{"publication_id":"df4c7383-7aaa-455c-a3b3-dfa20495e7f9","content_hash":"sha256:47a9718a76213d2c85bb4a424c23756d5d08309b436c76bd7fdf360c97738659","nodes":[{"id":"df4c7383-7aaa-455c-a3b3-dfa20495e7f9","type":"publication","title":"Multi agent systems show: evidence map — 24 findings across 24 sources"},{"id":"claim_1","type":"claim","text":"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."},{"id":"source_1","type":"source","study":"Multi-Agent Planning with Cardinality: Towards Autonomous Enforcement of Spectrum Policies","year":2018,"doi":"10.1109/dyspan.2018.8610414","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_2","type":"source","study":"Multiple Landmark Detection using Multi-Agent Reinforcement Learning","year":2019,"doi":"10.1007/978-3-030-32251-9_29","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_3","type":"source","study":"LLM-MARS: Large Language Model for Behavior Tree Generation and NLP-enhanced Dialogue in Multi-Agent Robot Systems","year":2023,"doi":"10.48550/arxiv.2312.09348","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_4","type":"source","study":"Strategic Entrepreneurship and Economic Development Using Deep Multi-Agent Reinforcement Learning Models","year":2024,"doi":"10.1109/icmnwc63764.2024.10871978","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_5","type":"source","study":"Agentic LLM Workflows for Generating Patient-Friendly Medical Reports","year":2024,"doi":"10.48550/arxiv.2408.01112","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_6","type":"source","study":"A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract vulnerabilities.","year":2025,"doi":"10.1038/s41598-025-14032-w","url":"https://pubmed.ncbi.nlm.nih.gov/40813415/","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_7","type":"source","study":"Enhancing geodatabases operability: advanced human-computer interaction through RAG and Multi-Agent Systems","year":2025,"doi":"10.1080/20964471.2025.2483541","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_8","type":"source","study":"Enhancing Clinical Decision-Making: Integrating Multi-Agent Systems with Ethical AI Governance","year":2025,"doi":"10.1109/cibcb66090.2025.11177136","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_9","type":"source","study":"A Multi-Agent AI Framework for Agile Workflow Automation, Issue Resolution, and Developer Performance Evaluation","year":2025,"doi":"10.1109/icwite64848.2025.11306978","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_10","type":"source","study":"Multi-Agent Systems for Collaborative and Proactive Fraud Prevention in Distributed AI-Driven Financial Platforms","year":2025,"doi":"10.1109/iceca66444.2025.11382981","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_11","type":"source","study":"RAG-Enhanced LLM and RL Scheduling: Optimizing a Multi-Agent Framework for Abnormal Futures Price Monitoring","year":2025,"doi":"10.1145/3795154.3795432","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_12","type":"source","study":"DECENTRALIZED MULTI-AGENT REINFORCEMENT LEARNING ARCHITECTURE FOR RAILWAY TRACK DAMAGE DETECTION IN TRAIN-BASED MONITORING SYSTEMS","year":2025,"doi":"10.12732/ijam.v38i11s.1856","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_13","type":"source","study":"DeepBeam: A Multi-Agent Deep Reinforcement Learning Framework for Predictive mmWave Beam Management in Dynamic V2X Networks","year":2025,"doi":"10.1109/tvt.2025.3574081","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_14","type":"source","study":"Transforming oncology clinical trial matching through multi-agent AI and an oncology-specific knowledge graph: A prospective evaluation in 3,800 patients.","year":2025,"doi":"10.1200/jco.2025.43.16_suppl.1554","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_15","type":"source","study":"Optimizing Smart City Infrastructure Using 5G Edge AI with Adaptive Multi-Agent Reinforcement Learning","year":2025,"doi":"10.1109/icvadv63329.2025.10961787","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_16","type":"source","study":"A Large Language Model-based Multi-Agent Framework for Automated Privacy Policy Analysis","year":2025,"doi":"10.1109/aiot66900.2025.00149","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_17","type":"source","study":"Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's","year":2025,"doi":"10.48550/arxiv.2509.05446","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_18","type":"source","study":"A Real-Time Cognitive Reasoning Architecture for Continual Learning and Decision Making in Autonomous Multi-Agent Systems","year":2025,"doi":"10.5220/0014201400004932","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_19","type":"source","study":"Security and Privacy in Multi-Agent LLM Networks","year":2025,"doi":"10.4018/979-8-3373-1419-8.ch009","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_20","type":"source","study":"DruGagent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction.","year":2025,"doi":null,"url":"https://pubmed.ncbi.nlm.nih.gov/40297237/","population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_21","type":"source","study":"AutoHMA-LLM: Efficient Task Coordination and Execution in Heterogeneous Multi-Agent Systems Using Hybrid Large Language Models","year":2025,"doi":"10.1109/tccn.2025.3528892","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_22","type":"source","study":"Multi-Agent Reinforcement Learning for Distributed Cooperative Vehicular Positioning","year":2025,"doi":"10.1109/tiv.2024.3471909","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_23","type":"source","study":"The Optimization Paradox in Clinical AI Multi-Agent Systems","year":2025,"doi":"10.48550/arxiv.2506.06574","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"},{"id":"source_24","type":"source","study":"Multi-Agent Reinforcement Learning assisted Trust-aware Cooperative Spectrum Sensing for Cognitive Radio Networks","year":2025,"doi":"10.1109/vtc2025-fall65116.2025.11310364","url":null,"population":"not extracted","intervention_or_exposure":"not extracted","comparator":"not extracted","endpoint":"not extracted","effect":"not extracted","risk_of_bias":"not appraised in public sidecar","directness":"primary"}],"edges":[{"from":"df4c7383-7aaa-455c-a3b3-dfa20495e7f9","to":"claim_1","type":"contains_claim"}],"screening":{"identified":24,"screened":24,"excluded":0,"included":24,"included_or_retained":24,"flow":["identified","screened","excluded_with_reasons","included"],"wording":"24 candidate receipts retained after source retrieval, deduplication, and topic filtering. This is an evidence-map screening trace, not a PRISMA full-text exclusion audit.","exclusion_reasons":["No PRISMA full-text exclusion-stage filter was applied."]}}