MAVEN-T: Multi-Agent enVironment-aware Enhanced Neural Trajectory predictor with

arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains

arXiv:2603.29908v1 Announce Type: new Abstract: Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM

arXiv:2605.19447v1 Announce Type: new Abstract: Reinforcement learning can train LLM agents from sparse task rewards, but long-horizon credit assignment remains challenging: a

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2604.06691v1 Announce Type: new Abstract: Real world deployment of multi agent reinforcement learning MARL systems is fundamentally constrained by limited compute memory and

arXiv:2610.24516v1 Announce Type: new Abstract: Spatial-temporal forecasting is pivotal for intelligent urban management yet remains challenging due to the intricate coupling of

arXiv:2605.15871v1 Announce Type: new Abstract: Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers.

arXiv:2603.23234v1 Announce Type: new Abstract: Large language model (LLM)-based agents rely on memory mechanisms to reuse knowledge from past problem-solving experiences.