mlsanityData to 14 Oct 2026

Internal Chain-of-Thought: Empirical Evidence for Layer-wise Subtask Scheduling in LLMs

arXiv 2505.14530 ↗First posted 20 May 2025cs.CL cs.LGcited by 9

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

We show that large language models (LLMs) exhibit an textitinternal chain-of-thought: they sequentially decompose and execute composite tasks layer-by-layer. Two claims ground our study: (i) distinct subtasks are learned at different network depths, and (ii) these subtasks are executed sequentially across layers. On a benchmark of 15 two-step composite tasks, we employ layer-from context-masking and propose a novel cross-task patching method, confirming (i). To examine claim (ii), we apply LogitLens to decode hidden states, revealing a consistent layerwise execution pattern. We further replicate our analysis on the real-world TRACE benchmark, observing the same stepwise dynamics. Together, our results enhance LLMs transparency by showing their capacity to internally plan and execute subtasks (or instructions), opening avenues for fine-grained, instruction-level activation steering.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 18 of its 54 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

18 of 54 references.

References · 18

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2024Qwen2.5 Technical Reportcited by 4,942
  3. 2023Mistral 7Bcited by 3,164
  4. 2022Toy Models of Superpositioncited by 971
  5. 2022In-context Learning and Induction Headscited by 964
  6. 2024DeepSeek-V3 Technical Reportcited by 811
  7. 2024Scaling and evaluating sparse autoencoderscited by 553
  8. 2017Word Translation Without Parallel Datacited by 487
  9. 2024Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Modelscited by 435
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  11. 2022Polysemanticity and Capacity in Neural Networkscited by 77
  12. 2024Separating Tongue from Thought: Activation Patching Reveals Language-Agnostic Concept Representations in Transformerscited by 44
6 more
  1. 2024Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questionscited by 35
  2. 2024IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimizationcited by 31
  3. 2024Do Large Language Models Perform Latent Multi-Hop Reasoning without Exploiting Shortcuts?cited by 28
  4. 2025Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Modelscited by 23
  5. 2025Task Vectors in In-Context Learning: Emergence, Formation, and Benefitcited by 20
  6. 2025Learning Task Representations from In-Context Learningcited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 5.

9 papers.

  1. 2025Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoningcited by 78
  2. 2025Implicit Reasoning in Large Language Models: A Comprehensive Surveycited by 43
  3. 2025How Do Language Models Compose Functions?cited by 8
  4. 2026Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probingcited by 6
  5. 2025Punctuation and Predicates in Language Modelscited by 5
  6. 2026Word Recovery in Large Language Models Enables Character-Level Tokenization Robustnesscited by 2
  7. 2026MTA: Multi-Granular Trajectory Alignment for Large Language Model Distillationcited by 2
  8. 2026Opening the Black Box: A Survey on the Mechanisms of Multi-Step Reasoning in Large Language Modelscited by 1
  9. 2026Transformers Stop Thinking Too Early, and a Tiny LoRA Fixes Itcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs
  2. 2026When Chain-of-Thought Helps and When It Hurts: An Empirical Investigation of the Serial-Depth Bottleneck in LLM Reasoning
  3. 2025The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs
  4. 2026Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering
  5. 2026Route-Induced Density and Stability (RIDE): Controlled Intervention and Mechanism Analysis of Routing-Style Meta Prompts on LLM Internal States
  6. 2026Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models
  7. 2026A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models
  8. 2024Chain-of-Thought in Large Language Models: Decoding, Projection, and Activation