Virtual Cell Benchmarks May Be Flawed
BoWang87 · x · 2026-07-08
A paper targeting ICML 2026 points out that standard perturbation benchmarks for virtual cell tasks might have severe issues.
The authors found that simple mean predictors outperform many models on these benchmarks. They argue the issue isn't poor model performance, but rather the evaluation method treating numerous irrelevant genes equally to a few signal genes, leading to distorted scores.
More from Research
- Meta FAIR's Self-Direction turns agent failures into reusable LoRA skills, +13.8pp on Terminal-Bench — karen_ullrich · 2026-10-27
- DONIC hits 86.79% on bimanual dexterous tracking, crushing prior SOTA's 9.43% — liuziwei7 · 2026-10-27
- Why don't more LLMs ship 10M-token context? A researcher asks about attention tradeoffs — agarwl_ · 2026-10-27
- PSNR below 15 dB in papers likely means the metric was misused, researcher warns — kwangmoo_yi · 2026-10-27
- Ortet's first health eval: 34 open-weight models, 50 tasks, generic rankings fall short — thegautamkamath · 2026-10-27
- Scaling-fit discrepancy: output-head FLOPs are ~65% of compute at the smallest rung, ~3% at 535B — dlwh · 2026-10-27