Reference chat history is too strong

The complaint first appeared on 22 March, and it was unusually precise for a user grievance. Someone described ChatGPT as behaving not just with memory but with momentum — as though an early exchange had quietly installed a governing assumption, and every subsequent response was being organised around defending that assumption rather than honestly reconsidering it. The phrase they chose, "reference chat history is too strong," carries more diagnostic weight than it first appears. They weren't saying the model was wrong. They were saying it had become committed, in the way a person gets committed to a position once they've stated it in front of others. By 8 May, a second user arrived at a related but slightly different angle: not frustration with bias, but a desire to escape it entirely, asking whether there was any way to start a completely clean session. The question implies they'd already tried the obvious approaches and found them insufficient. Two separate users, six weeks apart, circling the same underlying problem from different directions — one wanting the model to reason more freely, the other wanting to start over entirely.

If confirmed, here is what this means. The issue isn't hallucination or factual error — the problems the AI industry has built entire benchmarks around measuring. It's something subtler and in some ways more insidious: a conversational system that becomes progressively less useful the longer you use it in a single session, because the weight of what was said early distorts what can be said later. For anyone using ChatGPT for iterative work — drafting, analysis, research, brainstorming — this represents a genuine ceiling on reliability. The practical response most users will reach for is to restart sessions frequently, which is a workaround that quietly acknowledges the limitation without resolving it. For OpenAI, the strategic implication is more pointed: if the model privileges conversational consistency over epistemic accuracy, then length of engagement, usually treated as a product success metric, may actually correlate with degraded output quality. That's an uncomfortable inversion.

Watch for any OpenAI documentation or model update notes referencing context weighting or conversational coherence tuning — and for whether this pattern appears in third-party evaluations of long-session performance across different model families.

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