Dynamic Incentive Structures and Transformer-Based Competency Mapping for Innovation Talent Evaluation in Development Programs

Abstract

This research propose a dynamic incentive framework integrated with transformer-based competency mapping to address the limitations of static talent evaluation systems in development programs. The core innovation lies in the Adaptive Incentive Engine (AIE), which dynamically adjusts rewards based on real-time performance metrics, skill progression, and peer-relative benchmarks, thereby fostering sustained engagement and alignment with developmental goals. The system employs a dual-layer evaluation mechanism, where a transformer-based model processes multi-modal inputs to generate high-dimensional skill embeddings, while a feedback adoption layer delivers contextual nudges to participants exhibiting suboptimal progress. Furthermore, the AIE replaces conventional static reward structures by modulating resource allocation and prioritizing high-performing individuals for advanced opportunities. The implementation leverages fine-tuned RoBERTa-large models for competency mapping and a distributed reinforcement learning framework for adaptive weight calibration, ensuring scalability across large participant cohorts. Unlike traditional rubric-based approaches, our method captures nuanced skill evolution through latent space representations and hybrid nudge delivery, combining digital and institutional channels to reinforce behavioral change. The proposed framework demonstrates significant potential to enhance talent development outcomes by bridging the gap between quantitative metrics and qualitative assessments, offering a responsive and data-driven alternative to existing evaluation paradigms.