Over-Search and Under-Search

Problem

The agent re-retrieves passages it has already seen in the same context window (over-search), burning tokens and latency on duplicates, and it skips retrieval when its parametric knowledge is wrong (under-search), producing confident hallucinations. Both failures are invisible at the aggregate metric level — accuracy averages can stay flat while individual queries either pay for the same passage four times or get answered from stale weights. The HiPRAG paper measures over-search at double-digit baseline rates in standard agentic-RAG setups, with under-search rates rising under reinforcement-learning training that rewards short trajectories.

Solution

Don't ship agentic RAG without calibrated retrieval decisions. Adopt agentic-rag with explicit retrieval-decision instrumentation: per-step rewards that penalise redundant retrieval and reward retrieval when parametric knowledge is insufficient. Track over-search and under-search rates as first-class evaluation metrics. Compare against naive-rag (always retrieve) and naive-rag-first (RAG-by-reflex) as baselines — the goal is calibrated, not maximally agentic.

When to use

• Never. Cite when reviewing an agentic-RAG system that has no per-step retrieval-decision metric.
• Demand over-search and under-search rates as first-class evaluation outputs.
• Instrument retrieval decisions with process rewards or rule-based gates.