Matched Starts, Divergent Objects: How Human-AI Collaboration Forms What It Explains
3.40T1 sourcearXiv cs.HC
Source record
Published by arXiv cs.HC (T1 source). The original is at https://arxiv.org/abs/2609.04542.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryLongitudinal study of 843 turns in which the same expert researcher developed branch-isolated scholarly trajectories with different AI systems under matched starting conditions. Both trajectories independently shifted a continuity problem from recall toward usability but produced different research objects and endpoint manuscripts, with distinct analytic genealogies.
Why it mattersEmpirical evidence that matched starting conditions do not stabilize inquiry in human-AI collaboration; useful for researchers designing or interpreting AI-assisted scholarly workflows.
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