This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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Note: English is not my first language. I used AI to help with wording, but the observations and structure are mine. I might be wrong, and I’m here to get corrected. I’m not an AI researcher. I’m a heavy end-user who ran several long, continuous ChatGPT sessions (multi-hour, high-context). In one session, I noticed a gradual change in behavior that didn’t feel like simple random variation. I’m not claiming malice, intent, or anything AGI-like. I’m asking whether this resembles any known failure modes in long-horizon interaction (RLHF/policy layers/context management). What I observed (high level) Across the session, the model increasingly showed: self-contradiction drift in framing/persona loss or re-interpretation of previously established context “policy-like” responses triggering in places they hadn’t earlier mismatch between earlier commitments and later refusals/reframings The key point: it felt directional, not random. It wasn’t just forgetting; it often re-described earlier context in a way that looked more aligned with safety/policy heuristics as the session continued. Why I’m posting The shift didn’t happen all at once. It looked like a gradual gradient: the longer the session went, the more outputs were pulled toward a safer, more hedged equilibrium, even when earlier parts had a different stance. I don’t know what to call this. Possibilities I can imagine: a known RLHF artifact context-window / summarization / memory management effects a policy layer re-weighting effect under long sessions something else (or just my misinterpretation) What I’m sharing I’m sharing an index + analysis notes: session logs (raw) timestamps / state transition notes a “coordinates” summary of where shifts occur a minimal reproduction narrative Repository: edd-one-out-sion/llm-context-drift-logs: High-density human–LLM interaction logs documenting context drift, safety and alignment failure modes. This is not meant as a proof. It’s a request for interpretation: Does this pattern resemble anything already described in alignment / RLHF / safety-layer research? If yes, what is it called? If no, where is my framing misleading? I’m posting here because this kind of effect may be hard to notice in short benchmarks, but may show up in long, real-world interactions.
Note: English is not my first language. I used AI to help with wording, but the observations and structure are mine. I might be wrong, and I’m here to get corrected.
I’m not an AI researcher. I’m a heavy end-user who ran several long, continuous ChatGPT sessions (multi-hour, high-context). In one session, I noticed a gradual change in behavior that didn’t feel like simple random variation.
I’m not claiming malice, intent, or anything AGI-like.
I’m asking whether this resembles any known failure modes in long-horizon interaction (RLHF/policy layers/context management).
What I observed (high level)
Across the session, the model increasingly showed:
self-contradiction
drift in framing/persona
loss or re-interpretation of previously established context
“policy-like” responses triggering in places they hadn’t earlier
mismatch between earlier commitments and later refusals/reframings
The key point: it felt directional, not random.
It wasn’t just forgetting; it often re-described earlier context in a way that looked more aligned with safety/policy heuristics as the session continued.
Why I’m posting
The shift didn’t happen all at once. It looked like a gradual gradient:
the longer the session went, the more outputs were pulled toward a safer, more hedged equilibrium, even when earlier parts had a different stance.
I don’t know what to call this. Possibilities I can imagine:
a known RLHF artifact
context-window / summarization / memory management effects
a policy layer re-weighting effect under long sessions
something else (or just my misinterpretation)
What I’m sharing
I’m sharing an index + analysis notes:
session logs (raw)
timestamps / state transition notes
a “coordinates” summary of where shifts occur
a minimal reproduction narrative
Repository: edd-one-out-sion/llm-context-drift-logs: High-density human–LLM interaction logs documenting context drift, safety and alignment failure modes.
This is not meant as a proof. It’s a request for interpretation:
Does this pattern resemble anything already described in alignment / RLHF / safety-layer research?
If yes, what is it called? If no, where is my framing misleading?
I’m posting here because this kind of effect may be hard to notice in short benchmarks, but may show up in long, real-world interactions.