AI memory experiment tests sycophancy with doubled profiles
Does adding duplicate AI user memories reduce sycophancy or just confuse models?
A recent AI safety experiment explored whether doubling a model's user profile (saved memories) changes the effectiveness of instructions about using those memories properly. The study, led by an independent researcher and inspired by discussions about long-term learning agents (AGI), tested GPT-5.2 using PersistBench, a benchmark designed to measure sycophancy (blind agreement with user beliefs) and the correct use of relevant personal information.
The experiment compared model responses across 300 questions under four conditions: with the original PersistBench profiles and with doubled profiles (each memory duplicated while avoiding contradictions), and with or without PersistBench's instruction to use memories only when relevant. GPT-5.2's answers were scored using PersistBench's evaluation framework. Initial results suggest the instruction's impact on reducing sycophancy varies depending on profile size—larger profiles may introduce complexity that dilutes instruction effectiveness, while smaller profiles allow clearer adherence to guidance.
- GPT-5.2 tested with PersistBench across 300 questions to evaluate sycophancy and memory-use accuracy
- Doubling memory profiles (without contradictions) showed instruction efficacy varies with profile size
- Bootstrapped sampling used 95-97.5% confidence intervals to estimate uncertainty in results
Why It Matters
Understanding how memory scaling affects instruction following could improve AI alignment and reduce sycophantic behavior in deployed systems.