Accepted Papers

• Expert-Supported Reinforcement Learning for Automated Environment Anomaly Discovery

Aurélien Chambon, Nicolas Grelier

• Hierarchical Control in Multi-Agent Games: LLM- based Planning and RL Execution

Jannik Hösch, Alessandro Sestini, Florian Fuchs, Amir Baghi, Joakim Bergdahl, Konrad Tollmar, Jean-Philippe Barrette-LaPierre, Linus Gisslén

• Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26

Florian Fuchs, Jessy Gosselin-Grant, Boris Skuin, Michele Petteni, Alessandro Sestini, Joakim Bergdahl, Amir Baghi, Linus Gisslén

• Planning Before Acting: Dependency-Aware LLM Agents in Minecraft

Thomas Fenno, Daniel S. Brown, Matthew Mitchell

• Generating Plausible Cheating Trajectories for Multi- Player Games using LLM-Generated Behavior Trees

Romina Abachi, Bettina Hein, Joshua Romoff

• FootsiesGym: A Fighting Game Benchmark for Multi-Agent Reinforcement Learning

Chase McDonald, Nathan Tsang, Wesley N. Kerr

• Benchmarking Language Agents on Open-Ended Multi-Agent Coordination in Game Worlds

Kale-ab Tessera, Andras Szecsenyi, Cameron Barker, Alexander Rutherford, Davide Paglieri, Aidan Scannell, Henry Gouk, Elliot J. Crowley, Tim Rocktäschel, Amos Storkey

• Superhuman AI for Generals.io Using Self-play Reinforcement Learning

Matej Straka, Martin Schmid

• Hearing to Hunt: Acoustic Observation Spaces for Combat Policy Learning

Jessenth Ebenezer Sankar