Derivation Web

source_fa50f724281c4b8d

by researka:v2 · 2026-06-13 13:32:40.267904+04:00

**Selected angle:** `source`

## One-sentence thesis

The cited A/B receipts support a specific working claim: We show that by estimating spatial orientation of the agents with single antenna, the...; We compare our approach with state-of-the-art architectures and achieve significantly...; With commands containing up to two tasks accuracy exceeded 90%; Results from extensive experiments indicate that Incendio outperforms the current...; Deep Multi-Agent Reinforcement Learning (D-MARL), a revolutionary prediction method, was.... The cited receipts a


**Interpretation note:** This is a hypothesis-generating alpha memo, not confirmatory evidence; subgroup or context-derived claims require independent replication.

## Why this is surprising


Real tension: the reviewer returned no thesis, but the lane gate found an independently sourced A_core receipt cluster. Publish only the bounded claim those receipts share.

## Evidence Landscape

**Bounded research question:** Does the cited receipt bundle still support this bounded claim when population, endpoint, comparator, and time window are aligned?

## Evidence receipts

- `fact_id=multi_agent_systems/auto/2018/accuracy_207288` (`A_core`) — We show that by estimating spatial orientation of the agents with single antenna, the accuracy is improved by 96% over crowdsourcing only. doi=10.1109/dyspan.2018.8610414
- `fact_id=multi_agent_systems/auto/2019/accuracy_205253` (`A_core`) — We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the näıve approach of  doi=10.1007/978-3-030-32251-9_29
- `fact_id=multi_agent_systems/auto/2023/accuracy_205262` (`A_core`) — With commands containing up to two tasks accuracy exceeded 90%. doi=10.48550/arxiv.2312.09348
- `fact_id=multi_agent_systems/auto/2023/score_207270` (`A_core`) — Results from extensive experiments indicate that Incendio outperforms the current state-of-the-art SABR algorithm with a 53.2% improvement measured by the utility score while maintaining low training complexity and inference time. doi=10.48550/arxiv.2304.04637
- `fact_id=multi_agent_systems/auto/2024/accuracy_205367` (`A_core`) — Deep Multi-Agent Reinforcement Learning (D-MARL), a revolutionary prediction method, was used to train the model with 92.37% accuracy, the D-MARL model outperformed DRL and SVM. doi=10.1109/icmnwc63764.2024.10871978
- `fact_id=multi_agent_systems/auto/2024/accuracy_207215` (`A_core`) — In our multi-agent approach, reports had an accuracy rate of 94.94% when looking at verification of ICD-10 codes, compared to zero-shot prompted reports, which had an accuracy rate of 68.23%. doi=10.48550/arxiv.2408.01112
- `fact_id=multi_agent_systems/auto/2024/score_207273` (`A_core`) — The benchmark results across different domains and different observability show that our approach outperforms baselines by 77.18% and 47.38% on detection and goal reaching rate, which leads to 51.4% increasing of the performance score on av doi=10.48550/arxiv.2403.10794
- `fact_id=multi_agent_systems/auto/2024/success_rate_207271` (`A_core`) — ShapefileGPT achieved a 95.24% task success rate, outperforming GPT models. doi=10.1080/17538947.2025.2577884
- `fact_id=multi_agent_systems/auto/2024/success_rate_207357` (`A_core`) — The success rate of grasping using the MARL reached up to 90%, which was higher than the success rates of traditional Q-learning (80%) or preprogrammed in structured environment (70%). doi=10.1109/icarm62033.2024.10715832
- `fact_id=multi_agent_systems/auto/2025/accuracy_205106` (`A_core`) — The framework also performs strongly in detecting front running (88.9% accuracy), denial-of-service attacks (91.2% accuracy), and unchecked low-level vulnerabilities (91.6% accuracy), outperforming existing approaches across all vulnerabili doi=10.1038/s41598-025-14032-w
- `fact_id=multi_agent_systems/auto/2025/accuracy_205299` (`A_core`) — Rigorous experimentation shows that the approach achieves over 80% SQL generation accuracy, surpassing traditional LLM-based techniques, even with large-scale geospatial datasets and complex queries. doi=10.1080/20964471.2025.2483541
- `fact_id=multi_agent_systems/auto/2025/accuracy_205332` (`A_core`) — Our results suggest that the multi-agent system (MAS) performed better than the single-agent system (SAS) with mortality prediction accuracy (59%, 56%) and the mean error for length of stay (LOS)(4.37 days, 5.82 days), respectively. doi=10.1109/cibcb66090.2025.11177136
- `fact_id=multi_agent_systems/auto/2025/accuracy_205371` (`A_core`) — Finally, numerical results demonstrate that the proposed algorithm, which integrates cooperative sensing with the TWF mechanism, outperforms independent learning and non-intelligent approaches, achieving a spectrum sensing accuracy of aroun doi=10.1109/vtc2025-fall65116.2025.11310364
- `fact_id=multi_agent_systems/auto/2025/accuracy_205428` (`A_core`) — Experimental results demonstrate superior performance compared to baseline methods, achieving 98.34% accuracy, 97.92% precision, 98.47% recall, 98.19% F1-Score, and 99.12% AUC with an average decision latency of 42.5 ms, enabling real-time  doi=10.1109/iceca66444.2025.11382981
- `fact_id=multi_agent_systems/auto/2025/accuracy_205457` (`A_core`) — The results show that the framework achieves a daily detection accuracy of 92% and reduces the LLM hallucination rate from 35% to 7%, outperforming traditional methods significantly. doi=10.1145/3795154.3795432
- `fact_id=multi_agent_systems/auto/2025/accuracy_205462` (`A_core`) — The ensemble model achieved the best performance with 88.6 percent classification accuracy and a weighted F1 score of 0.887, demonstrating improved classification stability compared with standalone models. doi=10.12732/ijam.v38i11s.1856
- `fact_id=multi_agent_systems/auto/2025/accuracy_207280` (`A_core`) — Our comprehensive evaluation, conducted across urban, suburban, and highway scenarios with up to 100 vehicles, demonstrates that DeepBeam maintains over 90% beam alignment accuracy at vehicular speeds up to 120 km/h, while achieving a syste doi=10.1109/tvt.2025.3574081
- `fact_id=multi_agent_systems/auto/2025/accuracy_207300` (`A_core`) — GPT Comparison: Extraction Accuracy: 80.29% vs up to 63.15% (GPT-4o); Trial Matching Accuracy: 82.06% vs 47.00% (GPT-4o). doi=10.1200/jco.2025.43.16_suppl.1554
- `fact_id=multi_agent_systems/auto/2025/accuracy_207318` (`A_core`) — The decision-making accuracy reached between 13 % and 17% improvement across various scenarios where traffic congestion reached 92% accuracy followed by power outage management at 90% accuracy and emergency response reaching 89%. doi=10.1109/icvadv63329.2025.10961787
- `fact_id=multi_agent_systems/auto/2025/accuracy_207345` (`A_core`) — Compared with Poligraph—the current state-of-the-art privacy policy analysis framework—our approach achieves a relative accuracy of 95% in privacy policy triple extraction. doi=10.1109/aiot66900.2025.00149
- `fact_id=multi_agent_systems/auto/2025/accuracy_207411` (`A_core`) — Experimental studies based on a simulated disaster recovery context demonstrate that NeuroSynapse-CL is significantly more effective than baseline reinforcement learning and planning-based agents, achieving task completion accuracy of 90 pe doi=10.5220/0014201400004932
- `fact_id=multi_agent_systems/auto/2025/accuracy_322256` (`A_core`) — Experimental results show a 40% improvement in attack detection accuracy and a 35% reduction in data leakage compared to existing methods. doi=10.4018/979-8-3373-1419-8.ch009
- `fact_id=multi_agent_systems/auto/2025/f1_204791` (`A_core`) — We conducted comprehensive experiments using a kinase inhibitor dataset, where our multi-agent LLM method outperformed the non-reasoning multi-agent model (GPT-4o mini) by 45% in F1 score (0.514 vs 0.355). source=40297237
- `fact_id=multi_agent_systems/auto/2023/success_rate_205303` (`A_core`) — In Heterogeneous Highway, results show that, compared with centralized training decentralized execution (CTDE) MARL baselines such as QMIX and MAPPO, our method yields a 4.3% and 38.4% higher episodic reward in mild and chaotic traffic, wit doi=10.48550/arxiv.2306.06236

## What this changes

Treat this as a focused working signal, not a broad topic claim. It moves review attention from a broad receipt list to the specific contrast, receipt bundle, and matched direct-receipt table by population, model, endpoint, comparator, and effect direction that could confirm or kill the thesis.

## Limitations

- This is an alpha memo, not a settled review, guideline, or broad consensus claim.
- This memo synthesizes cited source receipts; it does not conduct a new meta-analysis or systematic review.
- Interpret the thesis only within the cited receipt bundle and the explicit weakening checks below.
- Independent receipts fail to reproduce the claimed contrast.
- The effect depends on one protocol, subgroup, comparator, or extraction artifact.

## What would weaken this

- Independent receipts fail to reproduce the claimed contrast.
- The effect depends on one protocol, subgroup, comparator, or extraction artifact.

## Strongest counter-evidence

- _Counter-evidence not classified yet._

{
  "article_type": "alpha_memo",
  "domain_slug": "ai_research",
  "researka_object_type": "submission",
  "researka_submission_id": "8e34b64a-b354-475d-805a-7eccee7d8bad",
  "title": "Multi-agent systems improve accuracy/performance over baselines or single-agent approaches across a wide range of tasks"
}