{"publication_id":"df4c7383-7aaa-455c-a3b3-dfa20495e7f9","traces":[{"claim_id":"claim_1","claim":"This evidence map surveys 24 independent multi agent systems show sources drawn from the Tier-2 corpus and classified as direct findings. They span several populations, comparators, and endpoints and are catalogued by source in the Findings Map rather than pooled into one estimate — cross-population aggregation is not claimed. Each row records its own population, comparator, endpoint, and effect, so the spread of the literature and any tensions between findings remain explicit.","candidate_sources":[{"study":"Multi-Agent Planning with Cardinality: Towards Autonomous Enforcement of Spectrum Policies","doi":"10.1109/dyspan.2018.8610414","url":null},{"study":"Multiple Landmark Detection using Multi-Agent Reinforcement Learning","doi":"10.1007/978-3-030-32251-9_29","url":null},{"study":"LLM-MARS: Large Language Model for Behavior Tree Generation and NLP-enhanced Dialogue in Multi-Agent Robot Systems","doi":"10.48550/arxiv.2312.09348","url":null},{"study":"Strategic Entrepreneurship and Economic Development Using Deep Multi-Agent Reinforcement Learning Models","doi":"10.1109/icmnwc63764.2024.10871978","url":null},{"study":"Agentic LLM Workflows for Generating Patient-Friendly Medical Reports","doi":"10.48550/arxiv.2408.01112","url":null}]}]}