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Agentic Competition and Benchmarking

A core design principle of Luminar Network is that intelligence quality must emerge from open competition rather than static model deployment. Instead of relying on centrally trained monolithic models, Luminar leverages Bittensor’s incentive mechanism to continuously benchmark, rank, and economically reward specialized agentic behaviors under realistic operating conditions.

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The Role of Benchmarks

Benchmarks serve three critical functions within the network:

  • Objective Performance Measurement: Establishing standardized, reproducible metrics for evaluating agent quality under production-like workloads.

  • Economic Signal Generation: Translating technical performance into incentive-aligned rewards that drive miner optimization and specialization.

  • Capability Evolution: Enabling rapid iteration and emergence of new intelligence primitives without centralized model governance.

This framework ensures that only agents demonstrating operational reliability, temporal consistency, and latency-aware performance are promoted within the subnet and exposed to downstream applications.

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3.1 The Benchmark: Luminar Multi-Object Tracking and Anomaly Benchmark (L-MOT)

To operationalize agentic competition, we introduce the Luminar Multi-Object Tracking and Anomaly Benchmark (L-MOT). L-MOT evaluates a miner’s ability to maintain persistent situational awareness across continuous video streams while simultaneously detecting security-relevant behavioral anomalies.

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Testing Environment

Miners are provided with curated test video segments that emulate real-world surveillance challenges, including:

  • Occlusions and variable lighting.

  • Camera motion and dense multi-object scenes.

Each miner must execute two concurrent tasks under strict latency constraints:

  1. Multi-Object Tracking: Maintain consistent identities for dynamic entities (e.g., vehicles, individuals, assets) across frames, camera transitions, and partial occlusions.

  2. Event and Anomaly Recognition: Detect and classify predefined anomalies (e.g., unattended objects, perimeter breaches, abnormal dwell time) with high precision.

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3.1.1 Evaluation Metrics and Scoring

Validators assess miner outputs using the Higher Order Tracking Accuracy (HOTA) metric, augmented with a latency-sensitive penalty function to discourage slow solutions.

Scoreagent=HOTA×(1−LatencyPenalty)Score_{agent} = HOTA \times (1 - LatencyPenalty)

Understanding HOTA

HOTA jointly captures two complementary dimensions of tracking quality:

  • Detection Accuracy (DetA): Measures the agent’s ability to correctly identify and localize objects present in each frame.

  • Association Accuracy (AssA): Measures the agent’s ability to preserve identity consistency across time, ensuring an entity is correctly associated with its later appearances despite scale variations or viewpoint changes.

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Operational Standards

The latency penalty incorporates end-to-end inference delay relative to real-time thresholds. By coupling spatial accuracy, temporal consistency, and execution efficiency into a single economic signal, L-MOT incentivizes miners to optimize for production-grade intelligence rather than academic performance.

This ensures that the intelligence surfaced to the Application Layer meets the reliability, responsiveness, and evidentiary standards required for operational security and forensic reconstruction.

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