title: Beyond the Last Answer: Your Reasoning Trace Uncovers More than You
    Think
summary: Large Language Models (LLMs) leverage step-by-step reasoning to solve complex
  problems. Standard evaluation practice involves generating a complete reasoning
  trace and assessing the correctness of the final answer presented at its
  conclusion. In this paper, we challenge the reliance on the final answer by
  posing the following two questions: Does the final answer reliably represent
  the model's optimal conclusion? Can alternative reasoning paths yield different
  results? To answer these questions, we analyze intermediate reasoning steps,
  termed subthoughts, and propose a method based on our findings. Our approach
  involves segmenting a reasoning trace into sequential subthoughts based on
  linguistic cues. We start by prompting the model to generate continuations from
  the end-point of each intermediate subthought. We extract a potential answer
  from every completed continuation originating from different subthoughts. We
  find that aggregating these answers by selecting the most frequent one (the
  mode) often yields significantly higher accuracy compared to relying solely on
  the answer derived from the original complete trace. Analyzing the consistency
  among the answers derived from different subthoughts reveals characteristics
  that correlate with the model's confidence and correctness, suggesting
  potential for identifying less reliable answers. Our experiments across various
  LLMs and challenging mathematical reasoning datasets (AIME2024 and AIME2025)
  show consistent accuracy improvements, with gains reaching up to 13\% and 10\%
  respectively. Implementation is available at:
  https://github.com/hammoudhasan/SubthoughtReasoner.
paper_authors._id: 6813376e2379095ee572f2f8
paper_authors.name: Hasan Abed Al Kader Hammoud
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T08:01:19.886Z
paper_authors._id: 642b51385bf2355d02a23d15
paper_authors.avatarUrl: /avatars/87985347643b2647555f2453fa4d94fb.svg
paper_authors.fullname: Hasan Abed Al Kader Hammoud
paper_authors.type: user
paper_authors.user: hammh0a
paper_authors._id: 6813376e2379095ee572f2f9
paper_authors.name: Hani Itani
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T08:01:27.975Z
paper_authors._id: 642b53bf48f67b6f21d3a357
paper_authors.avatarUrl: /avatars/d5b6e908a9aba43b813d5815fdf57f9e.svg
paper_authors.fullname: Hani Itani
paper_authors.type: user
paper_authors.user: itanh0b
paper_authors._id: 6813376e2379095ee572f2fa
paper_authors.name: Bernard Ghanem
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T08:01:34.030Z
paper_authors._id: 6808bf97ffadd78ec71cb721
paper_authors.avatarUrl: /avatars/9adca3142c06b8f69889fcbe85fa374d.svg
paper_authors.fullname: Bernard Ghanem
paper_authors.type: user
paper_authors.user: bernardghanem
paper_title: Beyond the Last Answer: Your Reasoning Trace Uncovers More than You
    Think
paper_summary: Large Language Models (LLMs) leverage step-by-step reasoning to solve complex
  problems. Standard evaluation practice involves generating a complete reasoning
  trace and assessing the correctness of the final answer presented at its
  conclusion. In this paper, we challenge the reliance on the final answer by
  posing the following two questions: Does the final answer reliably represent
  the model's optimal conclusion? Can alternative reasoning paths yield different
  results? To answer these questions, we analyze intermediate reasoning steps,
  termed subthoughts, and propose a method based on our findings. Our approach
  involves segmenting a reasoning trace into sequential subthoughts based on
  linguistic cues. We start by prompting the model to generate continuations from
  the end-point of each intermediate subthought. We extract a potential answer
  from every completed continuation originating from different subthoughts. We
  find that aggregating these answers by selecting the most frequent one (the
  mode) often yields significantly higher accuracy compared to relying solely on
  the answer derived from the original complete trace. Analyzing the consistency
  among the answers derived from different subthoughts reveals characteristics
  that correlate with the model's confidence and correctness, suggesting
  potential for identifying less reliable answers. Our experiments across various
  LLMs and challenging mathematical reasoning datasets (AIME2024 and AIME2025)
  show consistent accuracy improvements, with gains reaching up to 13\% and 10\%
  respectively. Implementation is available at:
  https://github.com/hammoudhasan/SubthoughtReasoner.
paper_ai_summary: Aggregating answers from multiple subthoughts in reasoning traces improves the accuracy of Large Language Models beyond relying on the final answer.
paper_ai_keywords: Large Language Models step-by-step reasoning reasoning trace subthoughts linguistic cues answer aggregation mode selection model confidence AIME2024 AIME2025