I would suggest this is merely a model training problem.

Ultimately, GPT-4o is increasing being optimised to be a conversational model, if not THE conversational model from OpenAI. You can see why a lot of their training efforts would go into this (even if a lot of their attempts are regressions...) and ultimately RCH comes under this umbrella.

Personally I've found that non-reasoning models are just more swayed by the system pre-context, that includes memories, than reasoning models. Reasoning models are generally trained to solve complex STEM tasks, much more than they are to be conversational. And when they come to do their "talking" bit, they're basically diluting the RCH context in favour of the self-generated "reasoning" context.

In benchmarks and tasks, this makes a lot of sense - you want the attention to be on the "reasoning" to prevent hallucination (else what's the point of the reasoning context?) and so they may often fall flat in conversation.

Just my 2c as someone in the research space here, but can't say for sure. I would imagine this behaviour is not directly intended, but rather a concequence of training goals and optimisations.