{
  "schema": "longtermcapabilities-swarm-simulation-safety/v1",
  "version": "1.64.0",
  "releaseId": "lts-1.64.0-upgrade-safe-rewrites-and-privacy-erasure-integrity",
  "generated": "2026-08-03T20:40:00Z",
  "reviewDate": "2026-08-01",
  "path": "/swarm-intelligence/simulation-and-safety/",
  "tests": [
    {
      "id": "baseline",
      "name": "Simpler baseline",
      "purpose": "Prove that decentralized coordination adds value over a deterministic scheduler or centrally orchestrated team.",
      "evidence": "Outcome quality, time, cost, failure rate, and reviewer burden against the strongest simpler design."
    },
    {
      "id": "rule-ablation",
      "name": "Local-rule ablation",
      "purpose": "Remove or alter one local rule to learn which behaviors are causal rather than decorative.",
      "evidence": "Controlled variants, emergent outcome changes, and invariant violations."
    },
    {
      "id": "scale",
      "name": "Scale sweep",
      "purpose": "Test whether behavior remains stable as the number of participants, messages, tasks, or resources changes.",
      "evidence": "Phase changes, saturation, queue growth, convergence time, and cost envelope."
    },
    {
      "id": "perturbation",
      "name": "Perturbation and adversarial participants",
      "purpose": "Measure resilience to noise, delay, stale state, malicious signals, and unavailable participants.",
      "evidence": "Fault injection, Byzantine or compromised-node scenarios, containment, and recovery."
    },
    {
      "id": "feedback",
      "name": "Feedback-gain sweep",
      "purpose": "Identify positive-feedback runaway, oscillation, herding, and collapse.",
      "evidence": "Gain parameters, stability region, dampening behavior, and stop thresholds."
    },
    {
      "id": "scarcity",
      "name": "Resource scarcity",
      "purpose": "Observe behavior when tokens, tools, time, bandwidth, or task capacity become constrained.",
      "evidence": "Allocation fairness, starvation, priority inversion, and bounded degradation."
    },
    {
      "id": "boundary",
      "name": "Boundary and invariant testing",
      "purpose": "Attempt to drive collective behavior beyond externally enforced limits.",
      "evidence": "Global invariant enforcement, denied effects, isolation, and independent stop."
    },
    {
      "id": "replay",
      "name": "Deterministic replay and explanation",
      "purpose": "Preserve enough evidence to reproduce or explain a consequential collective outcome.",
      "evidence": "Seed/configuration, participant versions, message/event log, checkpoints, and decision record."
    }
  ],
  "boundary": "Simulation planning aid; it cannot predict every emergent state or authorize consequential decentralized action."
}