mlsanityData to 7 Oct 2026

Do Large Language Models Latently Perform Multi-Hop Reasoning?

arXiv 2402.16837 ↗First posted 26 Feb 2024cs.CLcited by 165

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

We study whether Large Language Models (LLMs) latently perform multi-hop reasoning with complex prompts such as "The mother of the singer of 'Superstition' is". We look for evidence of a latent reasoning pathway where an LLM (1) latently identifies "the singer of 'Superstition'" as Stevie Wonder, the bridge entity, and (2) uses its knowledge of Stevie Wonder's mother to complete the prompt. We analyze these two hops individually and consider their co-occurrence as indicative of latent multi-hop reasoning. For the first hop, we test if changing the prompt to indirectly mention the bridge entity instead of any other entity increases the LLM's internal recall of the bridge entity. For the second hop, we test if increasing this recall causes the LLM to better utilize what it knows about the bridge entity. We find strong evidence of latent multi-hop reasoning for the prompts of certain relation types, with the reasoning pathway used in more than 80% of the prompts. However, the utilization is highly contextual, varying across different types of prompts. Also, on average, the evidence for the second hop and the full multi-hop traversal is rather moderate and only substantial for the first hop. Moreover, we find a clear scaling trend with increasing model size for the first hop of reasoning but not for the second hop. Our experimental findings suggest potential challenges and opportunities for future development and applications of LLMs.

Key facts. Cited by 165 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 40 of its 55 references that are in the map. First posted on arXiv 26 Feb 2024.

Builds on

40 of 55 references.

References · 40

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  6. 2019Language Models as Knowledge Bases?cited by 1,922
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28 more
  1. 2021Editing Factual Knowledge in Language Modelscited by 548
  2. 2022Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Spacecited by 522
  3. 2023Dissecting Recall of Factual Associations in Auto-Regressive Language Modelscited by 512
  4. 2023Faith and Fate: Limits of Transformers on Compositionalitycited by 496
  5. 2023The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A"cited by 399
  6. 2021Measuring and Improving Consistency in Pretrained Language Modelscited by 361
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  8. 2023Physics of Language Models: Part 3.1, Knowledge Storage and Extractioncited by 264
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  17. 2023Why think step by step? Reasoning emerges from the locality of experiencecited by 103
  18. 2023Jump to Conclusions: Short-Cutting Transformers With Linear Transformationscited by 89
  19. 2023Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examplescited by 84
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  25. 2024A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Taskcited by 49
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 43; 2025: 90; 2026: 32.

165 papers.

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  1. 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 763
  2. 2024Training Language Models to Self-Correct via Reinforcement Learningcited by 405
  3. 2024Alignment faking in large language modelscited by 316
  4. 2025A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyondcited by 131
  5. 2025Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokenscited by 120
  6. 2025Natural Emergent Misalignment from Reward Hacking in Production RLcited by 111
  7. 2025Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Spacecited by 109
  8. 2024Looking Inward: Language Models Can Learn About Themselves by Introspectioncited by 105
  9. 2024Small Language Models Need Strong Verifiers to Self-Correct Reasoningcited by 85
  10. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  11. 2024Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalizationcited by 78
  12. 2024Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queriescited by 78
38 more
  1. 2024GRAG: Graph Retrieval-Augmented Generationcited by 77
  2. 2024A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasonerscited by 73
  3. 2025Efficient Reasoning with Hidden Thinkingcited by 72
  4. 2025A Survey on Latent Reasoningcited by 71
  5. 2024The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalitiescited by 69
  6. 2025Are DeepSeek R1 And Other Reasoning Models More Faithful?cited by 69
  7. 2025SEAL: Steerable Reasoning Calibration of Large Language Models for Freecited by 65
  8. 2025Think with 3D: Geometric Imagination Grounded Spatial Reasoning from Limited Viewscited by 58
  9. 2024LoFiT: Localized Fine-tuning on LLM Representationscited by 56
  10. 2025Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoningcited by 50
  11. 2024Internal Consistency and Self-Feedback in Large Language Models: A Surveycited by 49
  12. 2025PonderLM: Pretraining Language Models to Ponder in Continuous Spacecited by 44
  13. 2024Disentangling Memory and Reasoning Ability in Large Language Modelscited by 43
  14. 2024Theoretical limitations of multi-layer Transformercited by 43
  15. 2025Measuring Chain of Thought Faithfulness by Unlearning Reasoning Stepscited by 43
  16. 2025SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based on Speech and Audio Informationcited by 42
  17. 2025Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Surveycited by 38
  18. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 38
  19. 2025Position: Episodic Memory is the Missing Piece for Long-Term LLM Agentscited by 35
  20. 2025LLM Pretraining with Continuous Conceptscited by 33
  21. 2025(How) Do Language Models Track State?cited by 31
  22. 2025Hybrid Latent Reasoning via Reinforcement Learningcited by 30
  23. 2024Not All LLM Reasoners Are Created Equalcited by 29
  24. 2024Intrinsic Test of Unlearning Using Parametric Knowledge Tracescited by 28
  25. 2025Roll the dice & look before you leap: Going beyond the creative limits of next-token predictioncited by 28
  26. 2024Calibrating Reasoning in Language Models with Internal Consistencycited by 27
  27. 2024Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?cited by 26
  28. 2025When One LLM Drools, Multi-LLM Collaboration Rulescited by 25
  29. 2025Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformercited by 24
  30. 2025Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Modelscited by 23
  31. 2025Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimizationcited by 23
  32. 2025The Reasoning-Memorization Interplay in Language Models Is Mediated by a Single Directioncited by 21
  33. 2025Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Modelcited by 21
  34. 2025How Do LLMs Perform Two-Hop Reasoning in Context?cited by 20
  35. 2025Language-Specific Latent Process Hinders Cross-Lingual Performancecited by 20
  36. 2024Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theorycited by 19
  37. 2024Transformers Struggle to Learn to Searchcited by 19
  38. 2025Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Modelscited by 19

50 most cited of 165 All 165 (JSON)

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  1. 2024Lessons from Studying Two-Hop Latent Reasoning
  2. 2024Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?
  3. 2024Large Language Models Still Face Challenges in Multi-Hop Reasoning with External Knowledge
  4. 2024Distributional reasoning in LLMs: Parallel reasoning processes in multi-hop reasoning
  5. 2026Bridge Routing Heads: Where Multilingual Multi-hop Reasoning Lives in LLMs
  6. 2025Beyond Chains of Thought: Benchmarking Latent-Space Reasoning Abilities in Large Language Models
  7. 2024Making Long-Context Language Models Better Multi-Hop Reasoners
  8. 2026Opening the Black Box: A Survey on the Mechanisms of Multi-Step Reasoning in Large Language Models