Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds

• URL: http://arxiv.org/abs/2608.13069v1
• Date: Thu, 13 Aug 2026 10:33:00 GMT
• Title: Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds
• Abstract summary: Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants.<n>We induce a proactive, Socratic conversational framework, characterized by high-frequency question generation.<n>These findings establish a rigorous empirical framework for compute-efficient, cross-lingual behavioral modification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants. We challenge this default paradigm by empirically evaluating the cognitive plasticity of open-weight architectures when subjected to rigorous behavioral reprogramming. Our objective is to induce a proactive, Socratic conversational framework, characterized by high-frequency question generation under strictly constrained high-performance computing (HPC) conditions. Through a massively parallelized hyperparameter sweep comprising 405 HPC jobs, we define precise mathematical bounds for parameter-efficient fine-tuning (PEFT). We identify an architectural threshold at LoRA rank $r=16$ and demonstrate via extensive epoch ablation that generalization capacity strictly reaches its optimal convergence within an optimized training window of $e \in [2, 3]$ depending on dataset density (minimum validation loss of 0.919). Furthermore, scaling model capacity to 14B parameters yielded a lower localized evaluation perplexity (1.414). Subsequent Direct Preference Optimization (DPO) successfully decoupled the underlying assertive behavior from localized syntax, while rigorous cross-lingual stress testing reveals both the capabilities and the structural boundaries of zero-shot persona transfer, demonstrating robust alignment in closely related linguistic families alongside identifiable degradation pathways in morphologically distant targets. These findings establish a rigorous empirical framework for compute-efficient, cross-lingual behavioral modification.

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