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arXiv AI By James Mooney, Zae Myung Kim, Young-Jun Lee, Dongyeop Kang

Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output

Read the original on arXiv AI →

The paper introduces SciSense, a framework that structures scientific ideation into eight cognitive stages and a dataset, SciSense-Traj, of 120K citation-conditioned research trajectories in both hindsight and foresight modes. It presents SciSense-LM, a family of LLMs trained on these trajectories, showing that hindsight-trained models yield higher-quality, more novel outputs and improve downstream coding agents’ research artifacts. The study demonstrates that outcome-anchored ideation reduces cognitive load for downstream agents, enabling more creative exploration.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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