The success of large language models (LLMs) across domains highlights their potential in scientific tasks, with molecular optimization being a promising frontier. Traditionally, this optimization relies on iterative expert feedback to refine molecules toward desired properties, a process well aligned with LLMs’ strengths. As an experience-driven task, molecular optimization depends critically on the domain feedback and accumulation of historical knowledge. However, none of the existing methods fully leverages such feedback and historical knowledge with reasoning traces and chemical insights. In this work, we propose F2R: Feedback to Reasoning, a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback. Like humans, LLMs can generate imperfect reasoning; F2R is the first framework to use detailed domain feedback to critique and improve this reasoning. This transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience. Consequently, F2R shows remarkable performance.

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.

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.

Knowledge Graph Embedding (KGE) is a powerful technique for predicting missing links in Knowledge Graphs (KGs) by learning the entities and relations. Hyperbolic space has emerged as a promising embedding space for KGs due to its ability to represent hierarchical data. Nevertheless, most existing hyperbolic KGE methods rely on tangent approximation and are not fully hyperbolic, resulting in distortions and inaccuracies. To overcome this limitation, we propose LorentzKG, a fully hyperbolic KGE method that represents entities as points in the Lorentz model and represents relations as the intrinsic transformation—the Lorentz transformations between entities. We demonstrate that the Lorentz transformation, which can be decomposed into Lorentz rotation/reflection and Lorentz boost, captures various types of relations including hierarchical structures. Experimental results show that our LorentzKG achieves state-of-the-art performance.