LLMs fine-tuned on time-sliced paper data generate proposals with up to 10.6% higher Future Alignment Score against actual later publications, with human experts and real implementations confirming gains.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Learning to Predict Future-Aligned Research Proposals with Language Models
LLMs fine-tuned on time-sliced paper data generate proposals with up to 10.6% higher Future Alignment Score against actual later publications, with human experts and real implementations confirming gains.