arXiv:2607.00006v1 Announce Type: new Abstract: Beckmann & Butlin's (2026) ontological framework for the LLM individuation problem inherits an unargued cross-regime co-reference assumption from the persona-vectors literature: that the same direction picks out the same content under prompt-conditioning, gradient-descent fine-tuning, and inference-time steering. We present four empirical wedges from persona-topology experiments on Qwen3-4B-Instruct and Mistral-7B-Instruct-v0.2 - non-collinearity of prompt-extracted vectors and fine-tune basins; fictional personas displacing the model along real-
This research addresses a foundational assumption in AI persona creation, becoming critical as LLMs are integrated into systems requiring consistent and controllable personality traits.
Understanding the 'LLM individuation problem' is crucial for developing robust, predictable, and trustworthy AI agents that can maintain consistent personas across different operational contexts.
The prior assumption that a 'persona-vector' reliably maps to the same content across different LLM conditioning methods is now empirically challenged, necessitating a re-evaluation of persona control mechanisms.
- · AI ethics researchers
- · Developers of robust AI persona frameworks
- · Academic AI research
- · Overly simplistic persona-vector methodologies
- · Applications relying on unstable LLM personas
Immediate difficulty in predictably engineering LLM behavior and psychological consistency for advanced AI applications.
Increased research focus on developing multi-modal or context-aware persona embedding techniques to address the regime-dependence.
The development of LLMs that autonomously adapt their internal representations of persona based on operational context, leading to more fluid but potentially less controllable AI identities.
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Read at arXiv cs.CL