DecisionRAG Design CQA285 - R@10: leaderboard
DecisionRAG v1 uses Jev (typesafe/jev-1.13), dense retrieval with Qwen3-Embedding-8B, BGE (bge-reranker-v2-m3), a Qwen3 8B reranker, and GPT 5 with low reasoning. Evaluation populations are MD2D with 4201 validation turns, CQA with 271 evidence-labeled development questions, MTRAG with 188 labeled IBM Cloud turns, and NQ with 500 seeded DAPR test questions. Main MTRAG gold labels are expanded using GPT 5 to judge the pooled top 5 documents; comparisons using the original official labels remain separate. Trained judges use Qwen3 Base at 1.7B, 4B, and 8B; their results are distinct from backbone language-model scores. All source-qualified harnesses and training variants remain separate. Design populations comprise 1832 MD2D training turns, 285 CQA paragraph-labeled design questions, and 86 MTRAG-UN questions (one per conversation). These are design results, not held-out evaluation. Metric: Accuracy (%).
Metric: Accuracy (%). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 3 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | DecisionRAG design CQA285 3 pages + dense 10 + multiple-choice | 78 | |
| 2 | DecisionRAG design CQA285 3 pages + dense 10 | 76.5 | |
| 3 | DecisionRAG design CQA285 3 pages | 74.1 |
Interactive version: theaggregate.ai/benchmark?slug=decisionrag-design-cqa285-r-10 · How It Works · Data refreshed daily, snapshot 2026-10-28.