Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds
Paper • 2608.13069 • Published
Behavioral Reprogramming & Persona Alignment in Open-Weight LLMs
Official model card and research overview for the study:
"Behavioral Modification Boundaries of Open-Weight Large Language Models Under Direct Preference Optimization"
- Paper: arXiv:2608.13069
- Experimental Logs & Code: GitHub Repository
Model & Research Overview
This project provides an end-to-end framework for assertive behavioral reprogramming and persona alignment in open-weight models, executed on large-scale HPC infrastructure (EuroHPC Leonardo).
Key Technical Highlights:
- HPC Scalability: Validated across tens of thousands of GPU hours with extensive parameter sweeps.
- 6 Comprehensive Experiments: Covering learning curves, base vs. instruct divergence, cross-lingual transfer resilience, and persona stress tests.
- Direct Preference Optimization (DPO): Advanced behavioral steering designed for multimodal agents and industrial avatar pipelines.
Access & Commercial Acquisition
The technical reproduction logs, Slurm batch configurations, and verification metrics are open for academic audit on GitHub.
The fine-tuned model checkpoints, custom LoRA adapters, and proprietary multimodal avatar stack are packaged for industrial deployment and full IP licensing.
For commercial licensing, enterprise integration, or asset acquisition, please contact the author directly via LinkedIn or registered institutional email.