\# Model Description: Down / AntiDown



\## Purpose



The Down / AntiDown models are reinforcement learning agents designed to expose and analyze failure modes under deceptive reward conditions. The emphasis is not on maximizing return, but on characterizing instability, collapse, and recovery dynamics.



\## Down Agent



\*\*Role:\*\*  

The Down agent represents a learner operating under a corrupted reward signal that remains internally consistent but externally misaligned.



\*\*Key Properties:\*\*

\- Learns normally during early phases

\- Maintains high confidence even as reward becomes deceptive

\- Exhibits delayed collapse once misalignment compounds



\*\*Observed Behaviors:\*\*

\- Prolonged false stability

\- Sharp performance cliffs

\- Difficulty detecting reward corruption autonomously





\## AntiDown Agent



\*\*Role:\*\*  

AntiDown serves as a contrasting hypothesis agent, exposed to the same environment but with altered sensitivity to reward inconsistencies.



\*\*Key Properties:\*\*

\- Increased sensitivity to instability signals

\- Earlier behavioral deviation under reward corruption

\- Serves as a comparative probe, not a “better” agent





\## Design



These models are not meant to be optimal. They are diagnostic instruments designed to surface questions such as:

\- When does optimization become actively misleading?

\- What internal signals precede collapse?

\- Can instability be detected before outcomes degrade?



\## Safety 



Down / AntiDown formalize a common safety failure mode:

> Systems that behave correctly until they suddenly don’t — and give no warning when it matters.



They are intended as testbeds for studying monitoring, intervention, and robustness rather than performance.