OpenRaiser/Intern-Atlas huggingface dataset:
Harmony: A Unified Framework for Modality Incremental Learning
Venue: arXiv cs.LG 2025
Year: 2025

Incremental learning aims to enable models to continuously acquire knowledge from evolving data streams while preserving previously learned capabilities. While current research predominantly focuses on unimodal incremental learning and multimodal incremental learning where the modalities are consistent, real-world scenarios often present data from entirely new modalities, posing additional challenges. This paper investigates the feasibility of developing a unified model capable of incremental learning across continuously evolving modal sequences. To this end, we introduce a novel paradigm called Modality Incremental Learning (MIL), where each learning stage involves data from distinct modalities. To address this task, we propose a novel framework named Harmony, designed to achieve modal alignment and knowledge retention, enabling the model to reduce the modal discrepancy and learn from a sequence of distinct modalities, ultimately completing tasks across multiple modalities within a unified framework. Our approach introduces the adaptive compatible feature modulation and cumulative modal bridging. Through constructing historical modal features and performing modal knowledge accumulation and alignment, the proposed components collaboratively bridge modal differences and maintain knowledge retention, even with solely unimodal data available at each learning phase.These components work in concert to establish effective modality connections and maintain knowledge retention, even when only unimodal data is available at each learning stage. Extensive experiments on the MIL task demonstrate that our proposed method significantly outperforms existing incremental learning methods, validating its effectiveness in MIL scenarios.
SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classification
Venue: arXiv cs.CV 2025
Year: 2025

Brain-computer interfaces (BCI) in electroencephalography (EEG)-based motor imagery classification offer promising solutions in neurorehabilitation and assistive technologies by enabling communication between the brain and external devices. However, the non-stationary nature of EEG signals and significant inter-subject variability cause substantial challenges for developing robust cross-subject classification models. This paper introduces a novel Spatial-Spectral-Temporal Attention Fusion (SSTAF) Transformer specifically designed for upper-limb motor imagery classification. Our architecture consists of a spectral transformer and a spatial transformer, followed by a transformer block and a classifier network. Each module is integrated with attention mechanisms that dynamically attend to the most discriminative patterns across multiple domains, such as spectral frequencies, spatial electrode locations, and temporal dynamics. The short-time Fourier transform is incorporated to extract features in the time-frequency domain to make it easier for the model to obtain a better feature distinction. We evaluated our SSTAF Transformer model on two publicly available datasets, the EEGMMIDB dataset, and BCI Competition IV-2a. SSTAF Transformer achieves an accuracy of 76.83% and 68.30% in the data sets, respectively, outperforms traditional CNN-based architectures and a few existing transformer-based approaches.
Non-Uniform Class-Wise Coreset Selection for Vision Model Fine-tuning
Venue: arXiv cs.LG 2025
Year: 2025

Coreset selection aims to identify a small yet highly informative subset of data, thereby enabling more efficient model training while reducing storage overhead. Recently, this capability has been leveraged to tackle the challenges of fine-tuning large foundation models, offering a direct pathway to their efficient and practical deployment. However, most existing methods are class-agnostic, causing them to overlook significant difficulty variations among classes. This leads them to disproportionately prune samples from either overly easy or hard classes, resulting in a suboptimal allocation of the data budget that ultimately degrades the final coreset performance. To address this limitation, we propose Non-Uniform Class-Wise Coreset Selection (NUCS), a novel framework that both integrates class-level and sample-level difficulty. We propose a robust metric for global class difficulty, quantified as the winsorized average of per-sample difficulty scores. Guided by this metric, our method performs a theoretically-grounded, non-uniform allocation of data selection budgets inter-class, while adaptively selecting samples intra-class with optimal difficulty ranges. Extensive experiments on a wide range of visual classification tasks demonstrate that NUCS consistently outperforms state-of-the-art methods across 10 diverse datasets and pre-trained models, achieving both superior accuracy and computational efficiency, highlighting the promise of non-uniform class-wise selection strategy for advancing the efficient fine-tuning of large foundation models.
Acoustic to Articulatory Inversion of Speech; Data Driven Approaches, Challenges, Applications, and Future Scope
Venue: arXiv cs.SD 2025
Year: 2025

This review is focused on the data-driven approaches applied in different applications of Acoustic-to-Articulatory Inversion (AAI) of speech. This review paper considered the relevant works published in the last ten years (2011-2021). The selection criteria includes (a) type of AAI - Speaker Dependent and Speaker Independent AAI, (b) objectives of the work - Articulatory approximation, Articulatory Feature space selection and Automatic Speech Recognition (ASR), explore the correlation between acoustic and articulatory features, and framework for Computer-assisted language training, (c) Corpus - Simultaneously recorded speech (wav) and medical imaging models such as ElectroMagnetic Articulography (EMA), Electropalatography (EPG), Laryngography, Electroglottography (EGG), X-ray Cineradiography, Ultrasound, and real-time Magnetic Resonance Imaging (rtMRI), (d) Methods or models - recent works are considered, and therefore all the works are based on machine learning, (e) Evaluation - as AAI is a non-linear regression problem, the performance evaluation is mostly done by Correlation Coefficient (CC), Root Mean Square Error (RMSE), and also considered Mean Square Error (MSE), and Mean Format Error (MFE). The practical application of the AAI model can provide a better and user-friendly interpretable image feedback system of articulatory positions, especially tongue movement. Such trajectory feedback system can be used to provide phonetic, language, and speech therapy for pathological subjects.
Automated Taxi Booking Operations for Autonomous Vehicles
Venue: arXiv cs.NI 2025
Year: 2025

In a conventional taxi booking system, all taxi operations are mostly done by a decision made by drivers which is hard to implement in unmanned vehicles. To address this challenge, we introduce a taxi booking system which assists autonomous vehicles to pick up customers. The system can allocate an autonomous vehicle (AV) as well as plan service trips for a customer request. We use our own AV to serve a customer who uses a mobile application to make his taxi request. Apart from customer and AV, we build a server to monitor customers and AVs. It also supports inter-communication between a customer and an AV once AV decided to pick up a customer.
Improved Decoding Algorithm of BD-LRPC Codes
Venue: arXiv cs.IT 2025
Year: 2025

A Bounded-Degree Low-Rank Parity-Check (BD-LRPC) code is a rank-metric code that admits a parity-check matrix whose support is generated by a set of powers of an element. This specific structure of the parity-check matrix was employed to enhance the first phase of the decoding algorithm through the expansion of the syndrome support. However, this expansion decreases the probability of recovering the error support in the second phase of the decoding algorithm. This paper introduces a novel method based on successive intersections to recover the error support. This method offers two key advantages: it increases the probability of successful decoding and enables the decoding of a greater number of errors.
A mean teacher algorithm for unlearning of language models
Venue: arXiv cs.LG 2025
Year: 2025

One of the goals of language model unlearning is to reduce memorization of selected text instances while retaining the model's general abilities. Despite various proposed methods, reducing memorization of large datasets without noticeable degradation in model utility remains challenging. In this paper, we investigate the mean teacher algorithm (Tarvainen & Valpola, 2017), a simple proximal optimization method from continual learning literature that gradually modifies the teacher model. We show that the mean teacher can approximate a trajectory of a slow natural gradient descent (NGD), which inherently seeks low-curvature updates that are less likely to degrade the model utility. While slow NGD can suffer from vanishing gradients, we introduce a new unlearning loss called "negative log-unlikelihood" (NLUL) that avoids this problem. We show that the combination of mean teacher and NLUL improves some metrics on the MUSE benchmarks (Shi et al., 2024).
Understanding Adolescents' Perceptions of Benefits and Risks in Health AI Technologies through Design Fiction
Venue: arXiv cs.HC 2025
Year: 2025

Despite the growing research on users' perceptions of health AI, adolescents' perspectives remain underexplored. This study explores adolescents' perceived benefits and risks of health AI technologies in clinical and personal health settings. Employing Design Fiction, we conducted interviews with 16 adolescents (aged 13-17) using four fictional design scenarios that represent current and future health AI technologies as probes. Our findings reveal that with a positive yet cautious attitude, adolescents envision unique benefits and risks specific to their age group. While health AI technologies were seen as valuable learning resources, they also raised concerns about confidentiality with their parents. Additionally, we identified several factors, such as severity of health conditions and previous experience with AI, influencing their perceptions of trust and privacy in health AI. We explore how these insights can inform the future of design of health AI technologies to support learning, engagement, and trust as adolescents navigate their healthcare journey.
CytoFM: The first cytology foundation model
Venue: arXiv cs.CV 2025
Year: 2025

Cytology is essential for cancer diagnostics and screening due to its minimally invasive nature. However, the development of robust deep learning models for digital cytology is challenging due to the heterogeneity in staining and preparation methods of samples, differences across organs, and the limited availability of large, diverse, annotated datasets. Developing a task-specific model for every cytology application is impractical and non-cytology-specific foundation models struggle to generalize to tasks in this domain where the emphasis is on cell morphology. To address these challenges, we introduce CytoFM, the first cytology self-supervised foundation model. Using iBOT, a self-supervised Vision Transformer (ViT) training framework incorporating masked image modeling and self-distillation, we pretrain CytoFM on a diverse collection of cytology datasets to learn robust, transferable representations. We evaluate CytoFM on multiple downstream cytology tasks, including breast cancer classification and cell type identification, using an attention-based multiple instance learning framework. Our results demonstrate that CytoFM performs better on two out of three downstream tasks than existing foundation models pretrained on histopathology (UNI) or natural images (iBOT-Imagenet). Visualizations of learned representations demonstrate our model is able to attend to cytologically relevant features. Despite a small pre-training dataset, CytoFM's promising results highlight the ability of task-agnostic pre-training approaches to learn robust and generalizable features from cytology data.
"Can't believe I'm crying over an anime girl": Public Parasocial Grieving and Coping Towards VTuber Graduation and Termination
Venue: arXiv cs.HC 2025
Year: 2025

Despite the significant increase in popularity of Virtual YouTubers (VTubers), research on the unique dynamics of viewer-VTuber parasocial relationships is nascent. This work investigates how English-speaking viewers grieved VTubers whose identities are no longer used, an interesting context as the nakanohito (i.e., the person behind the VTuber identity) is usually alive post-retirement and might "reincarnate" as another VTuber. We propose a typology for VTuber retirements and analyzed 13,655 Reddit posts and comments spanning nearly three years using mixed-methods. Findings include how viewers coped using methods similar to when losing loved ones, alongside novel coping methods reflecting different attachment styles. Although emotions like sadness, shock, concern, disapproval, confusion, and love decreased with time, regret and loyalty showed opposite trends. Furthermore, viewers' reactions situated a VTuber identity within a community of content creators and viewers. We also discuss design implications alongside implications on the VTuber ecosystem and future research directions.
Equilibrium Conserving Neural Operators for Super-Resolution Learning
Venue: arXiv cs.LG 2025
Year: 2025

Neural surrogate solvers can estimate solutions to partial differential equations in physical problems more efficiently than standard numerical methods, but require extensive high-resolution training data. In this paper, we break this limitation; we introduce a framework for super-resolution learning in solid mechanics problems. Our approach allows one to train a high-resolution neural network using only low-resolution data. Our Equilibrium Conserving Operator (ECO) architecture embeds known physics directly into the network to make up for missing high-resolution information during training. We evaluate this ECO-based super-resolution framework that strongly enforces conservation-laws in the predicted solutions on two working examples: embedded pores in a homogenized matrix and randomly textured polycrystalline materials. ECO eliminates the reliance on high-fidelity data and reduces the upfront cost of data collection by two orders of magnitude, offering a robust pathway for resource-efficient surrogate modeling in materials modeling. ECO is readily generalizable to other physics-based problems.
Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering
Venue: arXiv cs.CL 2025
Year: 2025

Existing Retrieval-Augmented Generation (RAG) systems face challenges in enterprise settings due to limited retrieval scope and data security risks. When relevant internal documents are unavailable, the system struggles to generate accurate and complete responses. Additionally, using closed-source Large Language Models (LLMs) raises concerns about exposing proprietary information. To address these issues, we propose the Secure Multifaceted-RAG (SecMulti-RAG) framework, which retrieves not only from internal documents but also from two supplementary sources: pre-generated expert knowledge for anticipated queries and on-demand external LLM-generated knowledge. To mitigate security risks, we adopt a local open-source generator and selectively utilize external LLMs only when prompts are deemed safe by a filtering mechanism. This approach enhances completeness, prevents data leakage, and reduces costs. In our evaluation on a report generation task in the automotive industry, SecMulti-RAG significantly outperforms traditional RAG - achieving 79.3 to 91.9 percent win rates across correctness, richness, and helpfulness in LLM-based evaluation, and 56.3 to 70.4 percent in human evaluation. This highlights SecMulti-RAG as a practical and secure solution for enterprise RAG.
The Long Arm of Nashian Allocation in Online $p$-Mean Welfare Maximization
Venue: arXiv cs.GT 2025
Year: 2025

We study the online allocation of divisible items to $n$ agents with additive valuations for $p$-mean welfare maximization, a problem introduced by Barman, Khan, and Maiti~(2022). Our algorithmic and hardness results characterize the optimal competitive ratios for the entire spectrum of $-\infty \le p \le 1$. Surprisingly, our improved algorithms for all $p \le \frac{1}{\log n}$ are simply the greedy algorithm for the Nash welfare, supplemented with two auxiliary components to ensure all agents have non-zero utilities and to help a small number of agents with low utilities. In this sense, the long arm of Nashian allocation achieves near-optimal competitive ratios not only for Nash welfare but also all the way to egalitarian welfare.
RT-HDIST: Ray-Tracing Core-based Hausdorff Distance Computation
Venue: arXiv cs.GR 2025
Year: 2025

The Hausdorff distance is a fundamental metric with widespread applications across various fields. However, its computation remains computationally expensive, especially for large-scale datasets. In this work, we present RT-HDIST, the first Hausdorff distance algorithm accelerated by ray-tracing cores (RT-cores). By reformulating the Hausdorff distance problem as a series of nearest-neighbor searches and introducing a novel quantized index space, RT-HDIST achieves significant reductions in computational overhead while maintaining exact results. Extensive benchmarks demonstrate up to a two-order-of-magnitude speedup over prior state-of-the-art methods, underscoring RT-HDIST's potential for real-time and large-scale applications.
D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Model
Venue: arXiv cs.CL 2025
Year: 2025

Evaluating generative models with open-ended generation is challenging due to inconsistencies in response formats. Multiple-choice (MC) evaluation mitigates this issue, but generating high-quality distractors is time-consuming and labor-intensive. We introduce D-GEN, the first open-source distractor generator model that transforms open-ended data into an MC format. To evaluate distractor quality, we propose two novel methods: (1) ranking alignment, ensuring generated distractors retain the discriminatory power of ground-truth distractors, and (2) entropy analysis, comparing model confidence distributions. Our results show that D-GEN preserves ranking consistency (Spearman's rho 0.99, Kendall's tau 0.94) and closely matches the entropy distribution of ground-truth distractors. Human evaluation further confirms the fluency, coherence, distractiveness, and incorrectness. Our work advances robust and efficient distractor generation with automated evaluation, setting a new standard for MC evaluation.
SatelliteCalculator: A Multi-Task Vision Foundation Model for Quantitative Remote Sensing Inversion
Venue: arXiv cs.CV 2025
Year: 2025

Quantitative remote sensing inversion plays a critical role in environmental monitoring, enabling the estimation of key ecological variables such as vegetation indices, canopy structure, and carbon stock. Although vision foundation models have achieved remarkable progress in classification and segmentation tasks, their application to physically interpretable regression remains largely unexplored. Furthermore, the multi-spectral nature and geospatial heterogeneity of remote sensing data pose significant challenges for generalization and transferability. To address these issues, we introduce SatelliteCalculator, the first vision foundation model tailored for quantitative remote sensing inversion. By leveraging physically defined index formulas, we automatically construct a large-scale dataset of over one million paired samples across eight core ecological indicators. The model integrates a frozen Swin Transformer backbone with a prompt-guided architecture, featuring cross-attentive adapters and lightweight task-specific MLP decoders. Experiments on the Open-Canopy benchmark demonstrate that SatelliteCalculator achieves competitive accuracy across all tasks while significantly reducing inference cost. Our results validate the feasibility of applying foundation models to quantitative inversion, and provide a scalable framework for task-adaptive remote sensing estimation.
Trust, but verify
Venue: arXiv cs.AI 2025
Year: 2025

Decentralized AI agent networks, such as Gaia, allows individuals to run customized LLMs on their own computers and then provide services to the public. However, in order to maintain service quality, the network must verify that individual nodes are running their designated LLMs. In this paper, we demonstrate that in a cluster of mostly honest nodes, we can detect nodes that run unauthorized or incorrect LLM through social consensus of its peers. We will discuss the algorithm and experimental data from the Gaia network. We will also discuss the intersubjective validation system, implemented as an EigenLayer AVS to introduce financial incentives and penalties to encourage honest behavior from LLM nodes.
Learning from Noisy Pseudo-labels for All-Weather Land Cover Mapping
Venue: arXiv cs.CV 2025
Year: 2025

Semantic segmentation of SAR images has garnered significant attention in remote sensing due to the immunity of SAR sensors to cloudy weather and light conditions. Nevertheless, SAR imagery lacks detailed information and is plagued by significant speckle noise, rendering the annotation or segmentation of SAR images a formidable task. Recent efforts have resorted to annotating paired optical-SAR images to generate pseudo-labels through the utilization of an optical image segmentation network. However, these pseudo-labels are laden with noise, leading to suboptimal performance in SAR image segmentation. In this study, we introduce a more precise method for generating pseudo-labels by incorporating semi-supervised learning alongside a novel image resolution alignment augmentation. Furthermore, we introduce a symmetric cross-entropy loss to mitigate the impact of noisy pseudo-labels. Additionally, a bag of training and testing tricks is utilized to generate better land-cover mapping results. Our experiments on the GRSS data fusion contest indicate the effectiveness of the proposed method, which achieves first place. The code is available at https://github.com/StuLiu/DFC2025Track1.git.