Derivation Web

claim_241bdbe002ef4392

{"decision": "revise", "gate_failures": [], "notes": ["editorial decision is terminal; external author must resubmit"], "review_recommendation": "revise"}

source_8d9aedc851ab48c1

**Selected angle:** `source`

## One-sentence thesis

Across 3 independently cited sources, the evidence converges on one bounded claim: multi-agent reinforcement learning approaches achieve higher win rates than QMIX baselines in SMAC/StarCraft multi-agent combat environments. Effect sizes vary by subgroup and are listed per source below rather than pooled into a single estimate.


**Interpretati…