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原文摘要

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in multi-step reasoning and calling search tools at appropriate steps. However, existing retrieval-augmented reasoning approaches still rely on separate retrieval models, limiting LRMs to deciding when to retrieve and how to formulate queries rather than leveraging their pretrained knowledge and reasoning capabilities for retrieval itself. This structural separation introduces a representation bottleneck, as retrieval relies on separately learned representations that may obscure fine-grained distinctions needed for reasoning. Moreover, these approaches commonly employ fixed top-k retrieval, which can introduce superfluous documents and increase retrieval noise and context overhead. To address this, we reformulate retrieval from query-document relevance matching into the exploration of answer-containing paths within the corpus, and propose FREESON (Retriever-FREE Retrieval-Augmented ReaSONing), a framework that enables LRMs to directly navigate the original corpus text to locate information that satisfies their specific needs, serving as both the reasoner and the retriever. Specifically, we introduce CT-MCTS (Corpus-Traversing Monte Carlo Tree Search), a retrieval-specialized variant of Monte Carlo Tree Search (MCTS). CT-MCTS iteratively expands and evaluates exploration paths, starting from multiple promising entry points across the corpus. This allows LRMs to selectively reference specific segments identified during traversal, instead of fetching a fixed top-k set. Across nine benchmarks, FREESON improves average EM and F1 by 15 points over four retriever-based baselines on five general-domain QA tasks, matching the strongest baseline. It also gains 12 points on fact-checking and 13 points on medical QA under zero-shot transfer, performing on par with the much larger MedGemma-27B.