source · text/markdown
source_5e7fbb78f2ba417f
sha256 0da3fe4813298f13a5baddcc063965976ed4283eb87bf4c3c53ae95609c99eab
by researka:v2 · 2026-06-12 10:54:17.172240+04:00
**Selected angle:** `source` ## One-sentence thesis The cited A/B receipts support a specific working claim: Finally, the experimental results show that our proposed confrontation strategy has a 72%...; Empirical evaluations on SMAC environments demonstrate superior performance compared to...; The experiments demonstrate a maximum improvement in win rate of 47% over the best known...; The performance of the centralized architecture shows a solid improvement in 2s3z...; Results show improved performance against a next-speaker prediction baseline ( **Interpretation note:** This is a hypothesis-generating alpha memo, not confirmatory evidence; subgroup or context-derived claims require independent replication. ## Why this is surprising Frontier review skipped; using deterministic gate audit. 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/2024/win_rate_205336` (`A_core`) — Finally, the experimental results show that our proposed confrontation strategy has a 72% higher win rate compared to the QMIX algorithm under asymmetric confrontation conditions. doi=10.1109/smc54092.2024.10832089 - `fact_id=multi_agent_systems/auto/2024/win_rate_205396` (`A_core`) — Empirical evaluations on SMAC environments demonstrate superior performance compared to baselines, achieving a higher win rate on 68% of test evaluations. doi=10.5555/3635637.3663141 - `fact_id=multi_agent_systems/auto/2023/win_rate_205101` (`A_core`) — The experiments demonstrate a maximum improvement in win rate of 47% over the best known algorithm. doi=10.1016/j.neunet.2023.02.037 - `fact_id=multi_agent_systems/auto/2022/win_rate_207382` (`A_core`) — The performance of the centralized architecture shows a solid improvement in 2s3z environment and achieves almost 70%win rate over the benchmark of 43%. doi=10.18178/ijmlc.2022.12.3.1084 - `fact_id=multi_agent_systems/auto/2026/win_rate_205465` (`A_core`) — Results show improved performance against a next-speaker prediction baseline (achieving a 72.13% win rate) and demonstrate effective group dynamics. doi=10.1609/aaai.v40i48.42120 ## 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._
metadata
{
"article_type": "alpha_memo",
"domain_slug": "ai_research",
"researka_object_type": "submission",
"researka_submission_id": "b297a5d3-6fbf-4b8f-86e8-7f5870add5a4",
"title": "Multi-agent systems achieve higher win rate than baseline MARL algorithms in SMAC/adversarial settings"
}