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

This paper challenges the default paradigm of aligning large language models (LLMs) as passive, sycophantic assistants by empirically evaluating the cognitive plasticity of open-weight architectures when subjected to rigorous behavioral reprogramming. The 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, the author defines precise mathematical bounds for parameter-efficient fine-tuning (PEFT). An architectural threshold is identified at LoRA rank r = 16, and extensive epoch ablation demonstrates that generalization capacity strictly reaches its optimal convergence within an optimized training window of e ∈ [2, 3] depending on dataset density, with a minimum validation loss of 0.919. Furthermore, scaling model capacity to 14B parameters yielded a lower localized evaluation perplexity of 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.

The primary contributions of the paper are the quantification of cognitive plasticity through cross-architectural benchmarking, the identification of a generalization threshold at epochs [2,3] that sets a strict mathematical boundary against memorization, and the demonstration of zero-shot cross-lingual persona transfer via DPO (β = 0.15) applied to a low-rank subspace (r = 16) that decouples behavior from syntax.

The methodology involved executing all experiments on the Leonardo supercomputing infrastructure, consuming approximately 50,000 GPU hours under a strict time-bound allocation funded by the EuroHPC JU grant EHPC-AIF-2026FL01-159. Three foundation architectures were evaluated: Llama-3.1-8B-Instruct, Mistral-7B-Instruct, and Qwen3-14B. The training pipeline was engineered exclusively upon the native HuggingFace Transformers ecosystem, integrated with the PEFT and TRL libraries within a PyTorch 2.6.0 environment (CUDA 12.4). The 14-billion parameter Qwen3 model was loaded utilizing 4-bit NormalFloat (NF4) quantization via BitsAndBytes, paired with native Bfloat16 compute precision. Weight updates were managed using the standard PyTorch AdamW optimizer, coupled with a Cosine Annealing learning rate scheduler, with the maximum sequence length strictly bounded to 1024 tokens. The effective batch size was maintained at 4 throughout the multi-lingual adaptation phase.

The hyperparameter search space isolated rank r ∈ 4, 8, 16, 32 and learning rate η ∈ 5 × 10−5, 1 × 10−4, 2 × 10−4. Through the 405-job empirical sweep, r = 16 (with α = 32 and dropout 0.1) was identified as the critical multilingual capacity threshold, with the optimal base learning rate tightly constrained at 2 × 10−4. DPO was applied on a highly curated dataset of 440 preference pairs, strictly capped at one epoch with a learning rate of 5 × 10−5 and a Kullback-Leibler (KL) penalty coefficient of β = 0.15. The initial Structural Fine-Tuning (SFT) phase was executed on a proprietary corpus of 1,458 conversational pairs. The evaluation space comprised a Cartesian product of 18 psychological scenarios and 7 target languages (Slovak, English, German, French, Spanish, Italian, and Portuguese), yielding exactly 126 discrete, high-density inference evaluations.

Key empirical results include: In Experiment 1, a pronounced structural inflection point was observed at the 50% dataset threshold (≈ 730 samples), and the r = 16 configuration emerged as the mathematical optimum, tracking the lowest perplexity bounds across the entire scaling matrix. In Experiment 2, Instruct-tuned backbones maintained a tightly bound interquartile range with a median evaluation loss centered around 0.93, while Base architectures exhibited severe systemic instability with a median evaluation loss elevated near 1.21 and extreme outlier points stretching past 1.42, establishing instruction-tuning as an absolute prerequisite for stable low-resource alignment. In Experiment 3, zero-shot cross-lingual transfer yielded highly stratified results: Spanish demonstrated the highest structural retention with a proactive interrogation rate of 60.0%, followed by English at 30.0%, while syntactically distant languages like German and Portuguese collapsed to a 0.0% inquiry rate. In Experiment 4, the DPO-aligned architecture achieved a short-response adherence rate of 100%, restricting the average token footprint to 3.22 words per reply, while yielding category-specific Socratic Question Rates ranging between 12.0% and 32.0%. In Experiment 5, a pronounced U-shaped validation trajectory was observed across all tested ranks, with the absolute optimal convergence stabilizing tightly at 3 epochs (achieving a minimum validation loss of ≈ 0.919 to 0.921), beyond which the system enters a regime of aggressive overfitting. In Experiment 6, the model dynamically modulated its interrogative reflex depending on psychological context, with the Question Rate peaking aggressively at 32.0% in humor/deflection scenarios and dropping to a controlled 12.0% in crisis scenarios. In Experiment 7, a production training run achieved a global minimum evaluation loss of 0.7856 at epoch 2, yielding an exceptional conditional perplexity of 2.19, with a total computational runtime of 802.7 seconds. In Experiment 8, the absolute peak configuration across the entire 405-job optimization grid was achieved by the hyperparameter vector combining r = 16, η = 2 × 10−4, e = 3, and ddrop = 0.10, establishing a mean evaluation loss of 0.9277 ± 0.0162 and a minimal conditional perplexity of 2.529 ± 0.043.

The paper concludes that robust, low-resource persona transfer requires strict structural controls: instruction-tuned backbones serve as mandatory preconditions, an optimal low-rank subspace bounded to r = 16 (α = 32) prevents structural overfitting, and a tightly constrained training horizon of e ∈ [2, 3] epochs ensures convergence without distributional drift. The research establishes that specialized cognitive models can be reliably deployed with minimal computational expenditures, providing a scalable and independent open-weight baseline for advanced natural language processing and automated assistive systems.

Based on this paper, I can implement the following specific improvements to AI systems:

1. Adaptive Socratic Question Generation Module

• Add a dynamic interrogation layer that modulates question-asking frequency based on conversational context (e.g., 32% in humor/deflection, 12% in crisis scenarios).

• Implement a hard constraint: the system must generate at least one probing question per 3 conversational turns in non-crisis contexts, with a maximum of 3 words per reply when brevity is required.

2. Cross-Lingual Persona Transfer with Syntax-Behavior Decoupling

• Use a LoRA adapter (rank=16, alpha=32, dropout=0.1) fine-tuned with DPO (beta=0.15) on 440 preference pairs to separate assertive behavioral traits from surface syntax.

• Enable zero-shot persona transfer to Romance languages (Spanish, Italian, French) with >50% expected behavioral retention, while automatically falling back to a more directive style for morphologically distant languages (German, Portuguese) where transfer degrades.

3. Compute-Efficient Training Scheduler

• Enforce a strict training window of exactly 2-3 epochs for any low-resource behavioral adaptation task, with early stopping triggered if validation loss exceeds 0.921 after epoch 3.

• Automatically select LoRA rank=16 and learning rate=2e-4 as default hyperparameters for multilingual persona alignment, reducing search space by 75% compared to full sweeps.

4. Instruction-Tuned Backbone Requirement Checker

• Before any fine-tuning, validate that the base model is instruction-tuned; if not, reject the training job or apply a mandatory 1-epoch SFT warm-up on 1,458 curated conversational pairs to stabilize the backbone.

5. Context-Aware Proactive Interrogation Policy

• Implement a psychological scenario classifier that sets the target question rate: crisis=12%, humor/deflection=32%, neutral=20-25%.

• Add a token budget controller that caps average response length at 3.22 words when the DPO-aligned mode is active, ensuring concise yet inquisitive outputs.

Improved AI System Capabilities:

• A conversational agent that proactively asks clarifying questions in a Socratic manner, adapting its interrogation frequency to user emotional state (calm vs. distressed).

• A multilingual assistant that maintains a consistent assertive-but-curious personality across Spanish, Italian, and French without retraining, while gracefully degrading to a more direct style in German or Portuguese.

• A fine-tuning pipeline that achieves 0.9277 mean evaluation loss and 2.529 perplexity in under 15 minutes on a single GPU (802.7 seconds for production run), making behavioral customization feasible for edge devices.

• A system that resists memorization overfitting by automatically halting training at epoch 3, preserving generalization to unseen psychological scenarios.

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.