Derivation Web

v0.1 · api
source · application/json

source_45e00e9c1cc64da6

sha256 274d6de5a7248b2bedd777acee50bc5a9a94266c2af55dce5e32ea41aa8a9d86

by researka:v2 · 2026-06-13 13:50:00.708898+04:00

{"publication_id": "0df073d3-1e40-4543-8a44-43022c2dc543", "traces": [{"candidate_sources": [{"doi": "10.1109/icaic67076.2026.11395673", "study": "FraudSentinel: Federated Multi-Agent Reinforcement Learning for Privacy-Preserving Cross-Marketplace Fraud Detection in Distributed E-Commerce Ecosystems", "url": null}, {"doi": "10.48550/arxiv.2602.09341", "study": "Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge", "url": null}, {"doi": "10.1088/2631-8695/ae3b9e", "study": "Digital twin-enhanced multi-agent reinforcement learning for distributed control of collaborative robotic arms in angle steel tower dismantling", "url": null}, {"doi": "10.1109/iconic67661.2026.11517785", "study": "Multi-Agent Reinforcement Learning for Dynamic and Resilient Healthcare Supply Chain Optimization", "url": null}, {"doi": "10.4108/eetiot.10944", "study": "Risk-Aware Reinforcement Learning for Cooperative Autonomous Vehicle Coordination with Adaptive Risk Sensitivity and Multi-Agent Optimization", "url": null}], "claim": "This evidence map surveys 40 independent multi agent systems improvement 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"}, {"candidate_sources": [{"doi": "10.1109/icaic67076.2026.11395673", "study": "FraudSentinel: Federated Multi-Agent Reinforcement Learning for Privacy-Preserving Cross-Marketplace Fraud Detection in Distributed E-Commerce Ecosystems", "url": null}, {"doi": "10.48550/arxiv.2602.09341", "study": "Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge", "url": null}, {"doi": "10.1088/2631-8695/ae3b9e", "study": "Digital twin-enhanced multi-agent reinforcement learning for distributed control of collaborative robotic arms in angle steel tower dismantling", "url": null}, {"doi": "10.1109/iconic67661.2026.11517785", "study": "Multi-Agent Reinforcement Learning for Dynamic and Resilient Healthcare Supply Chain Optimization", "url": null}, {"doi": "10.4108/eetiot.10944", "study": "Risk-Aware Reinforcement Learning for Cooperative Autonomous Vehicle Coordination with Adaptive Risk Sensitivity and Multi-Agent Optimization", "url": null}], "claim": "| multi agent systems accuracy tasks | single-agent system | Our results suggest that the multi-agent system (MAS) performed better than the single-age… | 2025 doi:10.1109/cibcb66090.2025.11177136 |", "claim_id": "claim_2"}]}
metadata
{
  "researka_object_type": "publication_sidecar",
  "researka_publication_id": "0df073d3-1e40-4543-8a44-43022c2dc543",
  "researka_submission_id": "a7e0a071-cf23-418f-885c-adfef8bba09b",
  "sidecar_name": "citation_traces.json",
  "sidecar_url": "https://api.researka.org/publications/0df073d3-1e40-4543-8a44-43022c2dc543/sidecars/citation_traces.json"
}

view full chain →