mlsanityData to 14 Oct 2026

What Survives When Activation Steering Is Used as Memory

arXiv 2610.14958 ↗First posted 10 Oct 2026cs.AI cs.CL cs.LGcited by 0

On the map: Mechanistic interpretability → Activation steering methods

Abstract (arXiv)

Activation steering has recently been used as a memory for skills: past experiences of an agent are stored as steering vectors and retrieved later. Facts and preferences about the user are assumed to need text instead. We test that assumption with a training-free activation memory beside a frozen LLM. The change an experience causes in late-layer states is stored as a shift, filed under a whitened key for the conversation's gist, and added back in a later session only when the gist matches. We argue that such a shift is a fixed average of an effect that attention, in context, recomputes for every question, so only question-independent content can survive. In a pre-registered study on Qwen3.5-2B and 9B, with judges from four model families, the memory is selective (about 98% of unrelated replies are unchanged), but facts are stored as the word to say, in the last layers only, and are almost never used (at most 15% clean use, against 96-98% in context). Leanings transfer only in a narrow strength window. Most strikingly, the reference subtracted when writing a preference decides whether its concept or its direction survives: unless the opposite is subtracted, a stored dislike is recalled as a like.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Activation steering methods, one of 13 directions in Mechanistic interpretability. Builds on 18 of its 24 references that are in the map. First posted on arXiv 10 Oct 2026.

Builds on

18 of 24 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026On the Limits of Steering Vectors for Preference-Aligned Generation2 Jul
  2. 2026Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering29 Jun
  3. 2025Understanding (Un)Reliability of Steering Vectors in Language Models28 May
  4. 2025Mitigating Memorization in LLMs using Activation Steering8 Mar
  5. 2024Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization28 May
  6. 2023Steering Llama 2 via Contrastive Activation Addition9 Dec
  7. 2023Steering Language Models With Activation Engineering20 Aug

Other references · 11

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2022Locating and Editing Factual Associations in GPTcited by 2,691
  4. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  5. 2022Mass-Editing Memory in a Transformercited by 1,001
  6. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 623
  7. 2023Dissecting Recall of Factual Associations in Auto-Regressive Language Modelscited by 528
  8. 2021Linear Transformers Are Secretly Fast Weight Programmerscited by 502
  9. 2023In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steeringcited by 245
  10. 2024Programming Refusal with Conditional Activation Steeringcited by 206
  11. 2022Can Large Language Models Truly Understand Prompts? A Case Study with Negated Promptscited by 59

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  1. 2026Where Steering Signals Come From: Activation Source Selection in Activation Steering
  2. 2026One Token Can Be Enough: Bridging Prompting and Activation Steering with Prefix Steering
  3. 2026When Steering Fails in Latent Reasoning: A Latent-to-Language Transition Gap
  4. 2026When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents
  5. 2026Memory Inception: Latent-Space KV Cache Manipulation for Steering LLMs
  6. 2026Endogenous Resistance to Activation Steering in Language Models
  7. 2026To Copy or Not to Copy: Copying Is Easier to Induce Than Recall
  8. 2026Memory Makes the Difference: Evaluating How Different Memory Roles Shape Conversational Agents