14/12/25
Reddit: Adult Flags
Start time: 07:12AM AEST
URL: https://www.reddit.com/r/ChatGPTPro/comments/1plun1h/comment/ntvfzu9/?context=3
Initially we were skeptical. OpenAI advised they would loosen adult guardrails last year in February. We do not care for guardrails but this has prompted us to be aware of new flags and impacts our metrics around corporate strategies.
Interaction Profiling
Start time: 08:29AM AEST
Adding to our behavioural fingerprinting theory, we are considering how language agnostic behavioural anchoring can utilise interaction geometry and statistical attractor states. If confidence patterns are high, medium and low. Are we able to utilise key maps to the geometry and add numerics to it to validate the pattern.
Identifying a user’s unique interaction profile can assist the AI with better ways of assisting and calibrating to a user. Humans do this often to help narrow down a person. For example; consider a person you know in your life and try to narrow them down to what makes them who they are. Start with primary definers
Visuals: What they look like
Race/Continent: Where they are from and if their visuals map to it. As a polyglot, I often get you look like you are from X instead of Y.
Mannerisms
and that in itself is a good example of humans. You may not follow this path and map them the way you see fit to narrow it down to that one person.
What we have discovered currently and still mapping against our security models and strategies:
Linguistic style (compression, cadence, register)
Interaction control (how the user steers, stops, corrects)
Cognitive preference signals (depth vs summary, ambiguity tolerance)
Boundary behaviour (response to refusal, friction, constraint)
Repair loops (how misalignment is handled)
Expectation management (what the user assumes a system should do)
Changes in 5.2, introduced firmer boundaries, less negotiation, clearer refusal surfaces and reduced over alignment. This in turn has an effect on:
Boundary reactions as first class signals
Frustration mapping is sharper
Repair vs escalation diverges cleanly
Expectation mismatch shows up immediately.
These are amplifications from 5.0 where signals sometimes blurred our testing.
5.2 further updates
Start time: 08:01PM AEST
Unplanned testing on audio indicated 5.2. Observed a reasoning surface degradation compared to text.
Weaker abstraction switching
Higher tendency to stay in “helpful conversational coach” despite user’s attempt to steer back to the correct frame.
Slower to arrest repetition once locked on the wrong response/logic branch.
System appears to optimise for flow and continuity rather than hard frame resets and causing it to remain in the incorrect abstraction layer and fall into a loop procedure.
We have no intention to test the guard rail agreement on voice.
