Cementitious materials teams need machine-readable databases of compressive strength records to support mix-design optimization. However, manual curation of the literature is too slow and hard to scale. We present CemLit-IE, a five-stage zero-shot pipeline, converting raw PDFs into fully attributed records with provenance. The core challenge is that the relevant information is distributed across tables, text, and figures. Even though table-only extraction produces plausible numbers, the results are not auditable. CemLit-IE addresses this through a series of methods, including keyword-weighted page scoring, structured table injection, value-provenance schema, and review queue. On a benchmark of 20 papers with 655 expert-annotated records, CemLit-IE achieves a recall of 76.3% with 99.2% precision and 100% exact-match accuracy in a text-only model. The recall can be raised to 83.5% when figure reading is enabled. The ablation study shows that each design choice contributes to improving precision. As for the cost, a separate 270-paper journal-batch run extracted 2,039 strength records at $31.28 total API cost. All data and code are publicly released.

Cultural reasoning lies at the core of many emerging AI applications, yet existing benchmarks provide limited insight into how well models understand and contextualize cultural artifacts. We introduce ExhibitionBench, the first benchmark that frames exhibition curation as a high-level evaluation testbed, comprising 300 institution-derived exhibition and thematic collection records, 23,658 artifacts, and three interconnected tasks that evaluate complementary aspects of cultural understanding and reasoning, translating them into user-perceivable utilities. Extensive experiments across 29 systems reveal a significant gap between frontier LLMs and expert-level competence, particularly on tasks that require collection-level coherence and thematic understanding. Performance gains from scaling, prompting, and multimodal inputs are limited and highly model-dependent, whereas tailored model optimizations can equip open-weight models with better capabilities that support on-premise deployment. These findings highlight the difficulty of reasoning over cultural collections and provide a practical benchmark for developing more culturally grounded and reliable AI systems. The full project is released at https://github.com/mengze-hong/ExhibitionBench.