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

arXiv:2606.25191v1 Announce Type: cross 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, de

Source: arXiv cs.CL — read the full report at the original publisher.

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