Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output

arXiv cs.AI / 5/4/2026

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Key Points

The paper argues that ideation in scientific discovery should be treated as a structured process rather than a brief prelude, and introduces SCISENSE to operationalize sensemaking as eight cognitive stages.
It presents SCISENSE-Traj, a 100K-scale dataset of citation-conditioned research “trajectories” generated in two modes: Target (reconstructing paths to known papers) and Infer (proposing novel directions from the same citations).
The authors distill these ideas into SCISENSE-LM, a set of sensemaking LLMs (3B–70B parameters), finding that Target-trained models outperform Infer-trained ones in trajectory quality while also yielding more novel and diverse outputs.
Downstream evaluation shows that coding agents conditioned on Target trajectories produce research artifacts with higher executability and quality than those conditioned on Infer trajectories.
Overall, the work suggests that targeted ideation can reduce cognitive burden on downstream systems, enabling more creative exploration, and provides both a tool and a testbed for studying how planning affects discovery.

Abstract

Scientific discovery is an extended process of ideation--surveying prior work, forming hypotheses, and refining reasoning--yet existing approaches 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 \& Card, 2005). We construct SCISENSE-Traj, a 100K-scale dataset of citation-conditioned research trajectories in two modes: Target, where an LLM reconstructs the ideation path leading to a known paper from its cited works, and Infer, 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 greater exploration, Target-trained models achieve a 2.0\% improvement in trajectory quality over Infer-trained models while also producing more novel and diverse outputs. This advantage propagates downstream: coding agents conditioned on Target trajectories produce research artifacts with higher executability and quality than those conditioned on Infer trajectories. This suggests that targeted ideation reduces 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.