do not cover private indoor spaces because of privacy concerns, regulations, and the labor bottleneck of updating and maintaining 3D scans. In this paper, we present OpenFLAME, a federated VPS backend that allows independent organizations to 3D scan and maintain a separate VPS service for their own spaces. This enables access control of indoor 3D scans, distributed maintenance of the VPS backend, and encourages larger coverage. Sharding of VPS services introduces several unique challenges -- coherency of localization results across spaces, quality control of VPS services, selection of the right VPS service for a location, and many others. We introduce the concept of federated image-based localization and provide reference solutions for managing and merging data across maps without sharing private data.
Unreliability in Practical Subclasses of Communicating Systems
Venue: arXiv cs.LO 2025
Year: 2025

Systems of communicating automata are prominent models for peer-to-peer message-passing over unbounded channels, but in the general scenario, most verification properties are undecidable. To address this issue, two decidable subclasses, Realisable with Synchronous Communication (RSC) and k-Multiparty Compatibility} (k-MC), were proposed in the literature, with corresponding verification tools developed and applied in practice. Unfortunately, both RSC and k-MC are not resilient under failures: (1) their decidability relies on the assumption of perfect channels and (2) most standard protocols do not satisfy RSC or k-MC under failures. To address these limitations, this paper studies the resilience of RSC and k-MC under two distinct failure models: interference and crash-stop failures. For interference, we relax the conditions of RSC and k-MC and prove that the inclusions of these relaxed properties remain decidable under interference, preserving their known complexity bounds. We then propose a novel crash-handling communicating system that captures wider behaviours than existing multiparty session types (MPST) with crash-stop failures. We study a translation of MPST with crash-stop failures into this system integrating RSC and k-MC properties, and establish their decidability results. Finally, by verifying representative protocols from the literature using RSC and k-MC tools extended to interferences, we evaluate the relaxed systems and demonstrate their resilience.
FinCall-Surprise: A Large Scale Multi-modal Benchmark for Earning Surprise Prediction
Venue: arXiv cs.MM 2025
Year: 2025

Predicting corporate earnings surprises is a profitable yet challenging task, as accurate forecasts can inform significant investment decisions. However, progress in this domain has been constrained by a reliance on expensive, proprietary, and text-only data, limiting the development of advanced models. To address this gap, we introduce \textbf{FinCall-Surprise} (Financial Conference Call for Earning Surprise Prediction), the first large-scale, open-source, and multi-modal dataset for earnings surprise prediction. Comprising 2,688 unique corporate conference calls from 2019 to 2021, our dataset features word-to-word conference call textual transcripts, full audio recordings, and corresponding presentation slides. We establish a comprehensive benchmark by evaluating 26 state-of-the-art unimodal and multi-modal LLMs. Our findings reveal that (1) while many models achieve high accuracy, this performance is often an illusion caused by significant class imbalance in the real-world data. (2) Some specialized financial models demonstrate unexpected weaknesses in instruction-following and language generation. (3) Although incorporating audio and visual modalities provides some performance gains, current models still struggle to leverage these signals effectively. These results highlight critical limitations in the financial reasoning capabilities of existing LLMs and establish a challenging new baseline for future research.
Towards Carbon-Aware Container Orchestration: Predicting Workload Energy Consumption with Federated Learning
Venue: arXiv cs.DC 2025
Year: 2025

The growing reliance on large-scale data centers to run resource-intensive workloads has significantly increased the global carbon footprint, underscoring the need for sustainable computing solutions. While container orchestration platforms like Kubernetes help optimize workload scheduling to reduce carbon emissions, existing methods often depend on centralized machine learning models that raise privacy concerns and struggle to generalize across diverse environments. In this paper, we propose a federated learning approach for energy consumption prediction that preserves data privacy by keeping sensitive operational data within individual enterprises. By extending the Kubernetes Efficient Power Level Exporter (Kepler), our framework trains XGBoost models collaboratively across distributed clients using Flower's FedXgbBagging aggregation using a bagging strategy, eliminating the need for centralized data sharing. Experimental results on the SPECPower benchmark dataset show that our FL-based approach achieves 11.7 percent lower Mean Absolute Error compared to a centralized baseline. This work addresses the unresolved trade-off between data privacy and energy prediction efficiency in prior systems such as Kepler and CASPER and offers enterprises a viable pathway toward sustainable cloud computing without compromising operational privacy.
Beyond Softmax: A New Perspective on Gradient Bandits
Venue: arXiv cs.LG 2025
Year: 2025

We establish a link between a class of discrete choice models and the theory of online learning and multi-armed bandits. Our contributions are: (i) sublinear regret bounds for a broad algorithmic family, encompassing Exp3 as a special case; (ii) a new class of adversarial bandit algorithms derived from generalized nested logit models \citep{wen:2001}; and (iii) \textcolor{black}{we introduce a novel class of generalized gradient bandit algorithms that extends beyond the widely used softmax formulation. By relaxing the restrictive independence assumptions inherent in softmax, our framework accommodates correlated learning dynamics across actions, thereby broadening the applicability of gradient bandit methods.} Overall, the proposed algorithms combine flexible model specification with computational efficiency via closed-form sampling probabilities. Numerical experiments in stochastic bandit settings demonstrate their practical effectiveness.
ICEPool: Enhancing Graph Pooling Networks with Inter-cluster Connectivity
Venue: arXiv cs.LG 2025
Year: 2025

Hierarchical Pooling Models have demonstrated strong performance in classifying graph-structured data. While numerous innovative methods have been proposed to design cluster assignments and coarsening strategies, the relationships between clusters are often overlooked. In this paper, we introduce Inter-cluster Connectivity Enhancement Pooling (ICEPool), a novel hierarchical pooling framework designed to enhance model's understanding of inter-cluster connectivity and ability of preserving the structural integrity in the original graph. ICEPool is compatible with a wide range of pooling-based GNN models. The deployment of ICEPool as an enhancement to existing models effectively combines the strengths of the original model with ICEPool's capability to emphasize the integration of inter-cluster connectivity, resulting in a more comprehensive and robust graph-level representation. Moreover, we make theoretical analysis to ICEPool's ability of graph reconstruction to demonstrate its effectiveness in learning inter-cluster relationship that is overlooked by conventional models. Finally, the experimental results show the compatibility of ICEPool with wide varieties of models and its potential to boost the performance of existing graph neural network architectures.
What Shapes a Creative Machine Mind? Comprehensively Benchmarking Creativity in Foundation Models
Venue: arXiv cs.AI 2025
Year: 2025

The meteoric rise of foundation models (FMs) has expanded their capabilities far beyond conventional tasks. Creativity, long regarded as a hallmark of human intelligence and a driver of innovation, is now increasingly recognized as a critical dimension of machine intelligence in the era of generative FMs, complementing traditional measures of accuracy. However, existing evaluation frameworks for creativity remain fragmented, relying on ad hoc metrics not firmly grounded in established theories. To address this gap, we introduce C^2-Eval, a holistic benchmark for unified assessment of creativity in FMs. C^2-Eval distinguishes between two complementary forms of creativity: convergent creativity, where tasks admit constrained solutions (e.g., code generation), and divergent creativity, where tasks are open-ended (e.g., storytelling). It evaluates both dimensions using fine-grained criteria derived from social-science theory, focusing on Usefulness, Originality, and Surprise (U-O-S). Through extensive experiments on leading proprietary and open-source models, we analyze trade-offs in their creative capabilities. Our results highlight both the strengths and challenges of current FMs in pursuing a creative machine mind, showing that C^2-Eval is an effective lens for examining the evolving landscape of creative AI.
Video-in-the-Loop: Span-Grounded Long Video QA with Interleaved Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

We present \emph{Video-in-the-Loop} (ViTL), a two-stage long-video QA framework that preserves a fixed token budget by first \emph{localizing} question-relevant interval(s) with a low-fps skim and then \emph{answering} via span-aware reallocation of visual tokens at higher effective frame rate, emitting an interleaved output with both spans and the final option for direct attribution. We also introduce \dataname{}, which converts description based event graphs into \emph{span-grounded} multiple-choice QA by pairing each question with \emph{ground-truth} time span(s) and related reasoning. ViTL is trained end-to-end with an interleaved group-relative objective that couples temporal IoU for localization with answer correctness, allowing credit to flow from answers back to spans without increasing compute. Under fixed token budgets, ViTL attains up to 8.6% with 50% less frame input on long-video QA and temporal grounding (e.g., Charades-STA, ActivityNet-Captions) and ablations show that span-aware token reallocation consistently surpasses uniform sampling. Together, \dataname{} and ViTL provide an interpretable, compute-efficient recipe for scalable long-video QA.