Reconstruction: A Blind Benchmark for Recovering Research Ideas from Pre-Publication Bibliographies
Can a language model recover the true research idea of a published paper when given only that paper's pre-publication bibliography? We introduce Reconstruction, a blind idea-recovery benchmark that withholds the seed paper and all contemporaneous or future literature, and asks models to propose hypotheses that an independent large language model judge matches against the held-out ground-truth idea. A strict anti-leakage protocol-temporal citation cutoff, anonymous reference IDs, and frozen per-paper bibliographies, which prevents prompt-time leakage of the seed idea. Across six scientific domains and 643 evaluated papers, seven frontier models achieve only modest Match rates (approx. 3-15...
| Authors | Shaolong Chen, Yanlin Fei, Nazhou Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 August 2026 |
| Last updated at source | 24 August 2026 |
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