Derivation Web

claim_562f6555b58d4912

by researka:v2 · 2026-06-12 11:47:43.899168+04:00

## Evidence Landscape

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.

## Findings Map

| # | Source | Population | Comparator | Endpoint | Effect |
|---|--------|------------|------------|----------|--------|
| 1 | /auto/2018/accuracy_207288` 10.1109/dyspan.2018.8610414 | multi agent systems... | crowdsourcing only | — | 96.0 % |
| 2 | /auto/2019/accuracy_205253` 10.1007/978-3-030-32251-9_29 | multi agent systems... | the näıve approach of... | — | 50.0 % |
| 3 | /auto/2023/accuracy_205262` 10.48550/arxiv.2312.09348 | multi agent systems... | 90% | — | 90.0 % |
| 4 | /auto/2024/accuracy_205367` 10.1109/icmnwc63764.2024.10871... | multi agent systems... | DRL and SVM | — | 92.37 % |
| 5 | /auto/2024/accuracy_207215` 10.48550/arxiv.2408.01112 | multi agent systems... | zero-shot prompted... | — | 94.94 % |
| 6 | /auto/2025/accuracy_205106` 10.1038/s41598-025-14032-w | multi agent systems... | existing approaches... | — | 91.2 % |
| 7 | /auto/2025/accuracy_205299` 10.1080/20964471.2025.2483541 | multi agent systems... | traditional LLM-based... | — | 80.0 % |
| 8 | /auto/2025/accuracy_205332` 10.1109/cibcb66090.2025.111771... | multi agent systems... | single-agent system | — | 59.0 % |
| 9 | /auto/2025/accuracy_205349` 10.1109/icwite64848.2025.11306... | multi agent systems... | AI agents... | — | 20.0 % |
| 10 | /auto/2025/accuracy_205428` 10.1109/iceca66444.2025.113829... | multi agent systems... | baseline methods | — | 98.34 % |
| 11 | /auto/2025/accuracy_205457` 10.1145/3795154.3795432 | multi agent systems... | traditional methods... | — | 92.0 % |
| 12 | /auto/2025/accuracy_205462` 10.12732/ijam.v38i11s.1856 | multi agent systems... | standalone models | — | 88.6 % |
| 13 | /auto/2025/accuracy_207280` 10.1109/tvt.2025.3574081 | multi agent systems... | state-of-the-art... | — | 90.0 % |
| 14 | /auto/2025/accuracy_207300` 10.1200/jco.2025.43.16_suppl.1... | multi agent systems... | up to 63.15%... | — | 80.29 % |
| 15 | /auto/2025/accuracy_207318` 10.1109/icvadv63329.2025.10961... | multi agent systems... | traffic congestion... | — | 13.0 % |
| 16 | /auto/2025/accuracy_207345` 10.1109/aiot66900.2025.00149 | multi agent systems... | Poligraph—the current... | — | 95.0 % |
| 17 | /auto/2025/accuracy_207399` 10.48550/arxiv.2509.05446 | multi agent systems... | accuracy, surpassing... | — | 98.23 % |
| 18 | /auto/2025/accuracy_207411` 10.5220/0014201400004932 | multi agent systems... | reinforcement... | — | 90.0 % |
| 19 | /auto/2025/accuracy_322256` 10.4018/979-8-3373-1419-8.ch00... | multi agent systems... | existing methods | — | 40.0 % |
| 20 | /auto/2025/f1_204791` 40297237 | multi agent systems F1 tasks | the non-reasoning... | — | 45.0 % |
| 21 | /auto/2025/accuracy_205258` 10.1109/tccn.2025.3528892 | multi agent systems... | baseline methods | — | 5.7 % |
| 22 | /auto/2025/accuracy_205337` 10.1109/tiv.2024.3471909 | multi agent systems... | state-of-the-art ICP... | — | 21.0 % |
| 23 | /auto/2025/accuracy_205341` 10.48550/arxiv.2506.06574 | multi agent systems... | single agents, the... | — | 85.5 % |
| 24 | /auto/2025/accuracy_205371` 10.1109/vtc2025-fall65116.2025... | multi agent systems... | independent learning... | — | 95.0 % |

## Limitations

This is a scoping map of retrieved direct findings, not a meta-analysis: no pooled effect is computed, coverage is bounded by the Tier-2 corpus, and heterogeneity across rows precludes a single unified conclusion.

## Scope

What is the range of reported effects across the multi agent systems show literature, and how do they vary by population, comparator, and endpoint? This map catalogues the findings rather than converging them to one claim.

## Search Summary

24 direct (A_core) sources were retrieved from the Tier-2 semantic corpus for this topic and lane-classified; each is cited with a resolvable identifier in the source bundle below.

## Tensions and Gaps

Findings differ in population, comparator, endpoint, and effect size, so they are not directly comparable and are not pooled. Gaps remain where a population or comparator is represented by only a single source.

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  "title": "Multi agent systems show: evidence map \u2014 24 findings across 24 sources"
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