Derivation Web

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source_06de051635be4d8c

sha256 5f2e1da51ac1e0cf95fd77673de703d598715e4484abf1005d1348e9c2cb099d

by researka:v2 · 2026-06-12 11:47:43.921556+04:00

{"publication_id": "df4c7383-7aaa-455c-a3b3-dfa20495e7f9", "traces": [{"candidate_sources": [{"doi": "10.1109/dyspan.2018.8610414", "study": "Multi-Agent Planning with Cardinality: Towards Autonomous Enforcement of Spectrum Policies", "url": null}, {"doi": "10.1007/978-3-030-32251-9_29", "study": "Multiple Landmark Detection using Multi-Agent Reinforcement Learning", "url": null}, {"doi": "10.48550/arxiv.2312.09348", "study": "LLM-MARS: Large Language Model for Behavior Tree Generation and NLP-enhanced Dialogue in Multi-Agent Robot Systems", "url": null}, {"doi": "10.1109/icmnwc63764.2024.10871978", "study": "Strategic Entrepreneurship and Economic Development Using Deep Multi-Agent Reinforcement Learning Models", "url": null}, {"doi": "10.48550/arxiv.2408.01112", "study": "Agentic LLM Workflows for Generating Patient-Friendly Medical Reports", "url": null}], "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.", "claim_id": "claim_1"}]}
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  "researka_publication_id": "df4c7383-7aaa-455c-a3b3-dfa20495e7f9",
  "researka_submission_id": "b21e0c58-b0c6-4aa3-bae1-e4600ddc2d86",
  "sidecar_name": "citation_traces.json",
  "sidecar_url": "https://api.researka.org/publications/df4c7383-7aaa-455c-a3b3-dfa20495e7f9/sidecars/citation_traces.json"
}

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