CEM-Agent: Coupling Tool Scheduling and Compositional Evidence for Navigation

Compositional navigation requires binding observations across objects, encounters, and time before committing progress. We introduce CEM-Agent, which couples a frozen vision–language planner with Compositional Evidence Memory (CEM). CEM constructs source-linked witnesses for bounded task claims and exposes unresolved roles and relations to guide subsequent tool use. A shared gate checks semantic evidence and execution facts before accepting progress. We test whether evidence composition benefits more from autonomous than fixed tool scheduling through matched 2 × 2 comparisons on 100 compositional navigation episodes. With oracle pose, CEM raises strict episode success from 43% to 51% under autonomous scheduling, versus 34% to 40% under fixed scheduling. The resulting interaction is +2 percentage points; the estimated-pose comparison yields the same point estimate. On fixed histories, witness recovery improves from 69% to 79%. Exposing gap feedback raises autonomous CEM success from 47% to 51% and improves short-horizon gap resolution. These results support the usefulness of evidence diagnosis and its feedback to planning, while the small positive interaction remains descriptive because paired uncertainty is not established.