topos.

A stability-certified protocol for deciding when latent structure in high-dimensional biological data is real enough to act on.

Why this exists

High-dimensional biological analysis has a well-known failure mode: embed, cluster, find something that looks structured, and interpret it. Nonlinear methods are very good at finding a structure, and offer no built-in signal for whether that structure would survive a different seed, preprocessing choice, or equally defensible embedding. topos treats “is this structure real” as a certification problem rather than a modeling choice.

Approach

An eight-stage protocol, each stage gating entry into the next: data landscape audit, structural plausibility, methodological gap analysis, nonlinear structure certification, robustness stress testing, cross-modal validation, representation-space agreement, and a final value audit issuing an explicit go/kill/hold decision. Matched-model comparison, density-aware validation, and mandatory confound auditing run throughout. Heavy compute — genome foundation model embeddings, long-context reasoning — is evidence-gated: conditional on a candidate structure surviving the earlier, cheaper stages first.

Key results

None of the three has reached later stages yet, and that's intentional rather than a gap — the protocol is designed to keep projects at the cheap, falsifiable, kill-or-continue stages until they've earned the right to more expensive ones. The generalizable result so far: the same eight-stage protocol applies unmodified to a polyploid crop genome, a diploid crop genome, and a transposable-element analysis in a third species, without needing to be reinvented per organism.

Status

Framework active and stable. Case studies are early-stage by design; results above describe protocol validation, not yet biological findings.