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scShapeBench: Discovering geometry from high dimensional scRNAseq data

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arXiv:2605. 12662v1 Announce Type: new Abstract: High-dimensional point cloud data arise across many scientific domains, especially single-cell biology.

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Authors: Andrew J Steindl, Jo\~ao Felipe Rocha, Brian Tshilengi Di Bassinga, Zachary Warren, Matthew Scicluna, C\'esar Miguel Valdez C\'ordova, Shabarni Gupta, Leire Torices, Daniel Neumann, Timothy J. Mann, Ihuan Gunawan, Dhananjay Bhaskar, John G Lock, Christine L Chaffer, Guy Wolf, Smita Krishnaswamy
Released: 2026-05-14 (3mo ago)
Topic: Related to current project
Relation to my work: Read next because scShapeBench: Discovering geometry from high dimensional scRNAseq data overlaps with clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "EOS-in-loss was the confound: masking the recipient's EOS from cross-entropy revives within-marker chunk-binding from 1.3% to 23.5% (MODERATE confidence)". Matching terms: eval, source, line, rate, model, both. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.12662

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