mlsanityData to 14 Oct 2026

When Steering Fails in Latent Reasoning: A Latent-to-Language Transition Gap

arXiv 2609.21662 ↗First posted 18 Sep 2026cs.CL cs.AIcited by 0

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

also in 1 more
Abstract (arXiv)

Activation steering has become a widely used approach for controlling language models during explicit chain-of-thought (CoT) reasoning, motivating its extension to latent CoT. However, we find that steering continuous thoughts produces substantially weaker effects on subsequent language generation than steering explicit CoT, even when the hidden representations are moved by comparable amounts. We first show that task information remains identifiable in continuous thoughts. Hence, we hypothesize a latent-to-language transition gap, in which an intervention effect in latent space fails to transfer to language generation. Two further results support this hypothesis: the output distribution changes abruptly at the transition boundary, and task-related directions exert much weaker bidirectional control in latent CoT than in explicit CoT. These findings identify the transition interface as a central target for evaluating and designing future latent-steering methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 12 of its 12 references that are in the map. First posted on arXiv 18 Sep 2026.

Builds on

12 of 12 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation28 Feb
  2. 2025SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs17 Feb
  3. 2024Training Large Language Models to Reason in a Continuous Latent Space9 Dec
  4. 2024From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step23 May
  5. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting7 May
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 6

  1. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,336
  2. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
  3. 2023Think before you speak: Training Language Models With Pause Tokenscited by 264
  4. 2026The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlookcited by 43
  5. 2024On the Relationship between Truth and Political Bias in Language Modelscited by 31
  6. 2025SAE-SSV: Supervised Steering in Sparse Representation Spaces for Reliable Control of Language Modelscited by 29

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  1. 2026One Token Can Be Enough: Bridging Prompting and Activation Steering with Prefix Steering
  2. 2026Do Latent-CoT Models Think Step-by-Step? A Mechanistic Study on Sequential Reasoning Tasks
  3. 2026LLM Reasoning Is Latent, Not the Chain of Thought
  4. 2026Mechanistic Interpretability of Chain-of-Thought Reasoning via Sequential Activation Patching
  5. 2026The Illusion of Superposition? A Principled Analysis of Latent Thinking in Language Models
  6. 2026Post-Hoc Reasoning in Chain of Thought: Decoding and Steering Pre-Committed Answers
  7. 2025Do Latent Tokens Think? A Causal and Adversarial Analysis of Chain-of-Continuous-Thought
  8. 2026Therefore I am. I Think