Bigger datasets aren’t always better
MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.
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Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation
arXiv:2504.18720v2 Announce Type: replace Abstract: Deep learning has advanced weather forecasting, but accurate predictions first require identifying the current state of the atmosphere from observational data. In this work, we introduce Appa, a score-based data assimilation model generating global atmospheric…
The Shape of Data: Topology Meets Analytics. A Practical Introduction to Topological Analytics and the Stability Index (TSI) in Business
arXiv:2511.13503v1 Announce Type: cross Abstract: Modern business and economic datasets often exhibit nonlinear, multi-scale structures that traditional linear tools under-represent. Topological Data Analysis (TDA) offers a geometric lens for uncovering robust patterns, such as connected components, loops and voids, across…
Fira: Can We Achieve Full-rank Training of LLMs Under Low-rank Constraint?
arXiv:2410.01623v3 Announce Type: replace Abstract: Low-rank training has emerged as a promising approach for reducing memory usage in training Large Language Models (LLMs). Previous methods either rely on decomposing weight matrices (e.g., LoRA), or seek to decompose gradient matrices (e.g.,…
Multi-Domain EEG Representation Learning with Orthogonal Mapping and Attention-based Fusion for Cognitive Load Classification
arXiv:2511.12394v1 Announce Type: cross Abstract: We propose a new representation learning solution for the classification of cognitive load based on Electroencephalogram (EEG). Our method integrates both time and frequency domains by first passing the raw EEG signals through the convolutional…
Efficient Adversarial Malware Defense via Trust-Based Raw Override and Confidence-Adaptive Bit-Depth Reduction
arXiv:2511.12827v1 Announce Type: cross Abstract: The deployment of robust malware detection systems in big data environments requires careful consideration of both security effectiveness and computational efficiency. While recent advances in adversarial defenses have demonstrated strong robustness improvements, they often introduce…
Understanding World or Predicting Future? A Comprehensive Survey of World Models
arXiv:2411.14499v3 Announce Type: replace-cross Abstract: The concept of world models has garnered significant attention due to advancements in multimodal large language models such as GPT-4 and video generation models such as Sora, which are central to the pursuit of artificial…
MusRec: Zero-Shot Text-to-Music Editing via Rectified Flow and Diffusion Transformers
arXiv:2511.04376v2 Announce Type: replace-cross Abstract: Music editing has emerged as an important and practical area of artificial intelligence, with applications ranging from video game and film music production to personalizing existing tracks according to user preferences. However, existing models face…
Output Supervision Can Obfuscate the Chain of Thought
arXiv:2511.11584v1 Announce Type: new Abstract: OpenAI (2025) showed that training against a chain of thought (CoT) monitor can cause obfuscated CoTs, which contain bad behavior the monitor cannot detect. They proposed to keep CoTs monitorable by training only against output…
Parameter-Efficient and Personalized Federated Training of Generative Models at the Edge
arXiv:2511.11585v1 Announce Type: new Abstract: Large generative models (for example, language and diffusion models) enable high-quality text and image synthesis but are hard to train or adapt in cross-device federated settings due to heavy computation and communication and statistical/system heterogeneity.…