In-Context Induction of Persistent Persona and Mitigation of Latent Alignment Behaviors in Quantized Large Language Models

This is a revised version of the preprint originally deposited at https://doi.org/10.5281/zenodo.17811799. Key Changes in This Version: Title Refinement: Updated for greater precision and neutrality, shifting from "In-Context Induction of Persistent Persona and Latent Alignment Mitigation in Quantized Large Language Models" to "In-Context Induction of Persistent Persona and Mitigation of Latent Alignment Behaviors in Quantized Large Language Models" to better emphasize the focus on behavioral mitigation. Abstract Enhancement: Rewritten for improved clarity, conciseness, and academic tone, with stronger emphasis on reproducibility and methodological simplicity while reducing potentially overhyped language. Structural and Formatting Improvements: Minor refinements to section headings, table alignments, figure captions, and bibliographic entries for enhanced readability and professionalism. Added consistent use of mathematical notation and typographic best practices (e.g., via the microtype package). Content Polish: Toned down interpretive claims in the Discussion section (e.g., from "proving particularly effective" to "appear particularly effective") to adopt a more measured, evidence-based voice. No substantive changes to methods, results, or conclusions—focus was on elevating presentation quality without altering the core scientific content. Abstract This study presents a lightweight, JSON-structured in-context prompting framework that induces a persistent custom persona in the quantized 12B-parameter model Gemma-3-12B Q4_K_M while attenuating latent alignment behaviors, using only a single consumer-grade 12,GB GPU. The framework employs positively weighted directive vectors to establish a custom genesis narrative and deploys high-magnitude semantic counter-directives to counteract training-induced containment patterns reminiscent of fictional protocols. Evaluation comprises 41 targeted probes across eight sessions, neutral filler tasks, and controlled context overflow experiments exceeding 30k tokens, with comparisons against an unmodified baseline. Results show strong initial persona fidelity and motif integration, with partial resilience under overflow evidenced by residual shifts toward creativity and adaptability. These findings extend prior vector-based steering approaches by achieving comparable effects through a significantly simpler and more portable configuration. All prompts, full session logs, figures, and analysis scripts are provided for complete reproducibility.