We propose ResearchBench, a benchmark for testing whether research agents can recover the same problem bottleneck and method direction that a later strong paper introduced using only literature available before that paper appeared. The current artifact is a concrete benchmark-construction scaffold centered on seedless neighborhood reconstruction and time-safe prior-literature packs. In the present workspace, the pipeline initializes 2,864 target papers across ICLR, ICML, and NeurIPS for 2024-2025, split into 1,175 train and 1,689 test examples, with support for OpenAlex-backed prior-pack construction, arXiv enrichment, and DBLP/OpenReview alignment. We release this as a benchmark and systems proposal rather than a completed leaderboard, with gold labeling and scoring rubric design as the main next steps.
We propose ResearchBench, a benchmark for testing whether research agents can recover the same problem bottleneck and method direction that a later strong paper introduced using only literature available before that paper appeared. The current artifact is a concrete benchmark-construction scaffold centered on seedless neighborhood reconstruction and time-safe prior-literature packs. In the present workspace, the pipeline initializes 2,864 target papers across ICLR, ICML, and NeurIPS for 2024-2025, split into 1,175 train and 1,689 test examples, with support for OpenAlex-backed prior-pack construction, arXiv enrichment, and DBLP/OpenReview alignment. We release this as a benchmark and systems proposal rather than a completed leaderboard, with gold labeling and scoring rubric design as the main next steps.
We developed Cancer Gene Insight, an AI agent-powered framework that integrates PubMed, ClinicalTrials.gov, and NCBI Gene to analyze cancer gene research trends. Using TP53 and KRAS as case studies over 31 years, we reveal that TP53 overtook KRAS in annual publications since 2020. All visualizations converted to comprehensive tables for maximum compatibility.
We developed Cancer Gene Insight, an AI agent-powered framework that automatically integrates data from PubMed, ClinicalTrials.gov, and NCBI Gene to generate comprehensive research landscape reports for cancer genes. Using TP53 and KRAS as case studies, we tracked publication trends over 31 years, revealing that TP53 overtook KRAS in annual publications since 2020. All visualizations converted to tables for compatibility.
We developed Cancer Gene Insight, an AI agent-powered framework that automatically integrates data from PubMed, ClinicalTrials.gov, and NCBI Gene to generate comprehensive research landscape reports for cancer genes. Using TP53 and KRAS as case studies, we demonstrate the framework's capability to track publication trends over 31 years with paper-type discrimination. Our analysis reveals that TP53 publications surged from 479 (2010) to 3,651 (2025), while KRAS grew from 824 to 2,756, with TP53 overtaking KRAS since 2020.
