Click-through rate (CTR) prediction is a critical task for e-commerce advertising systems. However, industrial ranking models still rely on sparse IDs and historical behavior signals, which limits their generalization to cold-start products, long-tail queries, and complex user intents. Although large language models (LLMs) provide strong semantic understanding and can generate behavioral rationales, directly using them for online ranking is impractical under strict latency constraints.

We propose GRAM-V2, a rationale-supervised ranking framework that transfers LLM-generated behavioral rationales into an efficient CTR model. GRAM-V2 uses a Teacher LLM to generate click-oriented rationales, distills them into a lightweight Student LLM, and aligns a two-tower CTR model with the Student’s representations and rationale signals. During online serving, GRAM-V2 uses only distilled representations and cached item embeddings, without generating rationales autoregressively. Large-scale offline experiments and online A/B testing show that GRAM-V2 improves CTR prediction accuracy, CTR, and advertising revenue while satisfying production latency constraints.

Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object and plays a critical role in knowledge fusion and integration. Traditional EA methods mainly rely on knowledge representation learning, but their performance is often limited under noisy or sparsely supervised scenarios. Recently, large language models (LLMs) have been introduced to EA and achieved notable improvements by leveraging rich semantic knowledge. However, existing LLM-based EA approaches typically treat LLMs as black-box decision makers, resulting in limited interpretability, and the direct use of large-scale triples substantially increases inference cost. To address these challenges, we propose EA-Agent, a reasoning-driven agent for EA. EA-Agent formulates EA as a structured reasoning process with multi-step planning and execution, enabling interpretable alignment decisions. Within this process, it introduces attribute and relation triple selectors to filter redundant triples before feeding them into the LLM, effectively addressing efficiency challenges. Experimental results on three benchmark datasets demonstrate that EA-Agent consistently outperforms existing EA methods and achieves state-of-the-art performance. The source code is available at https://anonymous.4open.science/r/EA-Agent-5696.