OpenRaiser/Intern-Atlas huggingface dataset:
CUBIC: Concept Embeddings for Unsupervised Bias Identification using VLMs
Venue: arXiv cs.CV 2025
Year: 2025

Deep vision models often rely on biases learned from spurious correlations in datasets. To identify these biases, methods that interpret high-level, human-understandable concepts are more effective than those relying primarily on low-level features like heatmaps. A major challenge for these concept-based methods is the lack of image annotations indicating potentially bias-inducing concepts, since creating such annotations requires detailed labeling for each dataset and concept, which is highly labor-intensive. We present CUBIC (Concept embeddings for Unsupervised Bias IdentifiCation), a novel method that automatically discovers interpretable concepts that may bias classifier behavior. Unlike existing approaches, CUBIC does not rely on predefined bias candidates or examples of model failures tied to specific biases, as such information is not always available. Instead, it leverages image-text latent space and linear classifier probes to examine how the latent representation of a superclass label$\unicode{x2014}$shared by all instances in the dataset$\unicode{x2014}$is influenced by the presence of a given concept. By measuring these shifts against the normal vector to the classifier's decision boundary, CUBIC identifies concepts that significantly influence model predictions. Our experiments demonstrate that CUBIC effectively uncovers previously unknown biases using Vision-Language Models (VLMs) without requiring the samples in the dataset where the classifier underperforms or prior knowledge of potential biases.
Fault Diagnosis across Heterogeneous Domains via Self-Adaptive Temporal-Spatial Attention and Sample Generation
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning methods have shown promising performance in fault diagnosis for multimode process. Most existing studies assume that the collected health state categories from different operating modes are identical. However, in real industrial scenarios, these categories typically exhibit only partial overlap. The incompleteness of the available data and the large distributional differences between the operating modes pose a significant challenge to existing fault diagnosis methods. To address this problem, a novel fault diagnosis model named self-adaptive temporal-spatial attention network (TSA-SAN) is proposed. First, inter-mode mappings are constructed using healthy category data to generate multimode samples. To enrich the diversity of the fault data, interpolation is performed between healthy and fault samples. Subsequently, the fault diagnosis model is trained using real and generated data. The self-adaptive instance normalization is established to suppress irrelevant information while retaining essential statistical features for diagnosis. In addition, a temporal-spatial attention mechanism is constructed to focus on the key features, thus enhancing the generalization ability of the model. The extensive experiments demonstrate that the proposed model significantly outperforms the state-of-the-art methods. The code will be available on Github at https://github.com/GuangqiangLi/TSA-SAN.
MAVOS-DD: Multilingual Audio-Video Open-Set Deepfake Detection Benchmark
Venue: arXiv cs.CV 2025
Year: 2025

We present the first large-scale open-set benchmark for multilingual audio-video deepfake detection. Our dataset comprises over 250 hours of real and fake videos across eight languages, with 60% of data being generated. For each language, the fake videos are generated with seven distinct deepfake generation models, selected based on the quality of the generated content. We organize the training, validation and test splits such that only a subset of the chosen generative models and languages are available during training, thus creating several challenging open-set evaluation setups. We perform experiments with various pre-trained and fine-tuned deepfake detectors proposed in recent literature. Our results show that state-of-the-art detectors are not currently able to maintain their performance levels when tested in our open-set scenarios. We publicly release our data and code at: https://huggingface.co/datasets/unibuc-cs/MAVOS-DD.
Towards Robust Spiking Neural Networks:Mitigating Heterogeneous Training Vulnerability via Dominant Eigencomponent Projection
Venue: arXiv cs.LG 2025
Year: 2025

Spiking Neural Networks (SNNs) process information via discrete spikes, enabling them to operate at remarkably low energy levels. However, our experimental observations reveal a striking vulnerability when SNNs are trained using the mainstream method--direct encoding combined with backpropagation through time (BPTT): even a single backward pass on data drawn from a slightly different distribution can lead to catastrophic network collapse. Our theoretical analysis attributes this vulnerability to the repeated inputs inherent in direct encoding and the gradient accumulation characteristic of BPTT, which together produce an exceptional large Hessian spectral radius. To address this challenge, we develop a hyperparameter-free method called Dominant Eigencomponent Projection (DEP). By orthogonally projecting gradients to precisely remove their dominant components, DEP effectively reduces the Hessian spectral radius, thereby preventing SNNs from settling into sharp minima. Extensive experiments demonstrate that DEP not only mitigates the vulnerability of SNNs to heterogeneous data poisoning, but also significantly enhances overall robustness compared to key baselines, providing strong support for safer and more reliable SNN deployment.
Open-Source Multi-Viewpoint Surgical Telerobotics
Venue: arXiv cs.RO 2025
Year: 2025

As robots for minimally invasive surgery (MIS) gradually become more accessible and modular, we believe there is a great opportunity to rethink and expand the visualization and control paradigms that have characterized surgical teleoperation since its inception. We conjecture that introducing one or more additional adjustable viewpoints in the abdominal cavity would not only unlock novel visualization and collaboration strategies for surgeons but also substantially boost the robustness of machine perception toward shared autonomy. Immediate advantages include controlling a second viewpoint and teleoperating surgical tools from a different perspective, which would allow collaborating surgeons to adjust their views independently and still maneuver their robotic instruments intuitively. Furthermore, we believe that capturing synchronized multi-view 3D measurements of the patient's anatomy would unlock advanced scene representations. Accurate real-time intraoperative 3D perception will allow algorithmic assistants to directly control one or more robotic instruments and/or robotic cameras. Toward these goals, we are building a synchronized multi-viewpoint, multi-sensor robotic surgery system by integrating high-performance vision components and upgrading the da Vinci Research Kit control logic. This short paper reports a functional summary of our setup and elaborates on its potential impacts in research and future clinical practice. By fully open-sourcing our system, we will enable the research community to reproduce our setup, improve it, and develop powerful algorithms, effectively boosting clinical translation of cutting-edge research.
Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training
Venue: arXiv cs.LG 2025
Year: 2025

Ever-growing scale of large language models (LLMs) is pushing for improved efficiency, favoring fully quantized training (FQT) over BF16. While FQT accelerates training, it faces consistency challenges and requires searching over an exponential number of cases, each needing over 200B tokens to ensure stability. Pseudo-quantization training (PQT) addresses the issues of FQT, although it is not well-studied. We explore the practical implications of PQT in detail and propose a noise distribution $R$ that is floating-point (FP)-friendly, with ideal properties including stochastic precision annealing. As a result, the proposed method serves as an effective theoretical foundation for low-precision FP parameters through PQT, utilizing efficient fake quantization via an addition and subsequent FP casting. We demonstrate that Gaussian weight sampling is (1) scalable: supports low-precision FP parameters down to FP6 and high-precision noise up to 9-bit with BF16 operator. The proposed method is (2) efficient: incurring computational overhead as low as 1.40\% on the A100 GPU in terms of Llama2 training tokens per second, and requiring 2 bytes per parameter in GPU memory. We demonstrate that PQT with Gaussian weight sampling is (3) stable: closely following or even surpassing performance of the BF16 baseline while pre-training GPT2 and Llama2 models with up to 1B parameters and 300B tokens.
Sample Efficient Reinforcement Learning via Large Vision Language Model Distillation
Venue: arXiv cs.LG 2025
Year: 2025

Recent research highlights the potential of multimodal foundation models in tackling complex decision-making challenges. However, their large parameters make real-world deployment resource-intensive and often impractical for constrained systems. Reinforcement learning (RL) shows promise for task-specific agents but suffers from high sample complexity, limiting practical applications. To address these challenges, we introduce LVLM to Policy (LVLM2P), a novel framework that distills knowledge from large vision-language models (LVLM) into more efficient RL agents. Our approach leverages the LVLM as a teacher, providing instructional actions based on trajectories collected by the RL agent, which helps reduce less meaningful exploration in the early stages of learning, thereby significantly accelerating the agent's learning progress. Additionally, by leveraging the LVLM to suggest actions directly from visual observations, we eliminate the need for manual textual descriptors of the environment, enhancing applicability across diverse tasks. Experiments show that LVLM2P significantly enhances the sample efficiency of baseline RL algorithms.
AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Event cameras, which capture brightness changes with high temporal resolution, inherently generate a significant amount of redundant and noisy data beyond essential object structures. The primary challenge in event-based object recognition lies in effectively removing this noise without losing critical spatial-temporal information. To address this, we propose an Adaptive Graph-based Noisy Data Removal framework for Event-based Object Recognition. Specifically, our approach integrates adaptive event segmentation based on normalized density analysis, a multifactorial edge-weighting mechanism, and adaptive graph-based denoising strategies. These innovations significantly enhance the integration of spatiotemporal information, effectively filtering noise while preserving critical structural features for robust recognition. Experimental evaluations on four challenging datasets demonstrate that our method achieves superior recognition accuracies of 83.77%, 76.79%, 99.30%, and 96.89%, surpassing existing graph-based methods by up to 8.79%, and improving noise reduction performance by up to 19.57%, with an additional accuracy gain of 6.26% compared to traditional Euclidean-based techniques.
Lightweight LIF-only SNN accelerator using differential time encoding
Venue: arXiv cs.NE 2025
Year: 2025

Spiking Neural Networks (SNNs) offer a promising solution to the problem of increasing computational and energy requirements for modern Machine Learning (ML) applications. Due to their unique data representation choice of using spikes and spike trains, they mostly rely on additions and thresholding operations to achieve results approaching state-of-the-art (SOTA) Artificial Neural Networks (ANNs). This advantage is hindered by the fact that their temporal characteristic does not map well to already existing accelerator hardware like GPUs. Therefore, this work will introduce a hardware accelerator architecture capable of computing feedforward LIF-only SNNs, as well as an accompanying encoding method to efficiently encode already existing data into spike trains. Together, this leads to a design capable of >99% accuracy on the MNIST dataset, with ~0.29ms inference times on a Xilinx Ultrascale+ FPGA, as well as ~0.17ms on a custom ASIC using the open-source predictive 7nm ASAP7 PDK. Furthermore, this work will showcase the advantages of the previously presented differential time encoding for spikes, as well as provide proof that merging spikes from different synapses given in differential time encoding can be done efficiently in hardware.
A Step towards Interpretable Multimodal AI Models with MultiFIX
Venue: arXiv cs.NE 2025
Year: 2025

Real-world problems are often dependent on multiple data modalities, making multimodal fusion essential for leveraging diverse information sources. In high-stakes domains, such as in healthcare, understanding how each modality contributes to the prediction is critical to ensure trustworthy and interpretable AI models. We present MultiFIX, an interpretability-driven multimodal data fusion pipeline that explicitly engineers distinct features from different modalities and combines them to make the final prediction. Initially, only deep learning components are used to train a model from data. The black-box (deep learning) components are subsequently either explained using post-hoc methods such as Grad-CAM for images or fully replaced by interpretable blocks, namely symbolic expressions for tabular data, resulting in an explainable model. We study the use of MultiFIX using several training strategies for feature extraction and predictive modeling. Besides highlighting strengths and weaknesses of MultiFIX, experiments on a variety of synthetic datasets with varying degrees of interaction between modalities demonstrate that MultiFIX can generate multimodal models that can be used to accurately explain both the extracted features and their integration without compromising predictive performance.
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics
Venue: arXiv cs.MA 2025
Year: 2025

Artificial intelligence (AI) is reshaping strategic planning, with Multi-Agent Reinforcement Learning (MARL) enabling coordination among autonomous agents in complex scenarios. However, its practical deployment in sensitive military contexts is constrained by the lack of explainability, which is an essential factor for trust, safety, and alignment with human strategies. This work reviews and assesses current advances in explainability methods for MARL with a focus on simulated air combat scenarios. We proceed by adapting various explainability techniques to different aerial combat scenarios to gain explanatory insights about the model behavior. By linking AI-generated tactics with human-understandable reasoning, we emphasize the need for transparency to ensure reliable deployment and meaningful human-machine interaction. By illuminating the crucial importance of explainability in advancing MARL for operational defense, our work supports not only strategic planning but also the training of military personnel with insightful and comprehensible analyses.
Dynamic Base model Shift for Delta Compression
Venue: arXiv cs.CV 2025
Year: 2025

Transformer-based models with the pretrain-finetune paradigm bring about significant progress, along with the heavy storage and deployment costs of finetuned models on multiple tasks. Delta compression attempts to lower the costs by reducing the redundancy of delta parameters (i.e., the difference between the finetuned and pre-trained model weights) through pruning or quantization. However, existing methods by default employ the pretrained model as the base model and compress the delta parameters for every task, which may causes significant performance degradation, especially when the compression rate is extremely high. To tackle this issue, we investigate the impact of different base models on the performance of delta compression and find that the pre-trained base model can hardly be optimal. To this end, we propose Dynamic Base Model Shift (DBMS), which dynamically adapts the base model to the target task before performing delta compression. Specifically, we adjust two parameters, which respectively determine the magnitude of the base model shift and the overall scale of delta compression, to boost the compression performance on each task. Through low-cost learning of these two parameters, our DBMS can maintain most of the finetuned model's performance even under an extremely high compression ratio setting, significantly surpassing existing methods. Moreover, our DBMS is orthogonal and can be integrated with a variety of other methods, and it has been evaluated across different types of models including language, vision transformer, and multi-modal models.
Phare: A Safety Probe for Large Language Models
Venue: arXiv cs.CY 2025
Year: 2025

Ensuring the safety of large language models (LLMs) is critical for responsible deployment, yet existing evaluations often prioritize performance over identifying failure modes. We introduce Phare, a multilingual diagnostic framework to probe and evaluate LLM behavior across three critical dimensions: hallucination and reliability, social biases, and harmful content generation. Our evaluation of 17 state-of-the-art LLMs reveals patterns of systematic vulnerabilities across all safety dimensions, including sycophancy, prompt sensitivity, and stereotype reproduction. By highlighting these specific failure modes rather than simply ranking models, Phare provides researchers and practitioners with actionable insights to build more robust, aligned, and trustworthy language systems.
MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory
Venue: arXiv cs.CV 2025
Year: 2025

This paper introduces MutualNeRF, a framework enhancing Neural Radiance Field (NeRF) performance under limited samples using Mutual Information Theory. While NeRF excels in 3D scene synthesis, challenges arise with limited data and existing methods that aim to introduce prior knowledge lack theoretical support in a unified framework. We introduce a simple but theoretically robust concept, Mutual Information, as a metric to uniformly measure the correlation between images, considering both macro (semantic) and micro (pixel) levels. For sparse view sampling, we strategically select additional viewpoints containing more non-overlapping scene information by minimizing mutual information without knowing ground truth images beforehand. Our framework employs a greedy algorithm, offering a near-optimal solution. For few-shot view synthesis, we maximize the mutual information between inferred images and ground truth, expecting inferred images to gain more relevant information from known images. This is achieved by incorporating efficient, plug-and-play regularization terms. Experiments under limited samples show consistent improvement over state-of-the-art baselines in different settings, affirming the efficacy of our framework.
Finding Counterfactual Evidences for Node Classification
Venue: arXiv cs.LG 2025
Year: 2025

Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many real-world application domains where conducting randomized controlled trials is impractical, one has to rely on available observational (factual) data to detect counterfactuals. In this paper, we introduce and tackle the problem of searching for counterfactual evidences for the GNN-based node classification task. A counterfactual evidence is a pair of nodes such that, regardless they exhibit great similarity both in the features and in their neighborhood subgraph structures, they are classified differently by the GNN. We develop effective and efficient search algorithms and a novel indexing solution that leverages both node features and structural information to identify counterfactual evidences, and generalizes beyond any specific GNN. Through various downstream applications, we demonstrate the potential of counterfactual evidences to enhance fairness and accuracy of GNNs.
Can AI automatically analyze public opinion? A LLM agents-based agentic pipeline for timely public opinion analysis
Venue: arXiv cs.CY 2025
Year: 2025

This study proposes and implements the first LLM agents based agentic pipeline for multi task public opinion analysis. Unlike traditional methods, it offers an end-to-end, fully automated analytical workflow without requiring domain specific training data, manual annotation, or local deployment. The pipeline integrates advanced LLM capabilities into a low-cost, user-friendly framework suitable for resource constrained environments. It enables timely, integrated public opinion analysis through a single natural language query, making it accessible to non-expert users. To validate its effectiveness, the pipeline was applied to a real world case study of the 2025 U.S. China tariff dispute, where it analyzed 1,572 Weibo posts and generated a structured, multi part analytical report. The results demonstrate some relationships between public opinion and governmental decision-making. These contributions represent a novel advancement in applying generative AI to public governance, bridging the gap between technical sophistication and practical usability in public opinion monitoring.
MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection
Venue: arXiv cs.NE 2025
Year: 2025

Modern neural networks often activate all neurons for every input, leading to unnecessary computation and inefficiency. We introduce Matrix-Interpolated Dropout Layer (MID-L), a novel module that dynamically selects and activates only the most informative neurons by interpolating between two transformation paths via a learned, input-dependent gating vector. Unlike conventional dropout or static sparsity methods, MID-L employs a differentiable Top-k masking strategy, enabling per-input adaptive computation while maintaining end-to-end differentiability. MID-L is model-agnostic and integrates seamlessly into existing architectures. Extensive experiments on six benchmarks, including MNIST, CIFAR-10, CIFAR-100, SVHN, UCI Adult, and IMDB, show that MID-L achieves up to average 55\% reduction in active neurons, 1.7$\times$ FLOPs savings, and maintains or exceeds baseline accuracy. We further validate the informativeness and selectivity of the learned neurons via Sliced Mutual Information (SMI) and observe improved robustness under overfitting and noisy data conditions. Additionally, MID-L demonstrates favorable inference latency and memory usage profiles, making it suitable for both research exploration and deployment on compute-constrained systems. These results position MID-L as a general-purpose, plug-and-play dynamic computation layer, bridging the gap between dropout regularization and efficient inference.
Face Consistency Benchmark for GenAI Video
Venue: arXiv cs.CV 2025
Year: 2025

Video generation driven by artificial intelligence has advanced significantly, enabling the creation of dynamic and realistic content. However, maintaining character consistency across video sequences remains a major challenge, with current models struggling to ensure coherence in appearance and attributes. This paper introduces the Face Consistency Benchmark (FCB), a framework for evaluating and comparing the consistency of characters in AI-generated videos. By providing standardized metrics, the benchmark highlights gaps in existing solutions and promotes the development of more reliable approaches. This work represents a crucial step toward improving character consistency in AI video generation technologies.