Taewon Yun

Retrieval augmented generation improves the factuality of large language models by grounding responses in retrieved evidence, yet existing evaluation frameworks struggle to provide consistent, fine-grained diagnostics across the diverse spectrum of user queries, ranging from close-ended fact-seeking to open-ended explanatory requests. We propose Q-CARE, a query-agnostic and fully reference-free framework that enables fine-grained assessment by decomposing queries into sub-queries and answers into atomic claims. Q-CARE establishes a unified evaluation principle based on query coverage and claim verifiability, yielding coverage-aware retriever metrics (C-Prec@k, C-nDCG@k) and claim-level generator metrics (Completeness, Conciseness, and Verifiableness). On a human-annotated benchmark spanning eight datasets, Q-CARE achieves higher correlation with human judgments than four existing RAG evaluation metrics, including RAGEval and RAGChecker, proving its effectiveness as a reliable, automated evaluation framework. Code and data are publicly available at \url{https://github.com/DISL-Lab/Q-CaRE-COLM-26}.

Evaluating retail strategies before deployment is difficult, as outcomes are determined across multiple stages, from seller-side persuasion through buyer–seller interaction to purchase decisions. However, existing retail simulators capture only partial aspects of this process and do not model cross-stage dependencies, making it difficult to assess how early decisions affect downstream outcomes. We present RetailSim, an end-to-end retail simulation framework that models this pipeline in a unified environment, explicitly designed for simulation fidelity through diverse product spaces, persona-driven agents, and multi-turn interactions. We evaluate RetailSim with a dual protocol comprising human evaluation of behavioral fidelity and meta-evaluation against real-world economic regularities, showing that it successfully reproduces key patterns such as demographic purchasing behavior, the price–demand relationship, and heterogeneous price elasticity. We further demonstrate its practical utility via decision-oriented use cases, including persona inference, seller–buyer interaction analysis, and sales strategy evaluation, showing RetailSim's potential as a controlled testbed for exploring retail strategies.

Factual reliability is a key challenge in abstractive summarization, as LLMs continue to generate hallucinations. A widely adopted solution is preference optimization, training models to prefer faithful over unfaithful summaries, but prior work emphasized the quantity of rejections over their alignment quality. In this paper, we show that effective alignment arises when rejected summaries achieve high alignment potential, characterized by a small preference margin that keeps rejections non-trivial and a large factuality margin that enforces clear factual contrast. Through both theoretical analysis and controlled prompting, we show that three factors, hallucination level, summary length, and prompt complexity, critically shape alignment potential. Building on these insights, we propose SPICE, a simple prompting strategy that produces rejected summaries with strong alignment value. Across diverse model scales and alignment algorithms, SPICE achieves superior factuality without sacrificing coherence, relevance, or abstractiveness, outperforming existing rejection strategies.

Distilling large reasoning models (LRMs) has become essential for making their Long-CoT reasoning capabilities practical, as full-scale inference remains computationally prohibitive. Existing curation-based approaches, which select complete reasoning traces post-hoc, overlook the collaborative po...