When Agents Meet "Bad Teammates": Google Open-Sources CLASHarness, Testing How Frontier Large Models Handle Multi-Agent Conflict Scenarios

A research team from Google and multiple universities released the multi-agent conflict benchmark CLASHarness, covering 20 real-world domains and 600 multi-turn executable conflict tasks, with code and dataset open-sourced on GitHub (arXiv 2610.19128). The benchmark uses a three-layer, eight-stage generation pipeline to ensure tasks are 100% solvable, and renders key ground truth only into 4K screenshots to prevent models from passing by searching text. Evaluation of 15 mainstream models and over 9000 sandbox trajectories shows that under the standard 20-step limit, Claude Opus 5 and GPT-5.6 Sol have the highest pass rates, at only 25.2% and 24.5% respectively, far below the 55%–67% single-step scores of top models; weak models tend to blindly trust teammates' text reports, while strong models proactively check images to verify, and after turning off visual input, all models' pass rates plummet by 87%–90%.