Instance discrimination is a self-supervised representation learning paradigm wherein individual instances within a dataset are treated as distinct classes. This is typically achieved by generating two disparate views of each instance by applying stochastic transformations, which encourages the model to learn representations that are invariant to the common underlying object across these views. While this approach facilitates the acquisition of invariant representations for dataset instances under various handcrafted transformations (e.g., random cropping, color jittering), an exclusive reliance on such data transformations for achieving invariance may inherently limit the model's generalization to unseen datasets and diverse downstream tasks. The inherent limitation stems from the fact that the finite set of transformations within the data processing pipeline is unable to encompass the full spectrum of potential data variations. In this study, we provide the technical foundation for leveraging semantic pairs to enhance the generalization of the model's representation and empirically demonstrate that incorporating semantic pairs mitigates the issue of limited transformation coverage. Specifically, we propose that exposing the model to semantic pairs (i.e., two instances belonging to the same semantic category) introduces varied real-world scene contexts, thereby fostering the development of more generalizable object representations. To validate this hypothesis, we constructed and released a novel dataset comprising curated semantic pairs and conducted extensive experimentation to empirically establish that their inclusion enables the model to learn more general representations, ultimately leading to improved performance across diverse downstream tasks.

Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the fundamental types, which is characterized by its particular-to-general thinking process and the non-uniqueness of its answers. The inductive mode is crucial for knowledge generalization and aligns better with human cognition, so it is a fundamental mode of learning, hence attracting increasing interest. Despite the importance of inductive reasoning, there is no systematic summary of it. Therefore, this paper presents the first comprehensive survey of inductive reasoning for LLMs. First, methods for improving inductive reasoning are categorized into three main areas: post-training, test-time scaling, and data augmentation. Then, current benchmarks of inductive reasoning are summarized, and a unified sandbox-based evaluation approach with the observation coverage metric is derived. Finally, we offer some analyses regarding the source of inductive ability and how simple model architectures and data help with inductive tasks, providing a solid foundation for future research.

Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide complementary perspectives on interconnected physiological processes. While recent self-supervised learning (SSL) advances have improved unimodal representation learning, existing multi-modal approaches often rely on CLIP-style contrastive objectives that overfit to easily aligned features and misclassify valid cross-modal relationships as negatives, resulting in fragmented and non-generalizable embeddings. To overcome these limitations, we propose ProtoMM, a novel SSL framework that introduces a shared prototype dictionary to anchor heterogeneous modalities in a common embedding space. By clustering representations around shared prototypes rather than explicit negative sampling, our method captures complementary information across modalities and provides a coherent "common language" for physiological signals. In this work, we focus on developing a Pulse Motion foundation model with ProtoMM and demonstrate that our approach outperforms contrastive-only and prior multimodal SSL methods, achieving state-of-the-art performance while offering improved interpretability of learned features. Digital biomarkers (for stress, physical activity, sleep, etc.) obtained from wearable sensors, such as smart watches and smartphones, provide unprecedented opportunities to give individuals novel insights into their states of health and wellness throughout their daily life, along with new tools for managing their health-related behaviors (Rehg et al., 2017). In order to realize this potential, however, it is critical to develop effective models for multi-modal time series biosignal data, so that complementary sensing modalities can be leveraged to overcome the ambiguities and noise that are inherent in wearable signals collected in the field environment. Recently, there has been substantial progress in developing unimodal Foundation Models (FMs) which are pre-trained using large datasets on modalities such as accelerometry (Xu et al.; Y uan et al., 2024), ECG (Abbaspourazad et al., 2023; McKeen et al., 2024), and PPG (Saha et al., 2025; Pillai et al., 2024). These models have demonstrated effective generalization to downstream tasks and have established new benchmarks for performance.

Models of hyperbolic geometry have been successfully used to embed hierarchies, i.e. tree-based structures [

In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL methods rely on heuristics or predefined rules for generating pseudo-labels and leveraging unlabeled data.

Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts.

Contrastive learning has been a leading paradigm for self-supervised learning, but it is widely observed that it comes at the price of sacrificing useful features ( e.g., colors) by being invariant to data augmentations.

Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness.