OpenRaiser/Intern-Atlas huggingface dataset:
AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts
Venue: arXiv cs.LG 2025
Year: 2025

Sleep is essential for good health throughout our lives, yet studying its dynamics requires manual sleep staging, a labor-intensive step in sleep research and clinical care. Across centers, polysomnography (PSG) recordings are traditionally scored in 30-s epochs for pragmatic, not physiological, reasons and can vary considerably in electrode count, montage, and subject characteristics. These constraints present challenges in conducting harmonized multi-center sleep studies and discovering novel, robust biomarkers on shorter timescales. Here, we present AnySleep, a deep neural network model that uses any electroencephalography (EEG) or electrooculography (EOG) data to score sleep at adjustable temporal resolutions. We trained and validated the model on over 19,000 overnight recordings from 21 datasets collected across multiple clinics, spanning nearly 200,000 hours of EEG and EOG data, to promote robust generalization across sites. The model attains state-of-the-art performance and surpasses or equals established baselines at 30-s epochs. Performance improves as more channels are provided, yet remains strong when EOG is absent or when only EOG or single EEG derivations (frontal, central, or occipital) are available. On sub-30-s timescales, the model captures short wake intrusions consistent with arousals and improves prediction of physiological characteristics (age, sex) and pathophysiological conditions (sleep apnea), relative to standard 30-s scoring. We make the model publicly available to facilitate large-scale studies with heterogeneous electrode setups and to accelerate the discovery of novel biomarkers in sleep.
Solving the Heilbronn Triangle Problem using Global Optimization Methods
Venue: arXiv cs.CG 2025
Year: 2025

We study the Heilbronn triangle problem, which involves placing n points in the unit square such that the minimum area of any triangle formed by these points is maximized. A straightforward maximin formulation of this problem is highly non-linear and non-convex due to the existence of bilinear terms and absolute value equations. We propose two mixed-integer quadratically constrained programming (MIQCP) and one QCP formulation, which can be readily solved by any global optimization solver. We develop several formulation enhancements in the form of bound tightening and symmetry breaking inequalities that are prevalent in the global optimization literature in addition to other enhancements that exploit the problem structure. With the help of these enhancements, our models reproduce proven optimal values for instances up to n = 8 points with certified optimality in the order of seconds. In the case of n = 9 points, for which no analytical proof is known, we establish a certified optimal value by a computational effort of one day. This is a significant improvement over the previous benchmark established in 31 days of computations by Chen et al. (2017).
Improving Slow Transfer Predictions: Generative Methods Compared
Venue: arXiv cs.LG 2025
Year: 2025

Monitoring data transfer performance is a crucial task in scientific computing networks. By predicting performance early in the communication phase, potentially sluggish transfers can be identified and selectively monitored, optimizing network usage and overall performance. A key bottleneck to improving the predictive power of machine learning (ML) models in this context is the issue of class imbalance. This project focuses on addressing the class imbalance problem to enhance the accuracy of performance predictions. In this study, we analyze and compare various augmentation strategies, including traditional oversampling methods and generative techniques. Additionally, we adjust the class imbalance ratios in training datasets to evaluate their impact on model performance. While augmentation may improve performance, as the imbalance ratio increases, the performance does not significantly improve. We conclude that even the most advanced technique, such as CTGAN, does not significantly improve over simple stratified sampling.
Synthetic Electrogram Generation with Variational Autoencoders for ECGI
Venue: arXiv cs.LG 2025
Year: 2025

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, and its clinical assessment requires accurate characterization of atrial electrical activity. Noninvasive electrocardiographic imaging (ECGI) combined with deep learning (DL) approaches for estimating intracardiac electrograms (EGMs) from body surface potentials (BSPMs) has shown promise, but progress is hindered by the limited availability of paired BSPM-EGM datasets. To address this limitation, we investigate variational autoencoders (VAEs) for the generation of synthetic multichannel atrial EGMs. Two models are proposed: a sinus rhythm-specific VAE (VAE-S) and a class-conditioned VAE (VAE-C) trained on both sinus rhythm and AF signals. Generated EGMs are evaluated using morphological, spectral, and distributional similarity metrics. VAE-S achieves higher fidelity with respect to in silico EGMs, while VAE-C enables rhythm-specific generation at the expense of reduced sinus reconstruction quality. As a proof of concept, the generated EGMs are used for data augmentation in a downstream noninvasive EGM reconstruction task, where moderate augmentation improves estimation performance. These results demonstrate the potential of VAE-based generative modeling to alleviate data scarcity and enhance deep learning-based ECGI pipelines.
FoodLogAthl-218: Constructing a Real-World Food Image Dataset Using Dietary Management Applications
Venue: arXiv cs.CV 2025
Year: 2025

Food image classification models are crucial for dietary management applications because they reduce the burden of manual meal logging. However, most publicly available datasets for training such models rely on web-crawled images, which often differ from users' real-world meal photos. In this work, we present FoodLogAthl-218, a food image dataset constructed from real-world meal records collected through the dietary management application FoodLog Athl. The dataset contains 6,925 images across 218 food categories, with a total of 14,349 bounding boxes. Rich metadata, including meal date and time, anonymized user IDs, and meal-level context, accompany each image. Unlike conventional datasets-where a predefined class set guides web-based image collection-our data begins with user-submitted photos, and labels are applied afterward. This yields greater intra-class diversity, a natural frequency distribution of meal types, and casual, unfiltered images intended for personal use rather than public sharing. In addition to (1) a standard classification benchmark, we introduce two FoodLog-specific tasks: (2) an incremental fine-tuning protocol that follows the temporal stream of users' logs, and (3) a context-aware classification task where each image contains multiple dishes, and the model must classify each dish by leveraging the overall meal context. We evaluate these tasks using large multimodal models (LMMs). The dataset is publicly available at https://huggingface.co/datasets/FoodLog/FoodLogAthl-218.
Towards Nepali-language LLMs: Efficient GPT training with a Nepali BPE tokenizer
Venue: arXiv cs.CL 2025
Year: 2025

Nepali, a low-resource language spoken by over 32 million people, continues to face challenges in natural language processing (NLP) due to its complex grammar, agglutinative morphology, and limited availability of high-quality corpora. Most efforts to date have centered on basic encoder architectures; they remain insufficient for Nepali-specific text generation. This study presents a GPT-2-based Nepali language model trained using several training strategies inspired by GPT-3, including optimized learning rate schedules, batch scaling, and architectural refinements. A custom 16k Byte-Pair Encoding (BPE) tokenizer was trained exclusively on Nepali text to ensure more consistent segmentation and improved input representation. The model was pretrained on a combined dataset comprising a 10.75GB cleaned NepBERTa corpus and additional web-scraped Nepali news articles. FlashAttention was integrated to reduce memory usage and stabilize training. After two epochs, the model achieved a training loss of 3.168177, a validation loss of 3.081982, and a final perplexity of 21.80, demonstrating its capability to generate coherent Nepali news-style text.
LLM-driven Knowledge Enhancement for Multimodal Cancer Survival Prediction
Venue: arXiv cs.CV 2025
Year: 2025

Current multimodal survival prediction methods typically rely on pathology images (WSIs) and genomic data, both of which are high-dimensional and redundant, making it difficult to extract discriminative features from them and align different modalities. Moreover, using a simple survival follow-up label is insufficient to supervise such a complex task. To address these challenges, we propose KEMM, an LLM-driven Knowledge-Enhanced Multimodal Model for cancer survival prediction, which integrates expert reports and prognostic background knowledge. 1) Expert reports, provided by pathologists on a case-by-case basis and refined by large language model (LLM), offer succinct and clinically focused diagnostic statements. This information may typically suggest different survival outcomes. 2) Prognostic background knowledge (PBK), generated concisely by LLM, provides valuable prognostic background knowledge on different cancer types, which also enhances survival prediction. To leverage these knowledge, we introduce the knowledge-enhanced cross-modal (KECM) attention module. KECM can effectively guide the network to focus on discriminative and survival-relevant features from highly redundant modalities. Extensive experiments on five datasets demonstrate that KEMM achieves state-of-the-art performance. The code will be released upon acceptance.
PerProb: Indirectly Evaluating Memorization in Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

The rapid advancement of Large Language Models (LLMs) has been driven by extensive datasets that may contain sensitive information, raising serious privacy concerns. One notable threat is the Membership Inference Attack (MIA), where adversaries infer whether a specific sample was used in model training. However, the true impact of MIA on LLMs remains unclear due to inconsistent findings and the lack of standardized evaluation methods, further complicated by the undisclosed nature of many LLM training sets. To address these limitations, we propose PerProb, a unified, label-free framework for indirectly assessing LLM memorization vulnerabilities. PerProb evaluates changes in perplexity and average log probability between data generated by victim and adversary models, enabling an indirect estimation of training-induced memory. Compared with prior MIA methods that rely on member/non-member labels or internal access, PerProb is independent of model and task, and applicable in both black-box and white-box settings. Through a systematic classification of MIA into four attack patterns, we evaluate PerProb's effectiveness across five datasets, revealing varying memory behaviors and privacy risks among LLMs. Additionally, we assess mitigation strategies, including knowledge distillation, early stopping, and differential privacy, demonstrating their effectiveness in reducing data leakage. Our findings offer a practical and generalizable framework for evaluating and improving LLM privacy.
ParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Venue: arXiv cs.LG 2025
Year: 2025

Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the over-smoothing problem in deep GNNs, global attention was initially introduced, eliminating the necessity for using deep GNNs. However, through empirical and theoretical analysis, we verify that the introduced global attention exhibits severe over-smoothing, causing node representations to become indistinguishable due to its inherent low-pass filtering. This effect is even stronger than that observed in GNNs. To mitigate this, we propose PageRank Transformer (ParaFormer), which features a PageRank-enhanced attention module designed to mimic the behavior of deep Transformers. We theoretically and empirically demonstrate that ParaFormer mitigates over-smoothing by functioning as an adaptive-pass filter. Experiments show that ParaFormer achieves consistent performance improvements across both node classification and graph classification tasks on 11 datasets ranging from thousands to millions of nodes, validating its efficacy. The supplementary material, including code and appendix, can be found in https://github.com/chaohaoyuan/ParaFormer.
Creating Opportunities: Co-designing an mHealth App with Older Adults
Venue: arXiv cs.HC 2025
Year: 2025

We conducted a qualitative co-design study with four adults aged 60+ to gather design insights on a Figma prototype and a generative AI (GenAI) chatbot for an app aimed at providing an AI coach to support older adults' physical activity. The initial design for both incorporates several novel aspects: a curated health knowledge base, personalised responses based on goals and health history, privacy considerations, integration with wearables for physical activity context, as well as dynamic context injection. The study yielded feedback on improving both the proposed user experience in the app and the conversation flow with the chatbot, and it will aid future work aimed at implementing a GenAI-powered health coach for older adults.
TiME: Tiny Monolingual Encoders for Efficient NLP Pipelines
Venue: arXiv cs.CL 2025
Year: 2025

Today, a lot of research on language models is focused on large, general-purpose models. However, many NLP pipelines only require models with a well-defined, small set of capabilities. While large models are capable of performing the tasks of those smaller models, they are simply not fast enough to process large amounts of data or offer real-time responses. Furthermore, they often use unnecessarily large amounts of energy, leading to sustainability concerns and problems when deploying them on battery-powered devices. In our work, we show how to train small models for such efficiency-critical applications. As opposed to many off-the-shelf NLP pipelines, our models use modern training techniques such as distillation, and offer support for low-resource languages. We call our models TiME (Tiny Monolingual Encoders) and comprehensively evaluate them on a range of common NLP tasks, observing an improved trade-off between benchmark performance on one hand, and throughput, latency and energy consumption on the other. Along the way, we show that distilling monolingual models from multilingual teachers is possible, and likewise distilling models with absolute positional embeddings from teachers with relative positional embeddings.
Robust Training of Singing Voice Synthesis Using Prior and Posterior Uncertainty
Venue: arXiv cs.SD 2025
Year: 2025

Singing voice synthesis (SVS) has seen remarkable advancements in recent years. However, compared to speech and general audio data, publicly available singing datasets remain limited. In practice, this data scarcity often leads to performance degradation in long-tail scenarios, such as imbalanced pitch distributions or rare singing styles. To mitigate these challenges, we propose uncertainty-based optimization to improve the training process of end-to-end SVS models. First, we introduce differentiable data augmentation in the adversarial training, which operates in a sample-wise manner to increase the prior uncertainty. Second, we incorporate a frame-level uncertainty prediction module that estimates the posterior uncertainty, enabling the model to allocate more learning capacity to low-confidence segments. Empirical results on the Opencpop and Ofuton-P, across Chinese and Japanese, demonstrate that our approach improves performance in various perspectives.
gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generation
Venue: arXiv cs.LG 2025
Year: 2025

We introduce gridfm-datakit-v1, a Python library for generating realistic and diverse Power Flow (PF) and Optimal Power Flow (OPF) datasets for training Machine Learning (ML) solvers. Existing datasets and libraries face three main challenges: (1) lack of realistic stochastic load and topology perturbations, limiting scenario diversity; (2) PF datasets are restricted to OPF-feasible points, hindering generalization of ML solvers to cases that violate operating limits (e.g., branch overloads or voltage violations); and (3) OPF datasets use fixed generator cost functions, limiting generalization across varying costs. gridfm-datakit addresses these challenges by: (1) combining global load scaling from real-world profiles with localized noise and supporting arbitrary N-k topology perturbations to create diverse yet realistic datasets; (2) generating PF samples beyond operating limits; and (3) producing OPF data with varying generator costs. It also scales efficiently to large grids (up to 10,000 buses). Comparisons with OPFData, OPF-Learn, PGLearn, and PF$Δ$ are provided. Available on GitHub at https://github.com/gridfm/gridfm-datakit under Apache 2.0 and via `pip install gridfm-datakit`.
Bias-Variance Trade-off for Clipped Stochastic First-Order Methods: From Bounded Variance to Infinite Mean
Venue: arXiv cs.LG 2025
Year: 2025

Stochastic optimization is fundamental to modern machine learning. Recent research has extended the study of stochastic first-order methods (SFOMs) from light-tailed to heavy-tailed noise, which frequently arises in practice, with clipping emerging as a key technique for controlling heavy-tailed gradients. Extensive theoretical advances have further shown that the oracle complexity of SFOMs depends on the tail index $α$ of the noise. Nonetheless, existing complexity results often cover only the case $α\in (1,2]$, that is, the regime where the noise has a finite mean, while the complexity bounds tend to infinity as $α$ approaches $1$. This paper tackles the general case of noise with tail index $α\in(0,2]$, covering regimes ranging from noise with bounded variance to noise with an infinite mean, where the latter case has been scarcely studied. Through a novel analysis of the bias-variance trade-off in gradient clipping, we show that when a symmetry measure of the noise tail is controlled, clipped SFOMs achieve improved complexity guarantees in the presence of heavy-tailed noise for any tail index $α\in (0,2]$. Our analysis of the bias-variance trade-off not only yields new unified complexity guarantees for clipped SFOMs across this full range of tail indices, but is also straightforward to apply and can be combined with classical analyses under light-tailed noise to establish oracle complexity guarantees under heavy-tailed noise. Finally, numerical experiments validate our theoretical findings.
TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs
Venue: arXiv cs.CV 2025
Year: 2025

This paper does not introduce a novel method but instead establishes a straightforward, incremental, yet essential baseline for video temporal grounding (VTG), a core capability in video understanding. While multimodal large language models (MLLMs) excel at various video understanding tasks, the recipes for optimizing them for VTG remain under-explored. In this paper, we present TimeLens, a systematic investigation into building MLLMs with strong VTG ability, along two primary dimensions: data quality and algorithmic design. We first expose critical quality issues in existing VTG benchmarks and introduce TimeLens-Bench, comprising meticulously re-annotated versions of three popular benchmarks with strict quality criteria. Our analysis reveals dramatic model re-rankings compared to legacy benchmarks, confirming the unreliability of prior evaluation standards. We also address noisy training data through an automated re-annotation pipeline, yielding TimeLens-100K, a large-scale, high-quality training dataset. Building on our data foundation, we conduct in-depth explorations of algorithmic design principles, yielding a series of meaningful insights and effective yet efficient practices. These include interleaved textual encoding for time representation, a thinking-free reinforcement learning with verifiable rewards (RLVR) approach as the training paradigm, and carefully designed recipes for RLVR training. These efforts culminate in TimeLens models, a family of MLLMs with state-of-the-art VTG performance among open-source models and even surpass proprietary models such as GPT-5 and Gemini-2.5-Flash. All codes, data, and models will be released to facilitate future research.
Effectively Detecting and Responding to Online Harassment with Large Language Models
Venue: arXiv cs.SI 2025
Year: 2025

Online harassment has been a persistent issue in the online space. Predominantly, research focused on online harassment in public social media platforms, while less is placed on private messaging platforms. To address online harassment on one private messaging platform, Instagram, we leverage the capabilities of Large Language Models (LLMs). To achieve this, we recruited human labelers to identify online harassment in an Instagram messages dataset. Using the previous conversation as context, we utilize an LLM pipeline to conduct large-scale labeling on Instagram messages and evaluate its performance against human labels. Then, we use LLM to generate and evaluate simulated responses to online harassment messages. We find that the LLM labeling pipeline is capable of identifying online harassment in private messages. By comparing human responses and simulated responses, we also demonstrate that our simulated responses are superior in helpfulness compared to original human responses.
Attention as Binding: A Vector-Symbolic Perspective on Transformer Reasoning
Venue: arXiv cs.AI 2025
Year: 2025

Transformer-based language models display impressive reasoning-like behavior, yet remain brittle on tasks that require stable symbolic manipulation. This paper develops a unified perspective on these phenomena by interpreting self-attention and residual streams as implementing an approximate Vector Symbolic Architecture (VSA). In this view, queries and keys define role spaces, values encode fillers, attention weights perform soft unbinding, and residual connections realize superposition of many bound structures. We use this algebraic lens to relate transformer internals to chain-of-thought traces, program-based reasoning, and memory-augmented tool use, and to explain characteristic failure modes such as variable confusion and inconsistency across logically related prompts. Building on this perspective, we propose VSA-inspired architectural biases, including explicit binding/unbinding heads and hyperdimensional memory layers, and training objectives that promote role-filler separation and robust superposition. Finally, we outline metrics for measuring "VSA-likeness" and logical compositionality, and pose theoretical and architectural open problems. Overall, the paper argues that viewing attention as soft vector-symbolic computation offers a principled route toward more interpretable and logically reliable reasoning systems.