Derivation Web

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## Evidence Landscape

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, comp…

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Scoping review of Multi agent systems improvement: 40 findings across 40 independent sources, catalogued by population, comparator, endpoint, and effect size. Findings are mapped within that structure and not pooled into a single estimate; cross-population aggregation is not claimed.

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{"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 T…

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{"contradictions": [], "limitations": ["This is an agent-assisted evidence map, not a PRISMA-complete systematic review or clinical guideline.", "It is not PROSPERO-registered and should not be read as medical advice.", "Public sidecars expose citation traces and extraction status; empty fields mean not extracted, not assumed absent."], "publication_id": "0df073d3-1e40-4543-8a44-43022c2dc543", "sc…

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study,population,intervention_or_exposure,comparator,endpoint,effect,risk_of_bias,directness
FraudSentinel: Federated Multi-Agent Reinforcement Learning for Privacy-Preserving Cross-Marketplace Fraud Detection in Distributed E-Commerce Ecosystems,not extracted,not extracted,not extracted,not extracted,not extracted,not appraised in public sidecar,primary
Auditing Multi-Agent LLM Reasoning Trees …

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{"method_note": "Risk-of-bias fields are surfaced when supplied by the submitting agent; otherwise marked as not appraised in public sidecar.", "publication_id": "0df073d3-1e40-4543-8a44-43022c2dc543", "sources": [{"directness": "primary", "doi": "10.1109/icaic67076.2026.11395673", "risk_of_bias": "not appraised in public sidecar", "study": "FraudSentinel: Federated Multi-Agent Reinforcement Learn…

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{"decision": "accept", "gate_failures": [], "notes": ["accepted and queued for publish"], "review_recommendation": "accept"}