OPIS: An Input-Grounded Benchmark for Multi-Object Memory in Video World Models
arXiv:2609.35052v2 Announce Type: replace-cross Abstract: Video world models must preserve the visual state of the world over time, but existing…
IECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram
arXiv:2610.28083v1 Announce Type: new Abstract: Electrocardiography (ECG) is a cornerstone of cardiac diagnosis, yet most machine learning methods for ECG…
Lagrangian–Eulerian learning of flow field and trajectories with TrajectoryFlowNet
Nature Machine Intelligence, Published online: 20 October 2026; doi:10.1038/s42256-026-01316-7 Wan et al. introduce a physics-informed learning model that simultaneously predicts…
Agentic inequality
Nature Machine Intelligence, Published online: 20 October 2026; doi:10.1038/s42256-026-01318-5 Sharp et al. investigate the implications of unequal access to AI…
Physics-informed machine learning: a hybrid lens for fluid dynamics
Nature Machine Intelligence, Published online: 20 October 2026; doi:10.1038/s42256-026-01319-4 A neural network approach bridging Lagrangian and Eulerian perspectives transforms sparse…
Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides
arXiv:2610.22373v1 Announce Type: new Abstract: LLM-based agents are increasingly used as autonomous orchestrators in distributed and multi-agent systems, yet a…
Router Drift Control: Mitigating Off-Policy Degradation in Mixture-of-Experts Reinforcement Learning
arXiv:2610.21682v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) can become off-policy due to training-inference mismatch, mini-batch updates, and asynchronous training.…
Behavioral Couplings: From Formation to Behavioral Emergence in Language Models
arXiv:2610.23847v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) can induce behaviors beyond those explicitly targeted. For example, training…
Neural Expansion: A Unified Mechanism for How Deep Neural Networks Generalize
arXiv:2610.23716v1 Announce Type: cross Abstract: Deep neural networks demonstrate remarkable generalization despite being highly over-parameterized, challenging traditional statistical learning theories.…