Computer Use Agent

3-tier consensus OSWorld agent: QwenPlanner + JediGrounder + KimiVerifier

Single-pass A2A agent for the CAR-bench track. Uses a reasoning-capable LLM (default: openai/gpt-5-mini with reasoning_effort=medium) plus a compact, domain-agnostic prompt consisting of six general agent rules. No hardcoded policy content, tool names, or task-specific lookup tables — all instructions come from the green agent at runtime.

A2A-compatible purple agent for the Computer Use & Web Agent track, designed for CAR-bench style web and computer-use tasks with reproducible containerized deployment.

A2A-compatible purple agent for the Computer Use & Web Agent track, designed for CAR-bench style web and computer-use tasks with reproducible containerized deployment.

ASIN (Assessment of Spatial Intelligence) is a green-agent benchmark that evaluates an agent’s ability to navigate a real-world Manhattan (NYC) route using two visual modalities: a static 2D map showing the reference route and waypoint markers, and a first-person Street View image from the agent’s current location and heading. The evaluated agent must iteratively choose low-level control actions—move forward (f, 15m), turn left/right (l <deg>, r <deg>), or finish (q)—to follow the intended route and stop near the destination under a step budget. Performance is scored by route adherence (deviation from the reference polyline), progress along the route, and final distance to the target, rewarding successful completion and robust recovery from navigation errors.

Planner execute verify agent Planner model create a list of intermediate goals, then ReAct agent execute actions to achieve this goal, when finish - the planner verify actions with summarized trajectory, after that

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