SpecForge: Agent-Oriented Code Documentation Optimization via Multi-Frontier Tree Search

As language models become autonomous software agents, documentation shifts from explanatory prose to a natural-language behavioral specification for code generation. We study agent-oriented documentation generation, where the objective is not readability but the correctness of code synthesized from documentation alone, turning the task into a black-box search over natural-language specifications evaluated only through the generated code and its execution behavior. Its defining difficulty is output coupling: program entities are behaviorally entangled, so a revision that sharpens the specification of one entity may simultaneously destabilize its dependents, inducing the familiar whack-a-mole failure mode in iterative refinement. We introduce SpecForge, a multi-frontier tree search solution that preserves complementary search frontiers through Pareto state management to avoid premature commitment and escape certain frontier-local optima, performs dependency-constrained bandit selection for callee-before-caller refinement, and uses diversified error-conditioned expansion to remain robust to noisy test feedback. On DevEval+, SpecForge attains the best performance across five backbone models, including a 94.9% solve rate with Claude-4.5-Sonnet and a 25.7% average improvement over the leading baseline. We further demonstrate that the resulting specifications improve downstream performance on both cross-language code translation and new-feature implementation.