Tootfinder

The Computational Foundations of Collective Intelligence
Charlie Pilgrim, Joe Morford, Elizabeth Warren, M\'elisande Aellen, Christopher Krupenye, Richard P Mann, Dora Biro
https://arxiv.org/abs/2509.07999

The Computational Foundations of Collective Intelligence
Why do collectives outperform individuals when solving some problems? Fundamentally, collectives have greater computational resources with more sensory information, more memory, more processing capacity, and more ways to act. While greater resources present opportunities, there are also challenges in coordination and cooperation inherent in collectives with distributed, modular structures. Despite these challenges, we show how collective resource advantages lead directly to well-known forms of …

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 …

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes
Jun Kang Yap, Vishnu Monn Baskaran, Wen Shan Tan, Ze Yang Ding, Hao Wang, David L. Dowe
https://arxiv.org/abs/2507.15307

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes
Electric Vehicles (EVs) are becoming increasingly prevalent nowadays, with studies highlighting their potential as mobile energy storage systems to provide grid support. Realising this potential requires effective charging coordination, which are often formulated as mixed-integer programming (MIP) problems. However, MIP problems are NP-hard and often intractable when applied to time-sensitive tasks. To address this limitation, we propose a deep learning assisted approach for optimising a day-ah…

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…