Reconstructing participant-level data from published Kaplan-Meier curves supports meta-analysis and other secondary survival analyses when original trial records are unavailable. Researchers may use original PDF figures or screenshots, which can look alike but retain different information. We assessed how converting vector figures to images affects reconstruction by a language model that can inspect files and execute code. We generated 64 independent two-arm datasets across four scenarios combining 100 or 1,000 participants per arm with proportional or crossing hazards. Each figure was supplied as a native vector PDF and a matched 300-dpi PNG to Opus 5.5 through Claude Code under the same instructions. Outcomes compared reconstructed records with the original observed records: time-averaged absolute curve error through 24 months, error in the between-group restricted mean survival time difference, and error in the Cox log-hazard ratio. Native PDFs had lower curve error in all 64 completed pairs, including replacements for incomplete runs. Mean errors were 0.000029 and 0.1047 percentage points for PDF and PNG, respectively; the paired difference was -0.1047 (95% confidence interval, -0.1194 to -0.0909). PNG errors in the restricted mean survival time difference and log-hazard ratio were small: mean absolute errors were 0.0084 months and 0.0027. Lower resolution and compression increased curve errors; embedding unchanged pixels in a PDF did not restore vector accuracy. Some PNG runs exceeded the instructed revision limit, so results describe the implemented workflow rather than performance under a strictly enforced limit. Preserving vector information improved reconstruction fidelity in this workflow, but the observed differences do not establish clinically consequential changes in treatment estimates. Validation against reported survival summaries and event counts remains necessary. Generalization to published clinical figures and other model workflows requires further evaluation.