Peer review is buckling under submission volume, and LLM assistance is gaining traction. This paper asks how four current LLMs treat 200 already-published articles from top-tier journals such as Nature, Science, and Cell. The authors find that model recommendations are overwhelmingly harsh and starkly inconsistent: $605$ of $800$ reviews ($75.6\%$) recommended major revision or rejection, yet the rate for any single model ranged from $17.5\%$ to $100\%$ ($p < 0.001$). The takeaway is that LLM editorial signals are too variable to replace human judgment, though convergent criticisms may still be useful.

Museum description generation is usually framed as a data-to-text task where richer metadata improves grounding, yet heterogeneous and unevenly documented catalogue records break that assumption. This paper introduces a two-phase framework—observational across 10,250 artefacts and controlled across 500 objects—to isolate how metadata quantity, field type, and image access shape what LLMs include, omit, and infer. The central finding is that field identity, not just field count, determines faithfulness: context fields are omitted while content fields trigger unsupported elaboration, and images can increase inference rather than grounding. The authors argue that aggregate reference similarity masks these failures and motivate field- and claim-level evaluation.

Extracting hypotheses and their supporting statistical evidence from full-text scientific articles is challenging due to document length and the distribution of scientific arguments across sections. This paper proposes a two-stage retrieve-and-extract pipeline that first links an abstract finding to its corresponding hypothesis, then extracts the statistical evidence supporting that hypothesis. Through controlled ablations varying context quantity ($k \in \{5, 10, 20\}$), retrieval quality (standard RAG, reranking, fine-tuned retriever), and oracle paragraph settings, the authors demonstrate that hypothesis extraction is primarily bounded by retrieval quality, while evidence extraction faces persistent extractor limitations even with perfect paragraph selection.