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Résumé : Retrieval-augmented generation (RAG) has facilitated large language models (LLMs) by grounding facts in external knowledge. While prior RAG strategies can achieve dynamic control by detecting sentence-wise hallucination, thereby reducing unnecessary computation, they ignore the entity, statement, and their combinations. In this work, we analyze adaptive RAG methods through probing uncertainty to explore real-time entity- and statement-level verification, finding that such information can serve as training-free signals for hallucination detection, and the entropy probes can also guide principled document ranking. Inspired by these insights, we propose SeaRAG, a training-free, adaptive RAG framework that dynamically detects and mitigates hallucinations by probing LLMs at both entity and statement aspects, ranking retrieved passages via entropy-based uncertainty reduction, and regenerating evidence-grounded responses in real-time. Experiments across various Question-Answering benchmarks and multiple LLMs demonstrate consistent accuracy improvements and reduced retrieval rates. SeaRAG with self-correction outperforms Always Retrieve by up to 11.1% on TriviaQA while cutting retrieval frequency from 45.2%, offering an efficient and real-time hallucination control.