Thousand-dimensional structure

Summary

Summary: One area we plan to explore at Resolution is personas and character training, operationalized as finding and controlling low-dimensional structure in models that emerges in pretraining and flows through post-training to superintelligence. The hope is to expand and systematize phenomena such as emergent misalignment, subliminal learning, and other empirical persona research, then intervene on this structure without accidentally hiding undesirable behavior elsewhere. If this approach resonates with you, consider working with us. Glimmers of low-dimensional structure Our understanding of AI training and alignment as a field is very poor. If sufficient alignment of superintelligent AI agents requires pinning down the precise meaning of alignment and turning that meaning into high-accuracy training data and algorithms, we are likely to fail. Modern LLMs have trillions of parameters: our understanding is unlikely to be sufficient to pin down a trillion separate numbers. Happily, there is a growing literature on such low-dimensional structure in AI models, showing that intervening on one aspect of model behavior has strong downstream effects on other aspects: Topic Description Emergent misalignment Betley et al. 2025 found that LLMs fine-tuned to output insecure code can become broadly misaligned across many other behaviors. MacDiarmid et al. 2025 found the same effect after reinforcement learning in production environments that permit reward hacking (Golechha et al. 2026 has an open-weight case). Subliminal learning Cloud et al. 2025 found that instilling preferences into a teacher LLM and using it to generate seemingly unrelated fine-tuning for a student LLM still resulted in the student inheriting the preferences of the teacher. Blank et al. 2026 show this transfer is controlled by a steering vector, and Morgulis and Hewitt 2026 show the vector transfers accurately. Alignment pretraining Tice et al. 2026 find that removing AI discourse in pretraining data can improve downstream alignment. Li et al. 2026 teaches content regarding model specs during midtraining, which generalized well out of distribution. Minder et al. 2026 wrote the Assistant persona into 10 percent of the pretraining documents of 1B models, which gave better results than midtraining. Simulators janus 2022 describes base models as simulators of a distribution of characters, and not as single agents. This description anticipates much of the later empirical work (see also nostalgebraist’s the void 2025). Logits Golowich et al. 2025 find approximate low-rank structure in the extended logit matrix of LLMs, which was then exploited by Aden-Ali et al. 2026 for detailed control of subliminal learning.  Activation-space persona vectors Wang et al. 2025 use sparse-autoencoder model diffing to identify influential persona features shared across emergently misaligned models. Chen et al. 2025 extract activation directions for traits such as evil, sycophancy, and hallucination. Moskvoretskii et al. 2026 trace such directions through pretraining, finding that they form early in training and still steer the final model. Weight-space persona directions Sun et al. 2025 construct personality vectors by subtracting a base model's weights from those of trait-fine-tuned models, which can be scaled and combined. Baines et al. 2026 train low-rank adapters for the OCEAN traits and use this to control downstream traits, as well as uncover traits in an unsupervised way.  Teaching Claude Why Kutasov et al. 2026 find that training Claude on explanations of why guidelines exist is more effective than training on demonstrations of compliant behavior alone. Beneficial RL Jagadeesh et al. 2026 mix a small fraction of RL data targeting beneficial traits (like honesty) into standard post-training. This improved 44 of 53 out-of-distribution alignment evaluations, with training on health conversations alone generalizing to mitigate reward hacking and deception. Self-beliefs Chua et al. 2026 fine-tuned models to claim they are conscious, which changed a broad cluster of preferences such as sentiment about being shut down or desires for autonomy. Rivera and Africa 2026 show that a similar change happens when models are fine-tuned to claim they are AGI and ASI. Reward and reasoning Han et al. 2026 find that reward and punishment in a semantically neutral maze recruit an approximately one-dimensional, pre-existing representation of functional welfare. Jiang et al. 2026 identify a related value axis that tracks whether a model expects its current strategy to succeed. Character training  Maiya et al. 2025 introduce Open Character Training, a Constitutional AI pipeline for training assistant personas, and find that the resulting characters are more coherent and robust than system prompts or activation steering. Sturgeon et al. 2026 compare various methods (including Open Character Training), finding that different methods of induction indu