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Agentic RAG Evaluation: Budget Allocation Across Questions, Trajectories, and Reads

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 02:18:25 UTC.

Briefings

A 2026 study evaluated agentic RAG budget allocation across questions, search trajectories, and repeated answers using HotpotQA and MuSiQue. At 34.14-34.39M tokens, broader question coverage reduced standard error by 33% versus five reads and 12.6% versus three trajectories. Archived nested and Q-only forecasts predicted allocations within 4.0% and 3.5% respectively. One-read variance penalties relative to the fitted optimum ranged from 0% to 9.9% with substantial uncertainty. Under recorded model fees, more questions beat more trajectories at search prices of $0-1 per 1,000 requests, but question-versus-read fee rankings remained unresolved.

Key points

• At 34.14-34.39M model tokens, broader question coverage lowered standard error by 33% compared to five reads.
• Archived nested and Q-only forecasts predicted allocation budgets within 4.0% and 3.5% of actual values.

Context

This research investigates how budget allocation strategies affect performance in agentic RAG systems using HotpotQA and MuSiQue datasets.

Limitations

Forecast accuracy for nested and Q-only strategies was within 4.0% and 3.5% of actual allocations. One-read variance penalties relative to the fitted optimum ranged from 0% to 9.9% with substantial uncertainty. Question-versus-read fee rankings remain unresolved under recorded model fees.

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