# Sab Moh Maya Hai

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Yash Raj](https://cerebralvalley.ai/u/yraj)
- **GitHub:** https://github.com/yashraj59/RefineRx/, https://huggingface.co/yraj/RefineRx,  https://github.com/yashraj59/RefineRx/blob/main/RefineRx_research_report.md, https://github.com/yashraj59/RefineRx/blob/main/RefineRx_hackathon_presentation.html , https://github.com/yashraj59/RefineRx/blob/main/09_paper/paper/paper_draft.pdf
- **Demo video:** https://www.youtube.com/watch?v=x88We-QB9yE
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/248

Adaptive-computation halting was built to save compute, easy inputs exit early and hard ones run longer. I used it the other way, as a measurement. My backbone is ARC Institute's STATE model, frozen, and all eight layers always run, so halting saves no compute. I read the layer where the model's prediction settles as a per-perturbation number, E of N, a proxy for how much work a perturbation's response takes. Then I asked three things. Is it reproducible, is it independent of effect size, and does it tell you anything you cannot already get from response magnitude. I tested it across four Replogle cell lines, K562, HepG2, Jurkat, and RPE1, and on a single-cell CD4 T-cell CRISPRi screen.
The signal reproduced across all four cell lines. In every one of the four lines, split-half reproducibility is 0.76 to 0.85, mean 0.80, and it is effect-independent in all four, so this is not a one-line result, it holds across four independent cell lines. On K562 as the worked example, reproducibility is 0.76, the partial correlation with effect size is minus 0.04 so it is not a proxy, and it recovers its oracle target at 0.61, while a naive gate just collapses to a constant. What does not carry across is the ranking itself, cross-line agreement is only 0.14, so the signal is real and reproducible in each line but cell-type-specific, not a shared invariant. In CD4 T cells it ports across donors only at the 48 hour endpoint, where the biology converges, and there it separates suppressors that spare resting cells from damaging perturbations.
When I cluster on the depth signature, approved drug targets pile into one translation and ribosome cluster in every line, with odds ratios from 2.8 to 9.8, and no network statistic reproduces the ordering, best is 0.23, so it is a new descriptor. The one honest limit is that it adds no extra lift over a STRING prior for one ranking task. The whole project is falsification-first, and the negatives are as much the result as the positives.
Most Perturb-seq gives one endpoint, control versus perturbed, and effect size cannot tell apart two perturbations with very different mechanisms that land at the same response size. This work recovers a stable, effect-independent signal for response complexity from the single endpoint we already collect, and it reproduced across four cell lines. It also turns an efficiency mechanism into a biological readout, which as far as I know is the first time halting is used this way on a perturbation model, and it gives a new descriptor that organizes druggability and toxicity while staying clear about where it holds and where it breaks.

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