Seek's test-time iterative retrieval lifts Qwen2.5-7B to an 82% relative gain over BM25 on BRIGHT and GPT-4.1 to 37.4 nDCG@10
The work introduces Seek, a training-free self-evaluative exploration framework for knowledge retrieval that performs iterative corpus interaction at test time—an LLM generates pseudo-passages conditioned on accumulated relevance feedback, a retriever surfaces fresh candidates, and a dedicated assessor assigns graded relevance judgments that guide subsequent rounds—matching trained rerankers in ranking quality while consistently improving Recall@100 over single-pass BM25 on TREC Deep Learning, and on the reasoning-intensive BRIGHT benchmark achieving an 82% relative gain over BM25 with Qwen2.5-7B, surpassing all trained baselines, and reaching 37.4 average nDCG@10 with GPT-4.1, exceeding the strongest baseline by 37%.