Do Latent-CoT Models Think Step-by-Step? A Mechanistic Study on Sequential Reasoning Tasks
cs.AI, cs.LG
Submitted: 2026-01-31
Updated: 2026-09-27
Code: https://github.com/TransformerLensOrg/TransformerLens
Terminology
Sources
- Understanding intermediate layers using linear classifier probes
- Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries
- A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Task
- From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step
- How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- How Do LLMs Perform Two-Hop Reasoning in Context?
- Training Large Language Models to Reason in a Continuous Latent Space
- Towards a Mechanistic Interpretation of Multi-Step Reasoning Capabilities of Language Models
- OpenAI o1 System Card
- Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models
- Adam: A Method for Stochastic Optimization
- LLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-step Arithmetics
- Understanding and Patching Compositional Reasoning in LLMs
- The Expressive Power of Transformers with Chain of Thought
- An Investigation of Neuron Activation as a Unified Lens to Explain Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs
- Reasoning with Latent Thoughts: On the Power of Looped Transformers
- CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation
- To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
- Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization
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