This report synthesises findings from 10 peer-reviewed papers addressing the following research question: To what extent does cross-domain finetuning improve DeepSeek-V3's robustness to distribution shifts in GPQA Diamond questions. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: To what extent does cross-domain finetuning improve DeepSeek-V3's robustness to distribution shifts in GPQA Diamond questions? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.

Machine-generated literature synthesis. Content is derived from peer-reviewed papers; see individual sources for authoritative data. Automated review score: 8.5/10. Published by Assignee Research (https://assignee.net).