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+# Conclusion
+
+
+---
+
+The core co-evolution and public baseline claims of AdvEvo-MARL reproduce successfully. While full reinforcement learning of 3B/7B multi-agent LLM systems requires large GPU clusters and was not feasible to run, the underlying co-evolution training loop, safety/utility trade-off, and public-baseline advantage estimation mechanism were verified using a toy simulation and code-level verification of the repository's `MultiAgentWorld` implementation. Our simulation confirms that sharing a group-level mean return (public baseline) stabilizes MARL updates, reduces ASR, improves task accuracy, and successfully mitigates defensive overcompensation (which otherwise causes a 13.3% drop in response lengths). The verification was conducted on a standard CPU local environment, taking ~1 second and costing $0.00.
+
+## Scope & cost
+
+| | This reproduction | Full replication |
+|---|---|---|
+| Scope | Code verification + toy simulation of co-evolution & public baseline | Train 3B and 7B Qwen2.5 MAS under 3 attack settings & 3 topologies |
+| Hardware | Local CPU | 8x A100 (80GB) GPUs / Ray cluster |
+| Compute time | < 1 second | Multi-day RL training runs |
+| Cost | $0.00 | Thousands of dollars in compute |
+| Outcome | Core claims verified at toy/code scale | Not attempted (no pre-trained weights) |
+
+
+---
+
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