Translations generated by Large Language Models (LLMs) often exhibit unnatural stylistic traces distinct from human-written translations, which we refer to as LLM translationese. We propose \textit{MELT}, a training-free post-editing framework for mitigating LLM translationese across five target languages (Korean, Chinese, Japanese, German, and French) without weight updates. MELT defines translationese patterns based on faithfulness, voice, register, naturalness, and surface form, and combines a three-stage verification gate with selective multi-agent debate to preserve meaning and mitigate self-bias. Experiments on FLORES-200 show that verification contributes most to quality improvement and that MELT achieves the lowest calibration error with respect to Oracle preference across the five languages. Our analysis further shows that LLM translationese exhibits language-specific realizations while sharing a common mechanism of English-structure mimicry.

Prior benchmarks for evaluating the domain-specific knowledge of large language models (LLMs) lack the scalability to handle complex academic tasks. To address this, we introduce ScholarBench, a benchmark centered on deep expert knowledge and complex academic problem-solving, which evaluates the academic reasoning ability of LLMs and is constructed through a three-step process. ScholarBench targets more specialized and logically complex contexts derived from academic literature, encompassing five distinct problem types. Unlike prior benchmarks, ScholarBench evaluates the abstraction, comprehension, and reasoning capabilities of LLMs across eight distinct research domains. To ensure high-quality evaluation data, we define category-specific example attributes and design questions that are aligned with the characteristic research methodologies and discourse structures of each domain. Additionally, this benchmark operates as an English-Korean bilingual dataset, facilitating simultaneous evaluation for linguistic capabilities of LLMs in both languages. The benchmark comprises 5,031 examples in Korean and 5,309 in English, with even state-of-the-art models like o3-mini achieving an average evaluation score of only 0.543, demonstrating the challenging nature of this benchmark.