mlsanityData to 7 Oct 2026

Exploring the Role of Reasoning Structures for Constructing Proofs in Multi-Step Natural Language Reasoning with Large Language Models

arXiv 2410.08436 ↗First posted 11 Oct 2024cs.CL cs.AIcited by 3

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

When performing complex multi-step reasoning tasks, the ability of Large Language Models (LLMs) to derive structured intermediate proof steps is important for ensuring that the models truly perform the desired reasoning and for improving models' explainability. This paper is centred around a focused study: whether the current state-of-the-art generalist LLMs can leverage the structures in a few examples to better construct the proof structures with textitin-context learning. Our study specifically focuses on structure-aware demonstration and structure-aware pruning. We demonstrate that they both help improve performance. A detailed analysis is provided to help understand the results.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 8 of its 34 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

8 of 34 references.

References · 8

  1. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  2. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
  3. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  4. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  5. 2022Complexity-Based Prompting for Multi-Step Reasoningcited by 387
  6. 2023Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thoughtcited by 41
  7. 2023Explaining Legal Concepts with Augmented Large Language Models (GPT-4)cited by 39
  8. 2020Case-Based Abductive Natural Language Inferencecited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Semantic-Aware Logical Reasoning via a Semiotic Frameworkcited by 32
  2. 2026Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoningcited by 24
  3. 2026Semiotic logical hexagon theory for LLM logical reasoningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Can Large Language Models Learn Formal Logic? A Data-Driven Training and Evaluation Framework
  2. 2025PCRLLM: Proof-Carrying Reasoning with Large Language Models under Stepwise Logical Constraints
  3. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  4. 2023Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
  5. 2026Opening the Black Box: A Survey on the Mechanisms of Multi-Step Reasoning in Large Language Models
  6. 2024Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning
  7. 2025LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models
  8. 2026Structured Reasoning for Large Language Models