Learning to Predict Future-Aligned Research Proposals with Language Models
The paper reframes research proposal generation as a time-sliced forecasting problem. Models are trained to generate proposals that align semantically with future scientific papers using a metric called the Future Alignment Score (FAS). This training, supported by structured supervision from historical data, improves proposal quality and suggests models capture meaningful research trajectories.