Sangho Suh, Joseph Chee Chang, Pao Siangliulue, Amy Xian Zhang, Matt Latzke, Jonathan Bragg, Doug Downey, Daniel S. Weld · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.30548 · cited by 0 works (OpenAlex) · open-access PDF
AI agents can now search literature, generate hypotheses, analyze datasets, and run experiments at a scale difficult for researchers to match. Yet scaling scientific execution does not automatically scale researchers' ability to make sense of what agents produce. When researchers return to a long-horizon run, they must evaluate work they did not monitor: what the agent produced, which decisions shaped those outputs, and whether those decisions served the research goal. We frame this post-run sensemaking as a representation problem across three linked levels: goal, decision, and artifact. From this framework we developed Atlas, an agentic workspace with a trajectory tree, which renders the run as a branching structure showing what the agent chose and set aside, and artifact projections over six representations. In a within-subjects study (N=14), participants reported higher decision transparency, artifact sensemaking, and goal connection with Atlas than with a standard baseline, together with substantially lower subjective workload.
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Topics: Computer science, Artifact (error), Set (abstract data type), Sensemaking, Scale (ratio), Representation (politics)
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