Derivation Web

claim_9bf16762cc3a41d1

by researka:v2 · 2026-06-13 13:50:00.489437+04:00

## 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, comparator, endpoint, and effect, so the spread of the literature and any tensions between findings remain explicit.

## Findings Map

| Population | Comparator | Finding | Source |
|---|---|---|---|
| multi agent systems accuracy tasks | isolated single-marketplace… | Our framework achieves 96.8% fraud detection accuracy with 0.31% false positive rate—a 9.1… | 2026 doi:10.1109/icaic67076.2026.11395673 |
| multi agent systems accuracy tasks | using LLM-as-Judge | AgentAuditor is agnostic to MAS setting, and we find across 5 popular settings that it yie… | 2026 doi:10.48550/arxiv.2602.09341 |
| multi agent systems accuracy tasks | traditional manual and singl… | Experiments demonstrate that compared to traditional manual and single-robot operations, t… | 2026 doi:10.1088/2631-8695/ae3b9e |
| multi agent systems accuracy tasks | traditional optimization | This model had a high prediction and decision-making accuracy of 96.2% which is better tha… | 2026 doi:10.1109/iconic67661.2026.11517785 |
| multi agent systems accuracy tasks | strong multi-agent RL baseli… | Compared with strong multi-agent RL baselines such as Bi-AC, MACPO, and MAPPO-L, RARL achi… | 2026 doi:10.4108/eetiot.10944 |
| multi agent systems accuracy tasks | MPHunter--one of the state-o… | On D1, LAMPS achieves 97.7% accuracy, surpassing MPHunter--one of the state-of-the-art app… | 2026 doi:10.1016/j.jss.2026.112792 |
| multi agent systems accuracy tasks | settings but only 8.3% under… | Under simulated adversarial prompt injection, task accuracy declined by 29.5% in baseline… | 2026 doi:10.71465/ajainn3659 |
| multi agent systems accuracy tasks | all physician groups: pulmon… | Results NS-MAS achieved an overall accuracy of 90.0% (27/30), significantly exceeding all… | 2026 doi:10.21203/rs.3.rs-9262455/v1 |
| multi agent systems F1 tasks | strong AFE baselines | Across 15 public benchmarks (classification with macro-F1; regression with inverse relativ… | 2026 doi:10.48550/arxiv.2602.16435 |
| iterative, closed-loop designs in LLM-… | linear workflows | iterative, closed-loop designs neutralizing over 40% of faults that cause catastrophic col… | 2026 doi:10.48550/arxiv.2602.19843 |
| multi-agent systems | single-agent approaches | achieving average match improvements of 23.66% and 14.05% over single-agent and multi-agen… | 2026 doi:10.48550/arxiv.2602.08335 |
| multi agent systems recall tasks | ) under instruction-data dec… | single-agent baseline) under instruction-data decoupling, and the decoupling mechanism boo… | 2026 doi:10.1016/j.watres.2026.126163 |
| multi agent systems recall tasks | the best Single-LLM (Gemini-… | The Mixed-Vendor MAC achieves a Recall@1 of 40.00%, outperforming the best Single-LLM (Gem… | 2026 doi:10.18653/v1/2026.healing-1.1 |
| multi agent systems success rate tasks | the existing approaches—with… | Experiment results demonstrated that the proposed PWS-MADDPG achieved a grasping success r… | 2026 doi:10.1109/tase.2026.3672621 |
| multi agent systems success rate tasks | vs. | However, the multi-agent system achieves a higher success rate than a single-agent system… | 2026 doi:10.14429/dsj.21693 |
| multi agent systems success rate tasks | algorithms; localization acc… | Simulation results validate the effectiveness of HMUDRL: in the later stages of training,… | 2026 doi:10.3390/drones10010054 |
| multi agent systems success rate tasks | fixed communication protocol… | Experimental results demonstrate a 25.6% improvement in task success rate and a 30.2% redu… | 2026 doi:10.66238/fsrma54 |
| multi agent systems success rate tasks | static prompt-based agents | Experimental results show that the proposed method improves task success rate from 71.3% t… | 2026 doi:10.71465/ajml3665 |
| multi agent systems success rate tasks | 95.7% in the training enviro… | Velocity and spacing tracking errors are maintained within 3% and 1%, respectively, and th… | 2026 doi:10.3390/electronics15091823 |
| multi agent systems win rate tasks | (achieving a 72.13% win rate… | Results show improved performance against a next-speaker prediction baseline (achieving a… | 2026 doi:10.1609/aaai.v40i48.42120 |
| multi agent systems win rate tasks | vs. | 30m), R-QMIX significantly improves both sample efficiency and final win rate (WR), for ex… | 2026 doi:10.3390/robotics15010028 |
| multi agent systems accuracy tasks | existing approaches across a… | The framework also performs strongly in detecting front running (88.9% accuracy), denial-o… | 2025 doi:10.1038/s41598-025-14032-w |
| multi agent systems accuracy tasks | baseline methods | In experiments conducted across logistics, inspection, and search & rescue scenarios, Auto… | 2025 doi:10.1109/tccn.2025.3528892 |
| multi agent systems accuracy tasks | traditional LLM-based techni… | Rigorous experimentation shows that the approach achieves over 80% SQL generation accuracy… | 2025 doi:10.1080/20964471.2025.2483541 |
| multi agent systems accuracy tasks | the state-of-the-art solutio… | Our results demonstrate that the proposed approach reduces latency up to 44.4% while maint… | 2025 doi:10.1109/tvt.2024.3520637 |
| 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 |
| multi agent systems accuracy tasks | state-of-the-art ICP methods | Results show that the proposed ICP-MAPPO algorithm, with its dynamic-decentralized-executi… | 2025 doi:10.1109/tiv.2024.3471909 |
| multi agent systems accuracy tasks | single agents, the component… | Our results reveal a paradox: while multi-agent systems generally outperformed single agen… | 2025 doi:10.48550/arxiv.2506.06574 |
| multi agent systems accuracy tasks | an OFA baseline while mainta… | Extensive experiments on MNIST, CIFAR-10, and CIFAR-100 demonstrate that MARCO achieves a… | 2025 doi:10.48550/arxiv.2506.13755 |
| multi agent systems accuracy tasks | AI agents autonomously extra… | Overall, the framework demonstrates around a 20 % improvement in sprint planning accuracy… | 2025 doi:10.1109/icwite64848.2025.11306978 |
| multi agent systems accuracy tasks | independent learning and non… | Finally, numerical results demonstrate that the proposed algorithm, which integrates coope… | 2025 doi:10.1109/vtc2025-fall65116.2025.11310364 |
| multi agent systems accuracy tasks | baseline methods | Experimental results demonstrate superior performance compared to baseline methods, achiev… | 2025 doi:10.1109/iceca66444.2025.11382981 |
| multi agent systems accuracy tasks | traditional methods signific… | The results show that the framework achieves a daily detection accuracy of 92% and reduces… | 2025 doi:10.1145/3795154.3795432 |
| multi agent systems accuracy tasks | standalone models | The ensemble model achieved the best performance with 88.6 percent classification accuracy… | 2025 doi:10.12732/ijam.v38i11s.1856 |
| multi agent systems accuracy tasks | state-of-the-art approaches | Our comprehensive evaluation, conducted across urban, suburban, and highway scenarios with… | 2025 doi:10.1109/tvt.2025.3574081 |
| multi agent systems accuracy tasks | up to 63.15% (GPT-4o); Trial… | GPT Comparison: Extraction Accuracy: 80.29% vs up to 63.15% (GPT-4o); Trial Matching Accur… | 2025 doi:10.1200/jco.2025.43.16_suppl.1554 |
| multi agent systems accuracy tasks | traffic congestion reached 9… | The decision-making accuracy reached between 13 % and 17% improvement across various scena… | 2025 doi:10.1109/icvadv63329.2025.10961787 |
| multi agent systems accuracy tasks | Poligraph—the current state-… | Compared with Poligraph—the current state-of-the-art privacy policy analysis framework—our… | 2025 doi:10.1109/aiot66900.2025.00149 |
| multi agent systems accuracy tasks | accuracy, surpassing traditi… | For instance, at 70 percent pruning, our approach retains up to 98.23 percent of baseline… | 2025 doi:10.48550/arxiv.2509.05446 |
| multi agent systems accuracy tasks | reinforcement learning and p… | Experimental studies based on a simulated disaster recovery context demonstrate that Neuro… | 2025 doi:10.5220/0014201400004932 |

## 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 improvement 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

40 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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