arXiv:2603.25226v1 Announce Type: cross
Abstract: The emergence of Large Language Models (LLMs) has catalyzed a paradigm shift in programming, giving rise to “vibe coding”, where users can build complete projects and even control computers using natur…
arXiv:2603.25740v1 Announce Type: new
Abstract: Human driving behavior is inherently personal, which is shaped by long-term habits and influenced by short-term intentions. Individuals differ in how they accelerate, brake, merge, yield, and overtake ac…
arXiv:2603.24908v1 Announce Type: new
Abstract: Coordinating teams of aerial robots in cluttered three-dimensional (3D) environments requires a principled integration of discrete mission planning-deciding which robot serves which goals and in what ord…
arXiv:2603.14867v2 Announce Type: replace-cross
Abstract: Many strategic decision-making problems, such as environment design for warehouse robots, can be naturally formulated as bi-level reinforcement learning (RL), where a leader agent optimizes its…
arXiv:2603.23722v1 Announce Type: cross
Abstract: While Multi-Agent Reinforcement Learning (MARL) algorithms achieve unprecedented successes across complex continuous domains, their standard deployment strictly adheres to a synchronous operational par…
arXiv:2603.23971v1 Announce Type: cross
Abstract: Developers and consumers increasingly choose reasoning language models (RLMs) based on their listed API prices. However, how accurately do these prices reflect actual inference costs? We conduct the fi…
arXiv:2603.23853v1 Announce Type: new
Abstract: Combining multiple Vision-Language Models (VLMs) can enhance multimodal reasoning and robustness, but aggregating heterogeneous models’ outputs amplifies uncertainty and increases the risk of hallucinati…
arXiv:2508.11733v3 Announce Type: replace-cross
Abstract: LLM-based multi-agent systems exhibit strong collaborative capabilities but often suffer from redundant communication and excessive token overhead. Existing methods typically enhance efficiency…
arXiv:2603.24559v1 Announce Type: cross
Abstract: We introduce the Free-Market Algorithm (FMA), a novel metaheuristic inspired by free-market economics. Unlike Genetic Algorithms, Particle Swarm Optimization, and Simulated Annealing — which require p…
arXiv:2602.16485v2 Announce Type: replace-cross
Abstract: Existing Multi-Agent Systems (MAS) typically rely on homogeneous model configurations, failing to exploit the diverse expertise inherent in different post-trained architectures. We propose Team…