TraceSeek: Scale-Aware Evidence Seeking for Progression Learning from Single-Cell Population Snapshots

Disease processes are often observed through ordered population snapshots, where multiple progression explanations can remain compatible with the same data. We introduce TraceSeek, a framework for progression learning that, under a fixed Scientific Query Program, selects which atlas view to inspect and at which inferential unit a claim may be supported. TraceSeek couples a progression-claim layer with a typed analytical topology, a query-conditioned budgeted controller, and surrogate Marginal Utility Distillation (MUD), enabling evidence refinement, comparison, aggregation back to valid units, and abstention when evidence remains insufficient. In controlled evaluations across eleven disease atlases with a shared typed tool interface and a deterministic query compiler, TraceSeek improves scale-selection diagnostics, reduces invalid-unit and false-support proxy rates, and achieves higher mean family-specific Task Score than ReAct and other tool-using agents. On a 54-episode multi-atlas progression-chain suite, TraceSeek improves Task Score from 0.606 to 0.840 and path completeness from 0.500 to 0.917 relative to ReAct. Ablations show that topology constraints, active scale selection, and MUD contribute materially to these gains. An Alzheimer's disease case study further shows stable microglial early-response localization under leave-one-donor-out analysis and stage-associated DAM structure.