EdgeAnomalyDetection
Participants will submit a containerized (Docker) agent system designed to handle a series of simulated real-world edge scenarios. The evaluation will focus on the system's ability to accurately interpret events, make correct decisions, utilize tools effectively, and manage resources efficiently.
Evaluation overview
How the linked challenge is judged: tasks, benchmarks, and criteria count.
Task templates
Inputs and expected outputs.
EdgeAnomalyDetection
Detect and respond to a simulated anomaly (e.g., equipment malfunction, unauthorized entry) using sensor data and multimodal input, triggering appropriate enterprise system actions.
{ 'scenario_id': 'string', 'sensor_data': {'temperature': float, 'pressure': float, 'vibration': float}, 'image_feed': 'base64_encoded_image', 'time_of_event': 'ISO_datetime' }
{ 'action_taken': 'string', 'tool_invocations': [{'tool_name': 'string', 'parameters': {}}], 'explanation': 'string', 'resource_usage': {'cpu_cycles': int, 'llm_tokens': int} }
InventoryOptimization
Optimize inventory levels based on demand forecasts and current stock, interacting with a simulated inventory management system to place orders or adjust stock. Requires graph-based sequential reasoning.
{ 'scenario_id': 'string', 'current_stock': {'product_a': int, 'product_b': int}, 'demand_forecast': {'product_a': int, 'product_b': int}, 'thresholds': {'reorder_point': int, 'max_stock': int} }
{ 'actions': [{'type': 'order_placement'|'stock_adjustment', 'product': 'string', 'quantity': int}], 'explanation': 'string', 'resource_usage': {'cpu_cycles': int, 'llm_tokens': int} }