Where Research Ideation Begins: Forward-Citation Supervision for Actionable Limitation Generation
Automated research ideation has advanced rapidly, with multi-agent workflows that generate whole research ideas end to end. These frameworks leave largely unexamined the step that seeds a good idea: identifying an actionable limitation from literature. In this paper, we focus on this first step in the research ideation pipeline. Starting from a pilot study, we find that as large language models (LLMs) scale from 0.6B to 32B parameters, they become far better at recognizing which limitation later work addressed, but no better at generating one, suggesting that the bottleneck lies in the supervision signal rather than model capacity. We therefore construct ActLim-Bench from forward citation...
| Authors | Liyang Zhao, Yong Cao, Haibin Ling |
|---|---|
| Primary category | cs.AI |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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