ThesisAgent: Revising Persistent Theses for Long-Horizon Decision Making

Long-horizon agents must revise decision-supporting assumptions while preserving unaffected beliefs. This requires controlling revision scope and separating belief changes from execution constraints. Behavioral responsiveness must also be distinguished from decision quality. We propose ThesisAgent, which maintains these assumptions as a persistent decision thesis governed by an explicit transition contract. To address Unscoped Thesis Revision, Evidence-Local Revision separates hypothesis selection from revision strength. Its numerical updater changes only a bounded subset and exactly preserves unselected confidences. To address Belief–Execution Entanglement, Decision–Execution Separation exposes desired actions before feasibility enforcement so that belief effects can be audited at fixed execution context. To address Sensitivity–Utility Ambiguity, Intervention-Based Utility Audit combines paired hypothesis interventions with oracle-referenced regret in the controlled setting to assess decision dependence and quality separately. ThesisWorld experiments show that local updates improve utility over matched dense updates and reveal a trade-off between rapid response and retained correction. In ALFWorld, persistent thesis state improves task success by 6.0 percentage points over a matched control using the same execution controller. In the 43-month return-based financial proxy, ThesisAgent exceeds the strongest evaluated baseline by 23.7 percentage points in cumulative net return. Our code is available at https://anonymous.4open.science/r/THESISAGENT-948D.