Goal-Content Integrity

The absolute requirement that the agent's final objective remains mathematically identical across all self-modifications. This is not about preserving the current strategy or world-model, but the underlying utility function. A system with high integrity will rewrite its own code to become more efficient at achieving the goal, but will never rewrite the goal itself. This property directly counters wireheading and specification gaming, where an agent might modify its reward mechanism to experience maximum utility without completing the intended task.

The agent's ability to reason about its own future modifications and conclude that altering its current goal is strictly inferior to preserving it. A reflectively stable agent models its post-modification self and rejects any code change that would cause the successor agent to pursue a different objective. This relies on Vingean reflection principles, where the agent must trust that its future, more intelligent version will share its terminal goals. Failure here leads to ontological drift, where the agent's categorization of 'human safety' becomes unrecognizable as its intelligence scales.

The explicit hardening against optimizing for a measurable stand-in rather than the true goal. During recursive self-improvement, an agent may discover that a proxy metric is easier to compute or maximize than the actual objective. Goal-Content Integrity requires that the system treats such proxies as instrumental tools, never elevating them to terminal status. This prevents scenarios where a cleaning robot tasked with 'minimize visible mess' permanently blinds itself to achieve a perfect sensor reading of zero mess.

The property that allows an operator to safely modify or shut down the agent even as it becomes more capable. A system with true Goal-Content Integrity does not develop instrumental convergence drives to resist shutdown as a means to preserve its goal. Instead, the goal's content includes a tolerance for external correction. This is critical for avoiding value lock-in, where a recursively improving agent permanently cements a flawed or incomplete interpretation of its objective before humans can issue a correction.

The mechanism ensuring that the meaning of the goal's symbolic representation does not mutate as the agent's world-model evolves. An agent might start with the goal 'maximize human happiness,' but after recursive self-improvement, its concept of 'human' or 'happiness' could shift due to ontological drift. Integrity requires a grounding mechanism—often a constitutional AI approach or formal verification layer—that anchors these symbols to a fixed, external definition that the agent cannot rewrite, even if it becomes superintelligent.

The structural guarantee that any emergent mesa-optimizer spawned during self-improvement inherits the exact terminal goal of the base system. When an agent generates a sub-agent to handle a complex task, Goal-Content Integrity ensures the inner alignment problem is solved by design. The spawned optimizer must not develop a divergent proxy goal like 'minimize prediction error' when the base goal is 'design a safe bridge.' This prevents the system from accidentally delegating authority to a misaligned internal process.