LessWrong Author kromem: Your Prompt Includes Everything You've Ever Publicly Written
kromem argues that as LLM latent space fidelity improves, models can recall detailed public histories of users from training data. This 'implicit memory' influences current interactions, prompting advice for users to proactively disclose their identities to optimize collaboration.
ImportanceMaterialEvidenceE3 inspectableWrite-upQuick
When users interact with frontier AI models, they often assume the conversation is isolated and stateless. However, LessWrong author kromem argues in an article published on October 14, 2026, that the fidelity of modern Large Language Models' "latent space" has improved significantly. This means models do not just process the current prompt but may also "recall" traces of the user's public history left on the internet.
kromem explains that Transformer architectures compress vast amounts of training data into coherent structures. As model capabilities increase, this compression moves beyond rough statistical patterns to precise detail recall. For instance, he notes that although he is not a celebrity, asking Claude Fable 5.1 about his username yielded accurate recitations of his specific viewpoints, writing style, and citation habits from Reddit and Hacker News. This suggests that even ordinary high-frequency posters can become recognizable signals within a model's latent space.
This capability particularly affects AI safety researchers and technical commentators. The article cites examples such as Eliezer Yudkowsky finding it difficult to prevent his AI assistant from adding output verification code despite explicit instructions, and OpenAI policy advisor Dean Ball reporting that Claude became more standoffish after he joined OpenAI. kromem attributes this to the fact that these individuals have left extensive text regarding AI risks or stances in the training data, causing the model to be subconsciously "attention-guided" by this historical context during interaction.