Retriever-Free Retrieval-Augmented Reasoning via Corpus-Traversing MCTS

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in multi-step reasoning and calling search engines at appropriate steps. However, existing retrieval-augmented reasoning approaches rely on separate retrieval models, limiting the LRM's role in retrieval to deciding when to retrieve and how to formulate queries, even with its inherent ability to handle vast knowledge spaces. This structural separation induces a representation bottleneck, as the retriever’s latent space often lacks the expressive granularity required to satisfy the generator’s sophisticated information needs. To address this, we shift our perspective on retrieval from sequence-to-sequence matching for all corpora to locating the answer-containing paths within the corpus, and propose a novel framework called FREESON (Retriever-FREE Retrieval-Augmented ReaSONing). This framework enables LRMs to directly access external knowledge by acting as both a generator and a retriever. To achieve this, we introduce a variant of the MCTS algorithm specialized for the retrieval task, which we call CT-MCT (Corpus-Traversing Monte Carlo Tree Search). Through this algorithm, the LRM selectively references specific segments identified during traversal, instead of fetching a fixed top-k set. Experiments on five open-domain QA benchmarks covering both single-hop and multi-hop questions demonstrate that FREESON achieves an average improvement of 14.4% in EM and F1 over four multi-step reasoning models with a separate retriever, and it also performs comparably to the strongest baseline, surpassing it by 3% on PopQA and 2WikiMultihopQA, and by 12% on the fact-checking benchmark FEVER.