EpiForge: Risk-Averse Verifier-Guided Editing for Epitope-Conditioned Antibody Design

Epitope-conditioned antibody design is dominated by open-loop pipelines that generate candidates, score them once with a structure predictor, and discard whatever fails, so a rejected candidate never informs the next proposal, developability is inspected only after the fact, and a design that pleases one folding oracle need not survive another. We present **EpiForge**, a closed-loop editing agent that reframes the task as verified repair of an initialized variable region. An epitope-conditioned initializer writes CDR sequences onto a fixed framework by masked discrete diffusion over a chemistry-aware coarse-grained graph. A reasoning policy then proposes edits as a short integer genotype; a parameter-free mapper projects that genotype onto a legal mutation set by sequential modular arithmetic, so no proposal can be malformed, out of bounds, duplicated or vacuous; an uncertainty-aware world model predicts the resulting interface metrics with its own variance; and a confidence-bounded selector spends the scarce verification budget where it is most informative. Acceptance is not a learned gate: every retained edit is confirmed by a real structure prediction and the metrics computed from it, and anything that does not improve the verified state is rolled back. Selection is optimistic and credit assignment conservative, since the selector adds the surrogate's predicted standard deviation while the group-relative objective subtracts it. On a SAbDab split that removes redundancy on the antibody, antigen-sequence and antigen-structure sides at once, EpiForge reaches 45.3% CDR-H3 recovery and 55.1% all-CDR recovery, improves CDR-H3 backbone accuracy under reporting predictors never used inside the loop, and attains the best humanness of every method compared without optimizing for it. A held-out case study recovers pose, loop conformation and sequence together at DockQ 0.649 against 0.387 for the strongest baseline, while our ablation and two further cases show these three quantities coming apart; we report where the loop trades sequence recovery for interface quality, and where it does not lead. Our code is at [https://anonymous.4open.science/r/EpiForge-82C6/](https://anonymous.4open.science/r/EpiForge-82C6/).