Expectation Reasoning for Exploring Functional Forms of Dynamics

Network reconstruction becomes particularly challenging when both the interaction structure and the dynamics governing the observations are unknown. We investigate whether an LLM-driven scientific process can explore effective dynamical response laws that improve reconstruction under a fixed graph-reconstruction backend. A closer examination of the research process reveals two limitations of proxy-driven optimization. First, improving the fitting proxy does not necessarily improve downstream network recovery. Second, the proxy-optimized realization provides insufficient evidence to determine whether a proposed dynamical mechanism is actually realized, whether it has a meaningful effect, or whether it remains worth investigating. Thus, successful proxy optimization can support numerical realization without providing an adequate basis for evaluating the scientific status of the proposed mechanism. We introduce Expectation Reasoning (ER), which separates numerical realization, evidence formation, epistemic revision, and solution commitment within the scientific-search process. Across two truth-blind CausalDynamics comparison cohorts totaling 20 cases, the recorded ER trajectories preserve contradictory, partial, and unresolved evidence across research rounds and exhibit a clearer separation between epistemic revision and candidate commitment. Downstream effects remain criterion-dependent: ER reduces fixed-threshold structural error most clearly in the primary cohort, while AUROC and AUPRC do not improve and the resulting reconstructions become substantially sparser. Taken together, our findings support separating scientific evidence and revision from proxy-driven numerical realization, rather than treating optimization outcomes as sufficient scientific decisions.