SearchWiki: Learning to Build and Navigate Knowledge Wikis for Active Information Seeking

• URL: http://arxiv.org/abs/2608.29953v1
• Date: Sun, 30 Aug 2026 18:36:13 GMT
• Title: SearchWiki: Learning to Build and Navigate Knowledge Wikis for Active Information Seeking
• Abstract summary: SearchWiki is a framework that synthesizes a corpus into a hierarchical, typed, navigable wiki.<n>We train an agent, WikiResearcher-9B, to retrieve information through multi-turn tool use.
• Score: 11.506364987885492
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flat retrieval-augmented generation treats a corpus as a bag of chunks, discarding document hierarchy and cross document structure. We introduce SearchWiki, a harness framework that synthesizes a corpus into a hierarchical, typed, navigable wiki and trains an agent, WikiResearcher-9B, to retrieve information through multi-turn tool use. The wiki organizes knowledge into three layers - document overviews, cross- document topic pages, and page-level source records; enabling progressive refinement of retrieval when initial lookup misses. We optimize the agent's navigation policy with on-policy reinforcement learning with a multi-component reward function balancing answer correctness, retrieval quality and trajectory efficiency. Evaluation on ViDoRe-V3 (8 domains), FinanceBench, and memory benchmarks (LoCoMo, LongMemEval, PersonaMem-v2) shows that WikiResearcher- 9B which is our RL-tuned Qwen 9B model, significantly outperforms same-size untrained baselines and exceeds or matches larger external models. SearchWiki paired with WikiResearcher-9B demonstrates that learned navigation over structured corpora is a superior alternative to flat retrieval.

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