mlsanityData to 7 Oct 2026

Beyond the Last Answer: Your Reasoning Trace Uncovers More than You Think

arXiv 2504.20708 ↗First posted 29 Apr 2025cs.CL cs.AI cs.LGcited by 16

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 25 of its 32 references that are in the map. First posted on arXiv 29 Apr 2025.

Builds on

25 of 32 references.

References · 25

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  4. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  5. 2025s1: Simple test-time scalingcited by 1,311
  6. 2024OpenAI o1 System Cardcited by 1,220
  7. 2024Qwen2.5-Coder Technical Reportcited by 1,151
  8. 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 763
  9. 2021Cross-Task Generalization via Natural Language Crowdsourcing Instructionscited by 696
  10. 2024Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMscited by 564
  11. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
  12. 2025L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learningcited by 381
13 more
  1. 2025Demystifying Long Chain-of-Thought Reasoning in LLMscited by 323
  2. 2024Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMscited by 265
  3. 2023Think before you speak: Training Language Models With Pause Tokenscited by 254
  4. 2025Training Language Models to Reason Efficientlycited by 242
  5. 2024From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Stepcited by 225
  6. 2023Implicit Chain of Thought Reasoning via Knowledge Distillationcited by 165
  7. 2024Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systemscited by 162
  8. 2024Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffoldingcited by 115
  9. 2022Successive Prompting for Decomposing Complex Questionscited by 94
  10. 2024O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?cited by 90
  11. 2023Guiding Language Model Reasoning with Planning Tokenscited by 58
  12. 2024Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizingcited by 49
  13. 2021Prompting Contrastive Explanations for Commonsense Reasoning Taskscited by 45

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 8.

16 papers.

Sort:

  1. 2025Towards Concise and Adaptive Thinking in Large Reasoning Models: A Surveycited by 18
  2. 2025Read Your Own Mind: Reasoning Helps Surface Self-Confidence Signals in LLMscited by 10
  3. 2025Do LLMs Really Need 10+ Thoughts for "Find the Time 1000 Days Later"? Towards Structural Understanding of LLM Overthinkingcited by 10
  4. 2026ATTNPO: Attention-Guided Process Supervision for Efficient Reasoningcited by 9
  5. 2026Probing the Trajectories of Reasoning Traces in Large Language Modelscited by 6
  6. 2025Self-Reflective Generation at Test Timecited by 3
  7. 2025Pinpointing crucial steps: Attribution-based Credit Assignment for Verifiable Reinforcement Learningcited by 3
  8. 2025ReTrace: Interactive Visualizations for Reasoning Traces of Large Reasoning Modelscited by 3
  9. 2026Reliable Chain-of-Thought via Prefix Consistencycited by 3
  10. 2025Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuningcited by 2
  11. 2026RREDCoT: Segment-Level Reward Redistribution for Reasoning Modelscited by 1
  12. 2025RLFR: Extending Reinforcement Learning for LLMs with Flow Environmentcited by 0
4 more
  1. 2026Beyond Model Scaling: Test-Time Intervention for Efficient Deep Reasoningcited by 0
  2. 2026Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Modelscited by 0
  3. 2026Imbuing Large Language Models with Bidirectional Logic for Robust Chain Repaircited by 0
  4. 2026Cliff Tokens: Identifying Single-Token Failure Triggers in LLM Mathematical Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs
  2. 2026Large Language Models Decide Early and Explain Later
  3. 2025Interpretable Traces, Unexpected Outcomes: Investigating the Disconnect in Trace-Based Knowledge Distillation
  4. 2025Beg to Differ: Understanding Reasoning-Answer Misalignment Across Languages
  5. 2024Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning
  6. 2025What Defines Good Reasoning in LLMs? Dissecting Reasoning Steps with Multi-Aspect Evaluation
  7. 2025Thought Anchors: Which LLM Reasoning Steps Matter?
  8. 2022Faithful Reasoning Using Large Language Models