Traditional marketing uplift models suffer from a fundamental limitation: they typically operate under static assumptions that fail to capture the temporal dynamics of customer responses to marketing interventions. This paper introduces a novel framework that combines causal forest algorithms with deep reinforcement learning to dynamically model marketing uplift effects. Our approach enables the real-time identification of heterogeneous treatment effects across customer segments while simultaneously optimizing intervention strategies through an adaptive learning mechanism. The key innovations of our framework include the following: (1) a counterfactual simulation environment that emulates diverse customer response patterns; (2) an adaptive reward mechanism that captures both immediate and long-term intervention outcomes; and (3) a dynamic policy optimization process that continually refines targeting strategies based on evolving customer behaviors. Empirical evaluations on both simulated and real-world marketing campaign data demonstrate that our approach significantly outperforms traditional static uplift models, achieving up to a 27% improvement in targeting efficiency and an 18% increase in the return on marketing investment. The framework leverages inherent symmetries in customer-intervention interactions, where balanced and symmetric reward structures ensure fair optimization across diverse customer segments. The proposed framework addresses the limitations of existing methods by effectively modeling the dynamic and heterogeneous nature of customer responses to marketing interventions, providing marketers with a powerful tool for implementing personalized and adaptive campaign strategies.

This paper introduces LLaMA-UTP, a novel framework for classifying and interpreting uncertain tax positions (UTPs) in technology companies. Tax professionals face significant challenges when analyzing UTPs: navigating vast bodies of intricate legal text, interpreting highly technical domain-specific language, maintaining accurate citations to authoritative sources, and producing justifications that comply with legal standards. We address these challenges with a knowledge-guided expert mixture system built upon a domain-adapted LLaMA architecture incorporating retrieval-augmented generation to ground analysis in relevant tax authorities, specialized expert subnetworks for different tax knowledge domains, and a tax citation knowledge graph that enhances interpretability through structured reasoning paths. Our comprehensive evaluation demonstrates that LLaMA-UTP achieves 90.5% accuracy in classifying UTP risk levels, significantly outperforming baseline models including LegalBERT (82.0%) and GPT-3.5 (78.4%). The system generates explanations with 93% citation accuracy and zero hallucinated references, as validated by tax professionals who rated its outputs as highly aligned with expert reasoning (4.2/5). Through ablation studies, we demonstrate that each architectural component contributes measurably to both accuracy and interpretability. This work makes significant contributions to both AI/ML methodology and tax law analysis by showcasing howknowledge-guided expert mixtures can enhance model performance in highly specialized professional domains while maintaining the explainability necessary for high-stakes legal applications.

Battery health monitoring and remaining useful life (RUL) estimation for electric vehicles face two significant challenges: battery data heterogeneity and sample imbalance. This study presents a novel approach based on Transformer architecture to specifically address these issues. We utilized the NASA lithium-ion battery cycling dataset, which contains charge-discharge and impedance measurement data under various temperature conditions. To tackle data heterogeneity, we developed a multimodal feature fusion strategy that effectively integrates battery sensor data from different sources and formats, including time-series charge-discharge sensor data and spectral impedance sensor measurements. To mitigate sample imbalance, we implemented an adaptive resampling technique and hierarchical attention mechanism, enhancing the model’s ability to recognize rare degradation patterns. Our Transformer-based model captures long-term dependencies in the battery degradation process through its self-attention mechanism. Experimental results demonstrate that the proposed solution significantly improves battery degradation prediction accuracy, achieving a 21.3% increase in accuracy when processing heterogeneous data and a 24.5% improvement in prediction capability for imbalanced samples compared to traditional methods. Additionally, through case studies, we validate the applicability of this method in actual electric vehicle battery management systems, providing reliable data support for battery preventive maintenance and replacement decisions. The findings have important implications for enhancing the reliability and economic efficiency of electric vehicle battery management systems.

The widespread adoption of electric vehicles (EVs) necessitates advanced energy management strategies to alleviate range anxiety and improve overall energy efficiency. This study presents a novel framework for optimizing energy consumption in EVs by integrating driver behavior patterns, road conditions, and environmental factors. Utilizing a comprehensive dataset of 3395 high-resolution charging sessions from 85 EV drivers across 25 workplace locations, we developed a multi-modal prediction model that captures the complex interactions between driving behavior and environmental conditions. The proposed methodology employs a combination of driving scenario recognition and reinforcement learning techniques to optimize energy usage. Specifically, we utilize contrastive learning to extract meaningful representations of driving states by leveraging the symmetric relationships between positive pairs and the asymmetric nature of negative pairs and implement graph attention networks to model the intricate relationships between road environments and driving behaviors. Our experimental results demonstrate that the proposed framework achieves a significant reduction in energy consumption compared to baseline methods, with an average improvement of 17.3% in energy efficiency under various driving conditions. Furthermore, we introduce an adaptive real-time optimization strategy that dynamically adjusts vehicle parameters based on instantaneous driving patterns and environmental contexts. This research contributes to the advancement of intelligent energy management systems for EVs and provides insights into the development of more efficient and environmentally sustainable transportation solutions.

Fair value hierarchy classification under IFRS 13 presents a significant challenge in financial reporting, requiring accurate categorization of financial instruments into Level 1 (directly observable prices), Level 2 (indirectly observable inputs), or Level 3 (unobservable inputs). This classification critically impacts perceived valuation reliability, yet remains inconsistent when performed manually. We propose a novel hybrid architecture combining transformer-based attention with graph neural networks (GNNs) that jointly exploits temporal market patterns and inter-instrument relational dependencies. Our approach leverages transformer encoders to capture time-series dynamics in market data while utilizing graph neural networks to model structural relationships between instruments. To address limited labeled data availability, we implement self-supervised pretraining on unlabeled market data, enhancing model efficiency. The architecture’s attention mechanisms provide interpretable explanations crucial for audit and regulatory compliance. Experiments on open-source datasets of derivatives and structured products demonstrate that our model achieves 88% classification accuracy, significantly outperforming traditional methods including rule-based classifiers (73%) and XGBoost (80%). Notably, our model excels at identifying Level 3 instruments (F1-score 0.90) while maintaining interpretability through attention-based explanations that highlight key temporal periods and influential instrument relationships. Ablation studies confirm the complementary contributions of both transformer and GNN components. This work bridges advanced deep learning with financial compliance requirements, facilitating more consistent, accurate, and transparent valuation procedures that align with regulatory standards.