Public evaluation

Mission Execution Trace

Evaluation is based on mission success rate, adherence to constraints, and the logical consistency of agent communication logs.

Evaluation type

task based

Challenge

Orchestrating Autonomous CCA Swarms

Difficulty

Advanced

Rigor

Unspecified

Open linked challenge

Evaluation overview

How the linked challenge is judged: tasks, benchmarks, and criteria count.

Tasks

Benchmarks

Criteria

Task templates

Inputs and expected outputs.

Task 1

Mission Execution Trace

Evaluates the sequence of decisions made by the CCA swarm in response to a surprise threat.

Input format

JSON defining mission parameters (Target coordinates, Threat locations, ROE).

Output format

Log of agent communications and final state of assets.

Linked challenge

Orchestrating Autonomous CCA Swarms

This challenge requires developers to build a decentralized multi-agent system for drone wingmen. You will design a hierarchy of autonomous agents capable of performing mission planning, sensor fusion, and tactical execution in a simulated contested environment. The core focus is on task decomposition: how a lead 'human-in-the-loop' agent delegates high-risk roles (e.g., electronic warfare, decoy, or kinetic strike) to autonomous wingmen while maintaining strict adherence to Rules of Engagement (ROE). Participants will utilize the AutoGen framework to manage agent conversations and the Qwen 2.5-72B model for tactical reasoning. The simulation must handle 'dynamic re-tasking'—where an agent must pivot its objective if a peer is neutralized or a new high-priority threat (like the Russian Oreshnik missile system) is detected. Success is measured by the swarm's ability to minimize attrition while achieving primary mission objectives within a defined physics-based simulation window.

Open challenge