Learning to Predict Future-Aligned Research Proposals with Language Models
Large language models (LLMs) are increasingly used to assist ideation in research, but evaluating the quality of LLM-generated research proposals remains difficult: novelty and soundness are hard to measure automatically, and large-scale human evaluation is costly. We propose a verifiable alternative by reframing proposal generation as a time-sliced scientific forecasting problem. Given a research question and inspiring papers available before a cutoff time, the model generates a structured proposal and is evaluated by whether it anticipates research directions that appear in papers published after the time. We operationalize this objective with the Future Alignment Score (FAS), computed ...
| Authors | Heng Wang, Pengcheng Jiang, Jiashuo Sun et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 March 2026 |
| Last updated at source | 28 September 2026 |
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