Improving LLM-driven program evolution with dynamic scope

LLM-driven program evolution typically searches over candidate programs while treating the extent that each mutation may change as a fixed generation interface. This creates a structural tension: narrow editing boundaries preserve locality and attribution but can make dependency-spanning improvements unreachable, whereas broad boundaries admit coordinated changes at the cost of a larger proposal space and weaker attribution. Because the appropriate side of this trade-off can change over a lineage, a fixed boundary can become either a reachability ceiling or an unnecessarily broad search interface. We introduce DyScope, which turns the permitted editing boundary into explicit, executable, and verifiable search state. A structural scope graph defines available regions, and each boundary action jointly specifies a target, dependency width, mutation regime, and transition guard. Trajectory evidence can retain productive local scope, widen to dependencies, redirect the target, contract after a broad transition, or invoke recovery. Before generation, the selected boundary changes the context exposed to the proposer and the set of reachable transformations. After generation, an executable transition contract compares the requested boundary with the realized parent–child diff, protects invariant regions, and records whether the transition is admissible. DyScope therefore adapts and verifies program transitions rather than only proposing programs. Feedback supplies evidence for updating boundary state; specific thresholds and rankings instantiate the selection policy rather than define the abstraction. DyScope attains 96.67%, 99.39%, and 98.83% on AIME 2026, HumanEval, and MBPP, respectively. In the matched boundary-policy comparison, dynamic scope reaches 90.18% versus 82.15% for fixed scope. These results identify the editing boundary as an adaptive dimension of program search rather than a static implementation choice. By making changes to an evolving system selectable, enforceable, and traceable, DyScope provides a concrete control interface toward auditable recursive self-improvement.