One Token Flips the Verdict: Hypergraph Propagation and State Readout Dissociate in Selective Belief Revision

Selective belief revision requires propagating evidence changes while preserving judgments whose justification remains unchanged. We show that evidence propagation and state readout dissociate in two 8B language models. We introduce Evidence–Intervention–Claim (EIC) to measure this separation by intervening on evidence and resolving each claim’s supporting and opposing coverage before and after the change. Updating intermediate hypergraph sources improves multihop revision over matched StaticSource training by 9.01 points on FLDx2-formal and 34.34 on EntailmentBank across three Qwen seeds. Yet pairing a graph-recoverable partial endpoint with itself reduces Qwen’s joint accuracy from 94.94% to 11.24%, despite unchanged endpoint evidence and a bitwise-identical graph residual. Changing only the preceding state token from partial to full raises second-state partial accuracy from 12.92% to 82.58%. Crossed interventions separate token, companion-evidence, and generated-history effects; a pre-specified prospective source-disjoint replication supports the same pattern. The dissociation also persists, though attenuated, when oracle graphs are replaced at inference by cross-fitted predicted graphs in an additional Qwen control. Training on the missing partial-preservation composition helps, but heavy augmentation causes severe state redistribution, whereas lower-dose augmentation improves preservation with substantially less collateral redistribution. Endpoint-factorized generation substantially restores partial preservation across all three Qwen seeds. Together, these results identify evidence propagation and state readout as separable computational problems and motivate isolating generation histories for endpoint-wise judgments that are semantically independent.