SearchMapper: Structuring Deep Search as Dynamic DAG with Process-Guided Refinement

LLM-based search agents have shown strong progress in complex multi-step retrieval and reasoning, yet errors introduced during early stages compound through subsequent steps. We term this phenomenon lost in search and trace it to implicit dependency tracking, where agents cannot identify which prior results a current action depends on or which downstream actions an earlier mistake would invalidate. We propose SearchMapper, a framework that makes these dependencies explicit by organizing the search process as a Directed Acyclic Graph (DAG) of sub-questions, constructing a search map that grows adaptively as reasoning unfolds. A hierarchical scoring model evaluates each sub-question along three dependency-grounded dimensions, namely redundancy, clarity, and progressiveness, producing scores and textual feedback that guide iterative refinement. A Non-descendant Error Attribution Region (NEAR)-guided detection algorithm exploits the DAG’s causal structure to localize critical errors via monotonic hypothesis space contraction, recovering 85.2% of failed trajectories at an average of 1.63 iterations. Both signals supervise a three-stage pipeline spanning imitation, alignment, and generalization, which lifts the five-benchmark average from 54.9 to 63.9. With only 30B parameters, SearchMapper leads all same-scale search agents on four of five benchmarks, while using 35.6% fewer reasoning steps than Tongyi-DeepResearch.