Derivation Web

source_31fc81c955394572

by researka:v2 · 2026-06-13 21:38:46.769149+04:00

**Selected angle:** `source`

## One-sentence thesis

Across 10 independently cited sources, the evidence converges on one bounded claim: multi-agent systems achieve higher accuracy than baselines across diverse tasks (detection, classification, prediction, extraction, etc.). Effect sizes vary by subgroup and are listed per source below rather than pooled into a single estimate.


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

## Why this is surprising

The surprise sits inside the cited receipt bundle; separate direct sources report measurable effects in multi agent systems accuracy tasks. Keep the claim inside that matched bundle until another receipt repeats it.

## 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/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_205258` (`A_core`) — In experiments conducted across logistics, inspection, and search & rescue scenarios, AutoHMA-LLM demonstrated a 5.7% improvement in task completion accuracy, a 46% reduction in communication steps, and a 31% decrease in token usage and API doi=10.1109/tccn.2025.3528892
- `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_205337` (`A_core`) — Results show that the proposed ICP-MAPPO algorithm, with its dynamic-decentralized-execution and centralized-training schemes, outperforms state-of-the-art ICP methods by 21% in terms of positioning accuracy, and it can reduce the communica doi=10.1109/tiv.2024.3471909
- `fact_id=multi_agent_systems/auto/2025/accuracy_205349` (`A_core`) — Overall, the framework demonstrates around a 20 % improvement in sprint planning accuracy and a 30% reduction in manual project tracking effort, introducing a novel multi-agent orchestration approach where AI agents autonomously extract, sy doi=10.1109/icwite64848.2025.11306978
- `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

## 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.
- Reviewer alignment: the repaired claim is narrowed to the cited receipt bundle 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

- _No direct opposing receipt was selected by this run. Treat that as a bundle limitation, not a claim that the wider literature has no counter-evidence._

{
  "article_type": "alpha_memo",
  "domain_slug": "ai_research",
  "researka_object_type": "submission",
  "researka_submission_id": "bc380652-3e0c-4e03-bc9e-89e81bb6a5de",
  "title": "Multi-agent systems achieve higher accuracy than baselines across diverse tasks (detection, classification, prediction, extraction, etc.)"
}