title: FREESON: Retriever-Free Retrieval-Augmented Reasoning via
    Corpus-Traversing MCTS
summary: 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 query. This separation not only increases hardware and operational costs
  but also leads to errors in the retrieval process due to the representation
  bottleneck, a phenomenon where the retriever's embedding space is not
  expressive enough to meet the generator's requirements. To address this, we
  shift our perspective from sequence-to-sequence matching 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 retrieve relevant knowledge on their own by acting as both a generator
  and retriever. To achieve this, we introduce a variant of the MCTS algorithm
  specialized for the retrieval task, which we call CT-MCTS (Corpus-Traversing
  Monte Carlo Tree Search). In this algorithm, LRMs traverse through the corpus
  toward answer-containing regions. Our results on five open-domain QA
  benchmarks, including single-hop and multi-hop questions, show 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.
paper_authors._id: 6833d7f5ba63298d8869d305
paper_authors.name: Chaeeun Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:09:41.154Z
paper_authors._id: 614c9487cbb5e52274a4024d
paper_authors.avatarUrl: /avatars/a923db5ea27c4184ed2ce84738860203.svg
paper_authors.fullname: Chaeeun Kim
paper_authors.type: user
paper_authors.user: Chaeeun-Kim
paper_authors._id: 6833d7f5ba63298d8869d306
paper_authors.name: Seungone Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:55:03.289Z
paper_authors._id: 6469949654873f0043b09c22
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6469949654873f0043b09c22/Lk7IJAR16Wa_sGJ2g81AQ.jpeg
paper_authors.fullname: Seungone Kim
paper_authors.type: user
paper_authors.user: seungone
paper_title: FREESON: Retriever-Free Retrieval-Augmented Reasoning via
    Corpus-Traversing MCTS
paper_summary: 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 query. This separation not only increases hardware and operational costs
  but also leads to errors in the retrieval process due to the representation
  bottleneck, a phenomenon where the retriever's embedding space is not
  expressive enough to meet the generator's requirements. To address this, we
  shift our perspective from sequence-to-sequence matching 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 retrieve relevant knowledge on their own by acting as both a generator
  and retriever. To achieve this, we introduce a variant of the MCTS algorithm
  specialized for the retrieval task, which we call CT-MCTS (Corpus-Traversing
  Monte Carlo Tree Search). In this algorithm, LRMs traverse through the corpus
  toward answer-containing regions. Our results on five open-domain QA
  benchmarks, including single-hop and multi-hop questions, show 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.
paper_ai_summary: FREESON, a novel framework that integrates retrieval and reasoning roles within LRMs using CT-MCTS, improves the performance of multistep reasoning models in QA tasks by reducing representation bottlenecks.
paper_ai_keywords: LREMS multi-step reasoning retrieval-augmented reasoning sequence-to-sequence matching MCTS CT-MCTS Corpus-Traversing Monte Carlo Tree Search representation bottleneck open-domain QA single-hop and multi-hop questions EM F1 PopQA 2WikiMultihopQA