Tootfinder

CAMAR: Continuous Actions Multi-Agent Routing
Artem Pshenitsyn, Aleksandr Panov, Alexey Skrynnik
https://arxiv.org/abs/2508.12845 https://arxiv.org/pdf/250…

CAMAR: Continuous Actions Multi-Agent Routing
Multi-agent reinforcement learning (MARL) is a powerful paradigm for solving cooperative and competitive decision-making problems. While many MARL benchmarks have been proposed, few combine continuous state and action spaces with challenging coordination and planning tasks. We introduce CAMAR, a new MARL benchmark designed explicitly for multi-agent pathfinding in environments with continuous actions. CAMAR supports cooperative and competitive interactions between agents and runs efficiently at…

Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
Jo\~ao Vitor de Carvalho Silva, Douglas G. Macharet
https://arxiv.org/abs/2508.14635

Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
The ability to coordinate actions across multiple agents is critical for solving complex, real-world problems. Large Language Models (LLMs) have shown strong capabilities in communication, planning, and reasoning, raising the question of whether they can also support effective collaboration in multi-agent settings. In this work, we investigate the use of LLM agents to solve a structured victim rescue task that requires division of labor, prioritization, and cooperative planning. Agents operate …