To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAG

• URL: http://arxiv.org/abs/2606.25191v1
• Date: Tue, 23 Jun 2026 21:34:23 GMT
• Title: To Isolate or to Score? Model-Adaptive Assessment for Cost-Efficient Multi-Agent RAG
• Abstract summary: Multi-agent document assessment for retrieval-augmented generation is computationally expensive.<n>We conduct a controlled study of training-free interventions on 7B-9B instruction-tuned models across diverse QA benchmarks.<n>We propose MADARA, a model-adaptive routing architecture.
• Score: 31.664195597760422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent document assessment for retrieval-augmented generation is computationally expensive, driving practitioners toward smaller, deployable models whose assessment mechanisms remain poorly understood. We conduct a controlled study of training-free interventions on 7B-9B instruction-tuned models across diverse QA benchmarks, revealing a sharp dichotomy in how models benefit from assessment. For weaker baselines, the dominant mechanism is per-document isolation. Astoundingly, assessment-free isolation matches full multi-agent assessment, demonstrating that resolving multi-document context confusion, rather than scoring quality, drives outsized gains of up to 50 percentage points. Conversely, for strong baselines where scoring quality matters, we introduce Reasoning-Score Coupling, a label-free perturbation probe that classifies scoring behavior. Integrating these findings, we propose MADARA, a model-adaptive routing architecture. Crucially, MADARA's diagnostic thresholds derived from a single pilot model generalize zero-shot to four unseen model families, providing a robust, lightweight pipeline to eliminate computational overhead.

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