What Survives When Activation Steering Is Used as Memory
cs.AI, cs.CL, cs.LG
Submitted: 2026-10-10
Updated: 2026-10-19
Comments: 12 pages, 7 figures, 2 tables. Code: https://github.com/vedant-jumle/Seahorse
Code: https://github.com/vedant-jumle/Seahorse
License: http://creativecommons.org/licenses/by/4.0/
The gist: Activation steering has recently been used as a memory for skills: past experiences of an agent are stored as steering vectors and retrieved later.
Terminology
Abstract
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.
Sources
- Eliciting Latent Predictions from Transformers with the Tuned Lens
- Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization
- Emergent Introspective Awareness in Large Language Models
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- MemGPT: Towards LLMs as Operating Systems
- Qwen3.5-Omni Technical Report
- On the Limits of Steering Vectors for Preference-Aligned Generation
- Mitigating Memorization in LLMs using Activation Steering
- Steering Language Models With Activation Engineering
- Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering
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