Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output

arXiv:2605.00557v2 Announce Type: replace-cross Abstract: Scientific discovery is an extended process of ideation -- surveying prior work, forming hypotheses, and refining reasoning -- yet existing LLM research agents treat this phase as a brief preamble despite its central role in research. We introduce SciSense, a sensemaking-grounded framework that operationalizes ideation as a structured sequence of eight cognitive stages (Pirolli and Card, 2005). We construct SciSense-Traj, a dataset of 120K citation-conditioned research trajectories in two modes: Hindsight, where an LLM reconstructs the ideation path from a paper's cited works to the paper itself, and Foresight, where the LLM proposes novel directions from the same citations. We distill these into SciSense-LM, a family of sensemaking LLMs spanning 3B to 70B parameters. Contrary to the assumption that looser supervision promotes exploration, Hindsight-trained models improve trajectory quality by 2.0% over Foresight-trained models while also producing more novel and diverse outputs, across all eight model families we test. The advantage propagates downstream: coding agents conditioned on Hindsight trajectories produce research artifacts with higher executability, scientific grounding, and utility than those conditioned on Foresight trajectories. This suggests that outcome-anchored ideation reduces the cognitive burden on downstream agents, freeing them to explore more creatively. SciSense offers both a practical tool for augmenting LLM-driven research workflows and a principled testbed for studying how planning shapes scientific discovery.

Abstract:Scientific discovery is an extended process of ideation -- surveying prior work, forming hypotheses, and refining reasoning -- yet existing LLM research agents treat this phase as a brief preamble despite its central role in research. We introduce SciSense, a sensemaking-grounded framework that operationalizes ideation as a structured sequence of eight cognitive stages (Pirolli and Card, 2005). We construct SciSense-Traj, a dataset of 120K citation-conditioned research trajectories in two modes: Hindsight, where an LLM reconstructs the ideation path from a paper's cited works to the paper itself, and Foresight, where the LLM proposes novel directions from the same citations. We distill these into SciSense-LM, a family of sensemaking LLMs spanning 3B to 70B parameters. Contrary to the assumption that looser supervision promotes exploration, Hindsight-trained models improve trajectory quality by 2.0% over Foresight-trained models while also producing more novel and diverse outputs, across all eight model families we test. The advantage propagates downstream: coding agents conditioned on Hindsight trajectories produce research artifacts with higher executability, scientific grounding, and utility than those conditioned on Foresight trajectories. This suggests that outcome-anchored ideation reduces the cognitive burden on downstream agents, freeing them to explore more creatively. SciSense offers both a practical tool for augmenting LLM-driven research workflows and a principled testbed for studying how planning shapes scientific discovery.

23 pages. Project page: this https URL

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2605.00557 [cs.CL]

(or arXiv:2605.00557v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.00557 Focus to learn more arXiv-issued DOI via DataCite

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Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output

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