Same Outcome, Different Readout: What Does a Steerable Valence Direction in LLMs Represent?
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Abstract (arXiv)
Claims about what an internal direction in an LLM represents need evidential constraints beyond an observer's prior beliefs about the system. Decodability and successful activation steering do not, by themselves, establish which construct the direction tracks. This gap is especially consequential for welfare-relevant interpretations, where a proposed functional state must be distinguished from correlated features of the extraction contrast. We treat the question as one of construct validity and study a good-bad outcome direction in a maze task, using controlled interventions that separate the realised outcome from the informational history through which it became known. Across multiple LLM checkpoints, directions fitted on one explicit outcome encoding transfer well to another, indicating that the readout is not tied to surface form. When the same realised outcome is reached through announced and unannounced histories, however, transfer degrades substantially: even after both histories receive the same explicit outcome, the post-event readout remains strongly conditioned on the earlier announcement. In the base model, steering along the direction changes actions, yet removing it leaves the natural cue effect almost intact. In a matched maze-RL run, the post-RL direction becomes substantially more predictive of reference-MDP remaining return and the policy becomes more dependent on it at the tested sites, while the history dependence persists. These dissociations support a functional, value-related interpretation of the direction, but not its identification with a history-invariant scalar valence state.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 30 references that are in the map. First posted on arXiv 19 Sep 2026.
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References · 10
- 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
- 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
- 2019Designing and Interpreting Probes with Control Taskscited by 640
- 2019Measurement and Fairnesscited by 308
- 2020Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?cited by 151
- 2023Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patchingcited by 80
- 2023Towards Evaluating AI Systems for Moral Status Using Self-Reportscited by 22
- 2026How's it going? Reinforcement learning in language models recruits a functional welfare axiscited by 6
- 2026Sparse Reward Subsystem in Large Language Modelscited by 4
- 2026Functional Emotions or Situational Contexts? A Discriminating Test from the Mythos Preview System Cardcited by 3
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- 2025Emergent Bayesian Behaviour and Optimal Cue Combination in LLMs
- 2026When Is a Steerable Concept Representation Real? Measurement Confounds in a Cross-Family Audit of Neuroscience Parallels in LLMs
- 2026Lagged Coupling: Internal Representations Become Readable Before They Become Causal
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- 2026Representation Without Control: Testing the Realization Effect in Language Models
- 2026When Steering Fails in Latent Reasoning: A Latent-to-Language Transition Gap