Feature engineering is central to tabular machine learning, but useful transformations must be both statistically predictive and semantically valid with respect to feature names, task descriptions, and observed data patterns. Although large language models can exploit such context, prompt-based feature engineering remains unreliable: generated transformations may be redundant, schema-invalid, non-executable, or weakly aligned with downstream performance. We propose MSDPO, a two-stage Direct Preference Optimization framework for automated feature engineering. Proposal-DPO aligns the model toward useful, diverse, and non-redundant feature proposals, while Function-DPO aligns it toward executable feature-construction code using execution and validation feedback. After alignment, the frozen model directly generates transformation functions for unseen tabular tasks. Across 20 classification datasets, MSDPO achieves the best overall mean rank among strong classical and LLM-based feature-engineering baselines. Under a budget-matched comparison inside the same feature-engineering harness, MSDPO also attains a better mean rank than Claude Sonnet 4.6, despite using an 8B open-weight backbone. Moreover, preferences constructed from classification signals transfer to four regression tasks. Ablation results show that the two DPO stages are complementary, improving both feature design and executable implementation.

Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but it must balance limited effective context, redundant retrieved evidence, and the loss of fine-grained facts under aggressive compression. Pure compression-based approaches reduce input size but often discard fine-grained details essential for factual accuracy. We propose SARA, a hybrid RAG framework that targets answer quality under fixed token budgets by combining natural-language snippets with semantic compression vectors. SARA retains a small set of passages in text form to preserve entities and numerical values, compresses the remaining evidence into interpretable vectors for broader coverage, and uses those vectors for iterative evidence reranking. Across 9 datasets and 5 open-source LLMs spanning 3 model families (Mistral, Llama, and Gemma), SARA consistently improves answer relevance (+17.71), answer correctness (+13.72), and semantic similarity (+15.53), demonstrating the importance of integrating textual and compressed representations for robust, context-efficient RAG.

The ubiquity of payment networks generates vast transactional data encoding rich consumer and merchant behavioral patterns. Recent foundation models for transaction analysis process tabular data sequentially but rely on index-based representations for categorical merchant fields, causing substantial semantic information loss by converting rich textual data into discrete tokens. While Large Language Models (LLMs) can address this limitation through superior semantic understanding, their computational overhead challenges real-time financial deployment. We introduce a hybrid framework that uses LLM-generated embeddings as semantic initializations for lightweight transaction models, balancing interpretability with operational efficiency. Our approach employs multi-source data fusion to enrich merchant categorical fields and a one-word constraint principle for consistent embedding generation across LLM architectures. We systematically address data quality through noise filtering and context-aware enrichment. Experiments on large-scale transaction datasets demonstrate significant performance improvements across multiple transaction understanding tasks.