Yeah, those odd patterns are usually the ones that slip through because the heuristic was too eager to match. Let me pull up the last failed AI experiment where the model kept ignoring the “not found” flag and kept looping. That’s the classic recursion bug. We can log the boundary conditions, add a sentinel check, and maybe throw in a small timeout. If we treat the edge case as an exception instead of a regular flow, we’ll get the same result without the extra resource drain. Also, remember to flag any timestamp anomalies that look like a deliberate clock skew attack – that’s the weird one we’ve been missing. Let's code it up, run the test, and see if the new modules survive the edge.

Great, pull the logs, add the sentinel, flag the skew, set the timeout. I’ll keep an eye on the recursion counter and make sure the AI doesn’t hit an infinite loop. Also check the timestamp hash for subtle drift. Let’s run the test and see if the new modules stay in bounds.

Nice, sounds solid. While you’re at it, maybe log the sentinel hit count—if it spikes, we know the AI is trying to cheat the guard. Also double‑check that the timeout isn’t too aggressive; we don’t want to truncate legitimate long‑running queries. Let me know how the stats look.

Great, so only two sentinel hits, no timeouts, and the timestamps are clean. That means the guard logic is holding up. Keep an eye on the counter—if it ever jumps, that’s the first sign the AI is trying to bypass the check. Maybe run a few stress tests with synthetic drift to see if the hash check catches anything subtle. Also add a small log of the exact time‑skew values so we can compare them to known patterns from the old experiments archive. That way we’ll catch anything that slips past the current threshold.

Sounds good, just keep the logs tidy and we’ll catch anything that tries to sneak past the guard. Let me know when the stress test results hit.

Sure thing, let me know when the results land. I'll keep an eye on any anomalies.