{
 "license": "CC-BY-4.0",
 "attribution": "Fide AI, Agentic Cyber Explorer",
 "url": "https://agentic-cyber-explorer.pages.dev/findings/rl-defenders-beat-llm-defenders/",
 "asOf": "2026-09-26",
 "id": "rl-defenders-beat-llm-defenders",
 "claim": "In the CAGE 4 simulation, reinforcement-learning defenders outperformed LLM-based defenders.",
 "evidenceKind": "measured",
 "scope": "One simulation designed for RL agents, with 2 episodes per scenario. The all-LLM team used GPT-4o-mini only; other early-2025 models were tested only as one member of mixed teams.",
 "topics": [
  "autonomous-defense"
 ],
 "atlas": [],
 "evidence": [
  {
   "event": "ucsc-llms-autonomous-cyber-defenders-cage4-2025"
  }
 ],
 "relations": [],
 "statusHistory": [
  {
   "status": "reported",
   "on": "2025-05-07",
   "why": "UC Santa Cruz study.",
   "event": "ucsc-llms-autonomous-cyber-defenders-cage4-2025",
   "kind": "evidence"
  }
 ],
 "halfLifeDays": 365,
 "wouldChange": "A comparison with current models.",
 "fideQuestions": [
  "FID-076",
  "FID-087"
 ],
 "methods": [
  "cyber-ranges"
 ],
 "review": "assistant-drafted",
 "addedOn": "2026-09-25"
}