Same-Checkpoint Intervention on Pre- and Post-Context States in Entity-Local Temporal Encoders

Relational structure can shape a temporal encoder during training or be mixed directly into its deployed representation. We introduce a same-checkpoint diagnostic that separates these roles by exposing entity-local pre-context $z^{\mathrm{pre}}$ and message-passed post-context $z^{\mathrm{post}}$ states from the same trained system, using a nonstationary equity panel as a stress test. Under a historical residual-correlation adjacency that ranks neighbors by $|\rho |$ and discards sign, switching retrieval from $z^{\mathrm{pre}}$ to $z^{\mathrm{post}}$ reduces rank correlation on 19/21 forward-state descriptors in the three-seed mean; 15 are negative in every seed and none is positive in every seed, with losses of 0.086–0.153 on eight highlighted descriptors. A sweep-selected, two-seed exploratory endpoint suggests that stronger graph-conditioned training can reallocate the unmixed state toward volatility/range geometry while degrading market-factor geometry, but the absence of a content-free shuffled-graph control and a third seed prevents attribution to relational edge content. The main result is therefore a controlled negative finding and a reusable diagnostic principle: training-time use of structure and inference-time relational propagation should be evaluated separately.