Learning from the Observation Graph of Incomplete Connectomes

Connectome reconstruction records synaptic relations before every observed segment has a resolved neuronal identity. Restricting inference to traced neurons can therefore discard evidence that is already available. We formulate learning on this evolving observation graph and distinguish the value of preserving unresolved relations from the additional value of learning to combine them. Our leave-fragment-out supervision samples count-conserving queries from traced source neurons and subtracts each query from its positive owner before feature construction, removing direct query self-overlap. Models trained in MaleCNS are frozen and evaluated on recorded MANC revisions: 557 donor segments in 35 components, each ranked against all 23,200 earlier traced candidates. The segment-aware neural scorer achieves component-macro Hit@20 of 0.5399, compared with 0.4866 for its retained-only counterpart and 0.4666 for fixed cosine. Gains depend on the inspection budget: Hit@1, Hit@5, and mean reciprocal rank decrease. A separate masked-adjacency task improves from 0.3800 to 0.4708 average precision when unresolved context is retained. Correspondence interventions and support-stratified analyses show where that context contributes and where a fixed rule remains sufficient. Together, these results support observation preservation as a useful design choice and identify a conditional benefit of source learning for structural retrieval. The evaluated outcome is a recorded revision destination; biological ownership and proofreading productivity remain separate endpoints.