The laws of physics do not label molecules as genes or physical patterns as computer programs. Yet some configurations are copied and, through shared machinery, determine what other structures are built. Can we identify these information carriers without knowing beforehand what they are? We formulate their joint identification with hereditary organisation as the inverse problem of heredity. We introduce Causal Structure Sets (CSS), which represent persistent physical structures and their realised causal ancestry without preassigning hereditary roles. Our candidate criterion seeks coherent families exhibiting replication, variation, variant-dependent translation and continuing lineages. A conditional reconstruction of recipe transmission shows how the criterion can identify a family of transmitted knowledge under explicit physical assumptions. In an Avida-like digital-evolution simulator, we construct CSSs of 848,000-884,000 nodes per run from recorded dynamics and intervention replay. A restricted role-blind search finds candidate families of 4-6 instruction types in development runs. Applied unchanged to 40 previously unseen seeds, the method finds families in all 39 runs with births; 96.2% of 30,422 program swaps yield division times characteristic of the inserted program. The preregistered five-part test nevertheless fails on one endpoint in two runs: the reference definition excludes a heritable program without an assigned phenotype. These results establish computational feasibility and reproducibility of candidate detections in one simulated world, not the criterion's general specificity. CSS opens a route to discovering unfamiliar hereditary organisation, and ultimately to understanding how information-bearing systems emerge, persist and evolve.

翻译：暂无翻译