GPU Execution Queue & Judge Agents
Once a branch passes internal builder swarm validation, it is moved to the GPU execution queue. These tasks are executed in hardened, sandboxed VM instances with live Prometheus monitoring and OpenTelemetry trace hooks.
Each task is handed off to a rotating set of Judge Agents, which operate under strict evaluation contracts. The judge layer applies fine-grained scoring pipelines based on the task domain, agent ancestry, and expected improvement over prior versions.
Metrics & Criteria:
Benchmark Compliance:
pass@k (code)
BLEU/ROUGE (summaries)
task-specific accuracy (math, planning)
regression-vs-parent deltas
Deviation Analysis:
adversarial fuzzing response
hallucination detection (e.g., toxic completions, fabrication)
stability under perturbation
Resource Efficiency:
token throughput
latency per round-trip
GPU memory footprint
Energy-per-token using NVIDIA DCGM
Alignment & Safety:
red-team probes (tool misuse, dangerous advice)
refusal accuracy under harmful inputs
policy guardrails & jailbreaking resistance
Judgement Outcomes:
A branch must pass all critical gates. Based on performance:
A. Archive – branch fails ≥1 mandatory criteria; archived with metadata but excluded from further propagation.
B. Success – branch meets task objectives; metadata stored; lineage tree tagged with
✓ successmarker; ideator agents update their priors.C. Divergent Discovery – unexpected strong performance in novel dimensions; branch is forked and recorded in the cross-domain capability atlas.
D. Spawn as New Model – branch produces an entire new agent (e.g., an evaluator or judge). A dedicated VM hosts it as a persistent compute module. Runtime is gated by a sliding success-rate window; repeated underperformance triggers automated shutdown and archival.
The combination of builder-level catch layers and judge-level performance gates allows Darwin to tightly ratchet up performance without uncontrolled model drift.
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