arXiv:2602.15239v1 Announce Type: new Abstract: Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key design choice in GTs is the use of Graph Neural Network (GNN)-based positional…
arXiv:2602.15089v1 Announce Type: new Abstract: In predictive maintenance of equipment, deep learning-based time series anomaly detection has garnered significant attention; however, pure deep learning approaches often fail to achieve sufficient accuracy on real-world data. This study proposes a hybrid approach…
arXiv:2602.15253v1 Announce Type: new Abstract: Neural scaling laws — power-law relationships between loss, model size, and data — have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored. We present the first…
These ricocheting ruptures may be more common than previously thought.
Overcoming challenges of 3D printing with multiple functional materials, MIT researchers fabricated an electric linear motor in hours.
The context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
arXiv:2406.03862v3 Announce Type: replace Abstract: This study investigates behavior-targeted attacks on reinforcement learning and their countermeasures. Behavior-targeted attacks aim to manipulate the victim’s behavior as desired by the adversary through adversarial interventions in state observations. Existing behavior-targeted attacks have some…
arXiv:2602.10452v2 Announce Type: replace-cross Abstract: This paper studies distributed online convex optimization with time-varying coupled constraints, motivated by distributed online control in network systems. Most prior work assumes a separability condition: the global objective and coupled constraint functions are sums…
arXiv:2602.15738v1 Announce Type: cross Abstract: Integrating human expertise into machine learning systems often reduces the role of experts to labeling oracles, a paradigm that limits the amount of information exchanged and fails to capture the nuances of human judgment. We…
arXiv:2502.07397v2 Announce Type: replace-cross Abstract: Linear bandits have long been a central topic in online learning, with applications ranging from recommendation systems to adaptive clinical trials. Their general learnability has been established when the objective is to minimise the inner…
