InterEvolve: Self-Evolving Agentic Search via Retrieval–Understanding Interaction

State assessment in self-evolving agentic search poses significant challenges, as inaccurate evaluations can mislead the evolutionary trajectory and trap the system in redundant or mis-evolved loops. In this paper, we observe that humans mitigate such failures via retrieval-understanding interaction. During multi-turn search, they use prior understanding to form expectations about retrieval results and assess the consistency between results and expectations to resist misdirection from search outputs. Inspired by this behavior, we propose InterEvolve, an agentic search framework that improves the accuracy of state assessment without requiring additional model training or task-specific fine-tuning. Specifically, InterEvolve comprises two main components: the Retrieval–Understanding Progress Assessor (RUPA) and the Query Refinement Validator (QRV). RUPA leverages retrieval–understanding consistency to assess whether each search iteration progresses in a productive direction. Moreover, QRV constrains retrieval discrepancies between the original and refined queries, ensuring reliable query refinement and preventing misdirected evolution. Extensive experiments on the MMEB-v2 benchmark (78 datasets) across 4 popular retrieval backbones demonstrate that InterEvolve consistently outperforms the naive agentic search baseline in all model combinations. Notably, agentic search relying on weaker models like Qwen3.8-27B for state assessment suffers from misdirected evolution, degrading from 74.96 to 74.14 below single-turn retrieval. InterEvolve recovers this degradation and further improves it to 75.48, demonstrating its robustness safeguard. The code will be released if the paper is accepted.