Team Kuntal

RegLens turns a noncoding variant into a cited, cell-type-specific mechanistic hypothesis — and tells you when it doesn't know. Annotation tools tell you where a variant is ("intergenic, unknown significance"); sequence models tell you that it has an effect. Neither tells you why. RegLens bridges them. A deterministic layer — a ChromBPNet chromatin model, JASPAR motif scanning with an empirical significance gate, ENCODE cCREs, GTEx, GWAS Catalog, Europe PMC — computes every number. A multi-agent layer (four specialists, an adversarial red-team, an adjudicator) reasons over them. The agents never invent a number, and a fabricated citation cannot pass the validation gate. It's exposed over MCP, so any Claude can call it. Then we tried to break it. On 33,359 MPRA variants with negatives matched within the same regulatory elements, it beats CADD — but only in its own cell type (0.716 vs 0.587). Swap the erythroid model for a hepatic one and the wins swap with it: PKLR collapses 0.805 → chance. Intervention, not correlation. Across 24 deliberations: zero confabulations — it refused even to name a transcription factor it plainly knew, when its tools didn't support it. Screened against 100 real GWAS variants, it returned zero claims. In a field full of confidently wrong agents, RegLens is validated to refuse.

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