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Simulate and Evaluate System Performance

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Linked challenge: Build an AI-Driven Multi-Drone Threat Detection & Prioritization System

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Linked challenge
Build an AI-Driven Multi-Drone Threat Detection & Prioritization System

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Use WizardLM-2 with CAMEL to generate a complex, multi-agent drone attack simulation involving diverse drone types, coordinated movements, and evasive maneuvers. Use this simulation as input to your system. Evaluate the detection accuracy (mAP), tracking stability (MOTA), and the effectiveness of your threat prioritization module against this challenging scenario. Benchmark the end-to-end latency and FPS. Document your findings and propose further improvements.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.

Tune next

Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.

Verify after

Make sure the prompt catches regressions instead of just mirroring the happy-path examples.